Airborne LiDAR bathymetry offers low cost and high mobility, making it an ideal option for shallow-water measurements. However, due to differences in the measurement environment and the laser emission channel, the received waveform is difficult to extract using a single algorithm. The choice of a suitable waveform processing method is thus of extreme importance to guarantee the accuracy of the bathymetric retrieval. In this study, we use a wavelet-denoising method to denoise the received waveform and subsequently test four algorithms for denoised-waveform processing, namely, the Richardson–Lucy deconvolution (RLD), blind deconvolution (BD), Wiener filter deconvolution (WFD), and constrained least-squares filter deconvolution (RFD). The simulation and measured multichannel databases are used to evaluate the algorithms, with focus on improving their performance after data-denoising and their capability of extracting water depth. Results show that applying wavelet denoising before deconvolution improves the extraction accuracy. The four algorithms perform better for the shallow-water orthogonal polarization channel (PMT2) than for the shallow horizontal row polarization channel (PMT1). Of the four algorithms, RLD provides the best signal-detection rate, and RFD is the most robust; BD has low computational efficiency, and WFD performs poorly in deep water (< 25 m). ALB uses a strong penetrating green laser (532 nm) to scan waters less than 50 m deep. ALB emits blue and green laser beams and receives signals reflected off various targets. The characteristic information is extracted from the difference in signal strength, and the technique is applied to measuring water depth and underwater topography, as well as providing low-quality classifications of the seabed6. A laser beam transmitted over a few hundred meters passes through the air and water to the seabed and is reflected back to the receiver. Along this path, a variety of noises perturb the signal, including noise internal to the receiver, atmospheric refraction, backscattering from water bodies, and diffuse reflection from the seabed. ALB is more complicated than terrestrial LiDAR measurements. Therefore, to process full-waveform ALB data, a crucial step is to improve the signal-to-noise ratio (SNR) of the data and accurately extract the echo signal7.Airborne LiDAR bathymetry (ALB) is an active remote-sensing technology that plays an important role in shallow-water topographic surveys and measurements. It boasts advantages of low operating cost, strong maneuverability, and high measurement accuracy, and is thus widely applied to update coastal topographic maps, for coastal construction, to monitor shallow waters, and in other fields8. Wong proposed using low-pass filters to remove noise from the signal and thereby extract the signal reflected from the seabed10. However, retaining only the transmitted and echo position signals may reduce target resolution, ranging accuracy, etc. Simultaneously, copious information about the water body (such as the turbidity) cannot be extracted from this measurement. The study of Mallet shows the importance of using full-wave laser data to measure seafloor topography. He proposes that full-wave laser data are more conducive to extracting the target position and for research on backscattering from water bodies11. Characterization of full-wave laser data is thus important to increase our understanding of the laser beam propagation. At present, processing algorithms for full-wave green laser data can be sorted into the following three categories:14. The algorithm is fast; however, it is susceptible to gross errors and noise. It is generally used to extract the initial value and extract the reflection time point after data preprocessing.Target-detection method: Depending on the original waveform, the target-detection method extracts the reflected laser energy time point by identifying the mutation point of the continuous signal reflection energy. Common target-detection methods include peak detection (PD), first-order derivative (FD), and the average square function (ASDF)17. Gaussian functions are widely used for fitting; for example, Gaussian fitting based on the nonlinear least-squares method18, Kai Guo based on Gaussian fitting19, and Gaussian half-wave width to decompose the waveform and extract the effective part of the data to suppress noise, which somewhat improves the problem of Gaussian overfitting. Wang14 compared quadrilateral fitting with mean variance fitting; however, the waveform fitting algorithm has difficulty in solving the problem of signal overlap, such as the overlap of surface and bottom echoes. The backscattering of the water body is an asymmetric waveform, making it difficult to fit the Gaussian function.Waveform decomposition method: The waveform decomposition method treats the laser waveform as a superposition of multiple mathematical functions and uses a combination of multiple mathematical functions to fit the waveform, thereby restoring the true original signal. Commonly used fitting functions include Gaussians, logarithmic normal distribution functions, and quadrilateral functions20, Richardson–Lucy deconvolution (RLD)21, blind deconvolution (BD)14, and B-spline deconvolution22. However, the deconvolution algorithm is more affected by noise, which leads to misjudgment of reflected echoes, and the data processing results are easily affected by ringing.Deconvolution algorithm: The deconvolution algorithm regards the received waveform as the convolution of the laser emission pulse and the target cross-section. Commonly used deconvolution algorithms include the Wiener filter deconvolutionIn early airborne laser scanning (ALS) systems, only the transmitted signal and echo position signal were accepted; the complete echo signal was not retained to avoid storing redundant data. Pe’Eri proposed the use of two waveforms for measurements in shallow waters. Near-infrared laser pulses are strongly reflected at the water surface, whereas a green laser beam penetrates the water more easily, after which it is reflected from the seabed23.The three types of data processing methods for green lasers show that target detection is relatively simple and does not depend on the laser propagation process; however, their accuracy is poor. The waveform decomposition method provides an approximate fit to the full-waveform data, and most waveform fitting functions are symmetrical functions. The waveform decomposition method does not consider backscattering of the water body, which causes unsatisfactory fitting. In contrast, the deconvolution algorithm starts from the laser propagation and waveform formation process and uses the inverse transformation of the waveform formation to restore a more realistic target cross-section. If noise can be removed, the deconvolution algorithm is an ideal method for laser echo processing. At present, the wavelet theory is widely employed in digital signal processingThis study first establishes a water-depth radar model (Wa-LiD) that is close/to reality according to the airborne laser propagation and formation process and then applies wavelet denoising to the simulation results to find the best denoising parameters. Subsequently, the denoised data are deconvoluted, the advantages and disadvantages of various algorithms are compared, and the influence of the parameters on the echo received by the laser is analyzed. Finally, the results of applying the algorithm to the simulation and measured data are comprehensively evaluated to determine the optimal deconvolution method. This lays the foundation for extracting water depth by airborne laser.24 is 300–1500 nm. The laser used by the ALB system produces long-wave 1064 nm infrared light and 532 nm blue-green light. Infrared light cannot penetrate the water body and is reflected from its surface. Blue-green light penetrates the water body and reflects from the bottom. Therefore, the model uses 532 nm green light, which has strong penetrability. The echo waveform of the ALB system can be expressed as the superposition of multiple echoes:20:The water-surface reflection modelAfter the laser pulse is emitted from the transmitter, it passes through the air and reflects from the water surface. The reflected energy received by the ALB detector can be expressed as the convolution of the transmitted pulse and the water surface echo energy Water reflection modelD received by the ALB system receiver can be expressed as the convolution of the transmitted pulse and the instantaneous echo energy at D, which is expressed asThe echo reflection signal from depth H is the sensor altitude,The instantaneous echo energy at Underwater reflection modelZ received by the ALB system receiver can be expressed as the convolution of the echo at Z and the transmitted pulse, with the echo energy at Z expressed asThe echo energy at the bottom ALB system noise modelThe Wa-LiD model is simulated by applying the reflection and refraction of different wavelengths in a complex water environment to form a reflected wave. The result of the simulation is close to the actual ALB waveform and can amplify the influence of various factors on the waveform, which facilitates the comparison of various parameters in waveform denoising and deconvolution. During the actual flight measurement, as noise-free waveform data is impossible to obtain, the accuracy of the algorithm is often evaluated using statistically relative truth values, and the simulation data can generate completely-noise-free simulation truth values, which promotes the accuracy of the algorithm. The evaluation, therefore, is particularly important to obtain laser simulation results that are closer to reality. The wavelength range of the Wa-LiD modelThe ALB system noise mainly includes the signal level of solar radiation and the internal noise of the instrument. The signal level due to solar radiation is defined as Gaussian white noise with a mean of zero and a standard deviation of one convolved by an instant echo; The detector internal noise is defined as a normal distribution with a zero mean and a standard deviation We use the above Wa-LiD model to simulate two types of echo waveform data with noise Fig. b and wit2, is a typical coral islet located in the Xisha Archipelago. The Mapper5000 system uses a four-channel high-speed waveform acquisition card for wavelengths of 1550, 1064, and 532 nm for echo collection. The sampling rate of each channel reaches 1 GSa/s, and the digital resolution is 10 bits. It includes three green channels  and one near-infrared channel . The three green channels are the shallow horizontal row polarization channel (PMT1), the shallow-water orthogonal polarization channel (PMT2), and the deep-water channel (PMT3). We conducted a comparative analysis of the measured data from PMT1 and PMT2 and used the seabed echo signal extracted from the PMT3 data and the water-surface reflection signal received by the APD as the reference true value of the actual measurement for evaluating the performance of the algorithm for data processing.The measured data are acquired by the airborne laser ALB system (Mapper5000), developed by the Shanghai Institute of Optics and Fine Mechanics, the Chinese Academy of Sciences on Yuanzhi Island. Yuanzhi Island, with an area of approximately 0.3 km25. However, the soft-threshold wavelet method produces better denoising of the deconvoluted waveform. Because the wavelet transform is a linear transform, the echo signal after orthogonal transform removes the correlation between the original signals to the maximum extent. The information from each component signal is retained, which provides a basis for the subsequent deconvolution to separate the cross-section.In the processing of airborne laser waveform data, suppressing the noise of the waveform data is an important step in preprocessing the waveform. The basic idea of the convolution algorithm is to regard the received waveform as the convolution of the laser emission pulse The experiment in this study consists of three steps. In the first step, the simulation results are used for the denoising experiment, and the simulation parameters are adjusted to maximize the approximation of the real received echo. The three main parameters in wavelet denoising  are compared and analyzed to obtain the optimal denoising parameters and lay the foundation for the following deconvolution operation.In the second step, the denoising data undergo four deconvolution operations, and the comparison with the direct deconvolution data demonstrates the necessity of denoising experiments. Simulation results are used to explore how the parameters affect the deconvolution algorithm. In the third step, the PMI1 and PMI2 channel data are used to evaluate the algorithm, where the surface position extracted from the APD data and the bottom position extracted from PMI3 are used as the true values of the measured data. Figure 27. Therefore, the high-frequency signal after wavelet transformation is subjected to wavelet threshold processing. The threshold processing function can be divided into hard and soft-threshold processing methods. The latter can be expressed as28; hence, herein we employ the soft-threshold denoising method. The wavelet-denoising threshold is mainly divided into four parts:Unbiased risk-estimation thresholdk as the threshold. The risk expression generated by the threshold isk. Therefore, the element with the least risk serves as the unbiased risk-estimation threshold.The original signal is arranged by taking the absolute value from small to large signals, squaring each, and using the square root of element Fixed thresholdHeursure thresholdWhen eta < crit, the fixed threshold is used; otherwise, the unbiased risk threshold is employed.Extreme value thresholdThe wavelet transform is divided into high-and low-frequency coefficients. The high-frequency coefficients reflect the noise and sudden changes in the signal, whereas the low-frequency coefficients reflect the overall trend and correlation of the signalWiener filter deconvolution29. The Wiener filter K is a parameter related to noise, whose value is to be determined through multiple experiments before using Wiener filtering. The time-domain estimate of the final cross-section isWFD assumes that the signal and noise are independent, and the Wiener filter Constrained least-square filter deconvolution30. These parameters can be calculated from the given received waveform, which is the advantage of constrained least-square filtering31. The core problem of RFD is to solve the problem of the sensitivity to noise of the degradation function. “Degenerate function” is a term used in image processing. In waveform signal processing, a degenerate function may be understood as the convolution of the transmitted signal and the target cross-section. To reduce the sensitivity to noise of the convolution, a minimum criterion function C with constraints is established,RFD is generally used for image restoration. WFD requires that the power spectrum of the undegraded image and noise be known. These two power spectra are usually difficult to estimate, while the RFD requires only the variance and mean of the noise32Richardson–Lucy deconvolutioni is expressed asi + 1 and i, respectively.RLD uses an iterative process to deconvolute the signal, which restores the likelihood of the signal by using an expectation maximization algorithm in the time domain. Iteration Blind deconvolution33. Iteration k − 1 yields the cross-section BD is similar to RLD, except that the received signal is iterated under the assumption that the point spread function is not known, and the point spread function  and the horizontal cross-sectional waveform are estimated at the same time, such that the gap between the original and the estimated cross-sections is reduced after multiple iterationsThe quality of the wavelet-transform denoising algorithm depends mainly on the choice of wavelet base, the decomposition level, and the threshold. These three parameters are compared and verified by simulation results to provide a good data basis for the deconvolution algorithm.Herein, we use four common wavelet bases for experiments, namely, Haar, Daubechies, Coiflets, and Symlets wavelets . The choice of wavelet base mainly considers orthogonality, tight support, symmetry, vanishing distance, and regularity. Orthogonality indicates that the inner product of every pair of wavelet bases is zero, hence all four are orthogonal functions. Supportability means that, if the function can only take a value near zero, the range of values is called a “compact support set.” Symmetry refers to whether the wavelet-basis function is symmetric, and vanishing distance ensures that as many wavelet coefficients as possible are zero or as small as possible. The nonzero wavelet coefficients help to eliminate noise, and regularity refers to the smoothness of the wavelet-basis function. Taking a water depth of 10 m as an example, we compare four wavelet-denoising effects under different SNRs Fig. . The SNRN is the number of signal samples, To compare the denoising effects of the four wavelet bases, this study employs the goodness of fit to reflect the degree of similarity between the denoising and the original signal. The goodness of fit can be expressed as:Table This section uses the db4 wavelet-basis function to evaluate the denoising effect of the four soft thresholds. The data are acquired from a water depth of 10 m as an example, and SNR = 16.91 dB.To clarify the comparison after denoising, Fig. N is the number of signal samples,The wavelet decomposition level is related to the data capacity and is one of the main parameters that affect denoising. Therefore, we obtain the optimal number of denoising decomposition layers of ALB by increasing the number of decomposition layers one by one. The denoising accuracy is quantified by the root mean square error (RMSE):Figure To explore how the wavelet-denoising algorithm affects the deconvolution algorithm, Fig. Figure Root mean square error of water-depth slope distanceRMSE of the water-depth slope distance is expressed as:Because the deconvolution algorithm restores the cross-sectional shape of the target, evaluating the accuracy of the water-depth data better reflects the quality of the algorithm. The Correlation coefficientWe calculate the correlation coefficient (CORR) between the deconvolution result and the true reflected echo:Water-depth goodness of fitThe water depth goodness of fit T required to evaluate the computational efficiency of the algorithm.The time To further analyze how processing affects each deconvolution algorithm, we add the following algorithm-evaluation indicators to evaluate how processing affects the deconvolution algorithm:Table Based on the deconvolution performance of each algorithm, we further explore how changes in the main parameters of the laser echo affect the algorithms . When SNR > 40 dB, the four algorithms perform well. Performing wavelet denoising before convolution thus improves the SNR and the accuracy of water-depth extraction.Comparing the four deconvolution algorithms shows that RLD and BD are affected similarly by convolution, and the same is true of WFD and RFD. The RLD and RFD algorithms are more robust, whereas the WFD algorithm is not. Although RFD is affected by intermittent pulses, it experiences a slight peak broadening, which is conducive to the extraction of peak time intervals. The RFD algorithm is relatively stable.Before processing, the PMI1 and PMI2 channel data, the infrared channel, and the deep-water channel data are preprocessed: The reflection from the water surface is extracted from the APD data, and the bottom reflection is extracted from the PMI3 deep-water channel data. These data are used as the true values for the measured data. On the one hand, a comparison of the measured data explores the performance and difference of the deconvolution algorithm in the positive channel and the orthogonal channel. On the other hand, it tests the effectiveness of the algorithm proposed herein, and the deconvolution algorithms are compared with the traditional peak detection method (PD), which is to extract the peaks directly from the original data, such that the PD algorithm does not need to calculate the two parameters of goodness of fit and correlation coefficient. Based on the characteristics of the laser reflection waveform and the simulation results, we divide the reflection data into three groups of water-depth slopes: 0–5 , 5–25, and > 25 m. Each group of data has 5000 original reflection signals. The experiments were conducted on the PMT1 and PMT2 channel data  data for water depths less than (greater than) 25 m. However, because the RLD algorithm is iterative, the RFD algorithm is more efficient. Further, the RFD algorithm applied to the PMT2 data leads to more accurate results than when applied to the PMT1 data, such that either the RLD or the RFD algorithm must be used, depending on the actual situation.For selecting wavelet bases, both the db4 wavelet and Haar wavelet bases offer good noise suppression. The Haar wavelets form a rectangular wave, and that the laser emission waveform is Gaussian. To avoid introducing more errors and to accurately extract the position of the bottom reflection, the db4 wavelet base must be used to denoise the ALB laser echo.To select a soft threshold, the heuristic threshold denoising method is equivalent to the fixed threshold denoising method, and both are better than the extreme threshold denoising method. Based on layer-by-layer decomposition, the heuristic threshold is best for echo signal denoising, and six decomposition layers are optimal for denoising.A comparative analysis of data deconvolution before and after wavelet denoising shows that, after wavelet denoising, the WFD and RFD algorithms perform significantly better for extracting reflections from the target, and the performance of RLD and BD algorithms likewise improves after wavelet denoising. The deconvolution experiment with the SNR model shows that a high SNR guarantees effective deconvolution. Therefore, performing a wavelet transform before the deconvolution experiment improves the deconvolution, which is particularly helpful for the RFD algorithm.The echo parameter has a varying effect on the algorithm: The RLD and RFD algorithms are more robust against such variations, whereas the WFD algorithm is less robust. Although RFD produces intermittent pulses, it has only small peak broadening, which facilitates the extraction of peak time intervals. Furthermore, the algorithm is stable.For multichannel data in actual experiments, both the RLD and the RFD algorithms are effective and robust. Although the accuracy of the BD algorithm is similar to that of the RLD algorithm (or even superior to a certain extent), it requires a longer calculation time and thus has low computational efficiency, making it unsuitable for large-scale data calculation. The RFD algorithm is more suited for PMT2 data than for PMT1 data. Compared with the RLD algorithm, RFD avoids the iterative process, and the solution accuracy is similar to that of RLD. Thus, RFD provides a new opportunities for ALB deconvolution algorithms.This study compares the simulated results with measured data for wavelet transforms, soft-threshold selection, and deconvolution. The main conclusions may be summarized as follows:


The re-entrant structures are among the simple unit cell designs that have been widely used in the design of mechanical metamaterials. Changing the geometrical parameters of these unit cell structures, their overall elastic properties , can be simultaneously tuned. Therefore, different design strategies  can be implemented to design advanced engineering materials with unusual properties. Here, using the theory of elasticity and finite element modeling, we propose a fast and direct approach to effectively design the microarchitectures of mechanical metamaterials with re-entrant structures that allow predicting complex deformation shapes under uniaxial tensile loading. We also analyze the efficiency of this method by back calculating the microarchitectural designs of mechanical metamaterials to predict the complex 1-D external contour of objects . The proposed approach has several applications in creating programmable mechanical metamaterials with shape matching properties for exoskeletal and soft robotic devices. Cellular materials  can be extensively found in nature. These natural cellular materials have been sources of inspiration for engineers for many decades to create bioinspired lattice structures , for insThe 2D mechanical metamaterials made from re-entrant structures are composed of hexagonal repetitive unit cells arranged rationally in a plane. As re-entrant unit cells belong to the class of bending-dominated ,9 unit cOne of the unique features in the design of re-entrant lattices is their inherent simplicity. This means a relatively low number of design parameters are necessary to design re-entrant lattices. That is probably one reason why these particular unit cell designs have received a great deal of attention with respect to other types of unit cell designs . For exFurthermore, by a rational combination of re-entrant unit cells with different values of Poisson’s ratios, one can control the local  or globThe state-of-the-art design of programmable mechanical materials with shape morphing and shape transformation properties requires spatial combinations of various unit cells to guide the deformation patterns within the structure and avoid any unnecessary strain localizations. This makes the design of such programmable metamaterials using re-entrant structures more challenging. It is because the re-entrant lattices exhibit anisotropic properties, which means their properties are different in two principal directions. Although several analytical relations have been proposed to determine the elastic properties of re-entrant structures ,28,29,30Here, using the theory of elasticity and finite element modeling, we propose a methodology to predict the deformation patterns in mechanical metamaterials created from re-entrant building blocks. The method offers a high-speed and direct approach in first, predicting their elastic properties and second, back calculating their microarchitectural designs. We also analyzed the efficiency of this approach in creating the programmable mechanical metamaterials with even more complex  microstructures by comparing our results with those of the literature. The proposed method has several applications in designing programmable mechanical metamaterials with shape matching properties for exoskeletal and soft robotic devices.y-axis , where φ is an Airy stress function. Stress fields derived from an Airy stress function satisfy the equilibrium equation and correspond to compatible strain fields. Airy stress function is suitable to solve stress boundary condition problems. The only component of the stress boundary condition here is the in-plane tensile loading applied along the irection . Therefox and y direction, and The re-entrant structure is under stretch loading, as mentioned above and shown in The stress field can be calculated according to Airy functions as Equation (3).The unknown coefficient ) can be calculated by replacing the Airy stress functions (Equation (3)) in Equation (5).x and y directions , the displacement field in the The functions f and g are unknown functions that can be calculated by applying the shear strain relation given in Equation (8):By integrating from Equation (10), the unknown functions can be written as:x-direction. The upper and lower edges of the plate were in tension. The equivalent load in the y-direction must be equal to the corresponding load in the elasticity theory. Python programming language was used to parametrically model the structure in Abaqus.Finite element modeling (FEM) of the re-entrant structure has been performed by Abaqus 6.12.1, as shown in First, the effects of porosity on the elasticity solution were studied. The area of the lattice structure was directly calculated from Abaqus. We then calculated the void space by subtracting this area from the equivalent solid plate. The porosity was defined as the ratio of the void space filling to the equivalent solid plate. The porosity value can therefore change between 0 and 1, where 0 means the fully solid plate. Here, we changed the porosity of the lattice structures by changing the dimensions of their unit cells  with Equation (7). Although the material properties vary along the y-coordinate (rows) were considered in Two other models with higher polynomial order for unit cell angle in a function of In this section, a method is presented to design a functionally graded re-entrant structure. Programming the microstructure to reach the desired deformation in tension is the main goal of this section. For this purpose, the angle of unit cells of the re-entrant structure must be calculated in terms of displacement.The angle effect on the displacement field can be obtained from Equation (12). The angle of the unit cell affects the mechanical properties of the structure and can change the displacement field. Therefore, the displacement field was assumed to be a function of the angle of unit cells, as shown in The desired deformation in tensile loading can be obtained by a re-entrant cellular structure using variation in angle. This change has the strongest effect on the mechanical properties of the lattice structure. The angle of each row can be obtained by fitting equations introduced in m is the number of segments and n is the row number.The angle of each row is dependent on the desired displacement. The desired displacement of each row must be calculated in the middle of the ligament of each row. The location of this point can be calculated from Equation (17), where To show the capability of this method, a typical linear deformation is considered as Equation (18).By dividing the height of the structure into 35 rows, the angle of each cell can be back-calculated from The implemented method was compared with experimental test data published in . The defTo show the application of this method for the design of programmable shape-matching mechanical metamaterials, two arbitrary shapes were used in To conclude, here, the functionally graded mechanical metamaterials made from re-entrant structures under uniaxial tensile load were modeled using the elasticity theory and FEM. The effects of different morphological and geometrical parameters  on the overall shape of deformation of such materials were analyzed. We also showed how the presented approach can be used for the back-calculation of the microarchitectural designs of lattice structures in order to achieve any arbitrary target shape of deformation. This approach can be considered an effective tool in the design of programable mechanical metamaterials with shape matching properties. We expect this approach can be extended to the design of 3D structures with the same properties and predicting arbitrary shapes in 2D or 3D. The proposed design method can be used in many applications, such as soft robotics, fashion industries, and medical devices.


This study focused on the analysis of the mechanical properties of thin-walled specimens fabricated by fused deposition modelling (FDM). Two materials were considered, i.e., polylactide (PLA) and polylactide with carbon fiber (PLA-CF). The article describes how the specimens with different thicknesses and printing orientations were designed, printed, measured to assess their geometric and dimensional accuracy, subjected to tensile testing, and examined using scanning electron microscopy. The data provided here can be used for further research aimed at improving filament deposition and modifying the base material by combining it with different components, for example carbon fiber. The investigations revealed that the properties of thin-walled elements produced by FDM varied significantly depending on the thickness. So far, this problem has not been investigated extensively. Research by analyzing the key parameter, which is the direction of printing that is important for thin-walled models, provides a lot of new information for designers and technologists and opens the way to further extended scientific research in the field of the strength analysis of thin-walled models produced by 3D printing, which is very applicable to structure optimization in the era of the industrial revolution 4.0 and progress in the LEAN manufacturing process. Additive manufacturing (AM) has received much attention over the last few years. AM technologies are becoming increasingly popular as an attractive alternative to conventional manufacturing, especially CNC machining, injection molding, and casting , becauseThis article analyzes tensile test results for two materials, i.e., pure PLA and PLA reinforced with carbon fiber. The SEM examinations explain the mechanisms affecting the strength of the two types of prints.The experiments were conducted for specimens made of two polymers. One was polylactide (PLA), with a trade name EASY PLA, produced by Fiberlogy, Brzezie, Poland, and the other was a composite, consisting of polylactide and carbon fiber (PLA-CF) sold under the trade name of CarbonFil, by Formfutura from the Nijmegen, Netherlands. The mechanical properties of the materials used to build the tensile specimens are given in Each 3D printing technology uses specific equipment and a specific process. They all, however, have some limitations. To choose a proper 3D printing method, we just need to answer some basic questions, i.e., how thick or thin, how big or small, and how precise the elements printed must be. Another problem to be dealt with is their physical properties, particularly mechanical and metrological properties. FDM is one of the most popular technologies with a large variety of applications. Since composites have recently seen an increase in popularity, extensive testing is necessary to determine the mechanical properties of such 3D models. The material is fed in the form of a filament from a spool to the heated nozzle by mechanical pressure from rollers. The temperature in the nozzle is regulated by the printing machine control system. Once the melting point is reached, the material in the filament form is first placed on the printer’s build plate and then as a successive layer on top of the previous layer, adhering to it mainly under the action of adhesive forces. In this way, the object printed has the same geometry as the model created using 3D CAD software. Its mechanical properties are slightly different from those of the material used for printing, i.e., a polymer filament. The properties of 3D prints are largely dependent not only on the type of printing material or the possibilities of the printing machine but also on the process or rather the control of the process parameters. In FDM, the key parameters involved in the printing process include printing direction, layer height, extruder temperature, ambient operating temperature, temperature of the movable build plate, temperature in the build chamber, deposition rate, nozzle diameter, and infill density. As 3D printing is a layer by layer fabrication process, properly selected parameters guarantee that the print has specific mechanical properties, which are largely dependent on the printing direction.One of the aims of this study was to analyze the effects of the printing direction on the mechanical properties of thin-walled composite elements. There has been some research in this field however, it has focused on solid objects. From the above description, it is clear that the 3D printing process may have a significant effect on the mechanical properties of elements. The major forces responsible for the proper bonding of a polymer filament extruded onto the layer beneath it are adhesive and cohesive in character. Depending on the positioning or orientation of the model on the build plate, either type of the forces may predominate. The way filaments are arranged is also vital; there are differences between thin-walled elements and large solid elements with varying infill density.The method described in this article allows thin-walled objects to be 3D printed in different orientations. Tensile tests were conducted for specimens differing in thickness and printing orientation to determine the tensile strength and the elasticity modulus. Currently, we can observe dynamic activities for standardization of 3D printing technologies and it seems, as the experimental data reveal, that 3D prints should be divided into those with a fully solid structure and those with thin walls; the latter may have a thickness of walls of less than 2 mm. So far such 3D samples have not been analyzed.stl files. The triangulation parameters used in the export options were as follows: resolution—adjusted; linear deviation—a tolerance of 0.016 mm; angle tolerance of 1. It is important that the values of the triangulation parameters should not be too low otherwise it will not be possible to create rounded objects. They should not be too high, either because such .stl files are too large for the embedded machine software to analyze.The shape of the test specimens was designed in accordance with the ISO 527 standard. The exception is one dimension, i.e., the thickness of the specimens , the impact of which was analyzed as part of further tests and compared with the reference specimen in fully compliant with ISO 527 with a thickness of 4 mm. The solid models of the specimens were created in SOLIDWORKS software  and saved as digital .stl-type file.stl format were positioned on a virtual build plate of the MakerBot Replicator (5th Generation) printing machine  in three orientations, as shown in x axis had the largest flat surface on the build plate. The specimens oriented along the y axis had the side touching the build plate. The specimens oriented vertically along the z axis were placed close to one another to ensure stability in printing and prevent models from collapsing.The 3D model files saved in the .✓Layer height: 0.2 mm;✓Infill density: 95% ;✓extruder temperature: 230 °C for PLA and 250 °C for PLA-CF;✓nozzle diameter: 0.4 mm.The printing parameters for both PLA and PLA-CF  used in the experiments were:The selected technological parameters affect the obtained mechanical properties and when other parameters are used, such as nozzle diameter, temperature, printing speed, cooling speed, the mechanical properties of the models produced may be slightly different. However, the mechanism of the influence of the print direction and thickness of the built models on the mechanical properties, regardless of the type of 3D printer in FDM technology, should be retained, which allows the presented test results to be related to different types of FDM printer. Examples of prints are shown in After the printing was completed, the support material was removed from the build plate, the models were cleaned by removing the remaining filaments and measured to check their geometry, and finally the tensile tests were conducted. There were five specimens representing each type for the purpose of statistical calculations.ā and the average width The thickness and width of each specimen built for the static tensile strength tests were measured along the gage length at three points, at the beginning, middle and at the end. Then, the average thickness stl file to ensure communication with the printer software. The actual dimensions, i.e., the dimensions of the prints, differ from the nominal dimensions.The measurements were performed by means of a micrometer with an accuracy of 0.01 mm. The specimens were marked according to the nominal thickness, the specimen number in a series, and the printing direction (the No. column in the tables). For example, a specimen marked as 1.4 2X is a specimen with a nominal thickness of 1.4 mm, second in a series, oriented in the X direction on the build plate b. The PLThe static tensile strength tests were performed at a crosshead speed of 1 mm/min using an Inspekt mini 3kN universal testing machine produced by Hegewald and Peschke MPT GmbH. mR was calculated by the embedded machine software according to the following formula:mF—maximum load, ā—average measured thickness of the specimen, The ultimate tensile strength mR could be calculated. Such programs use nominal values of the specimen dimensions, the same for the whole series; the values of the dimensional deviations are taken into consideration when measurement errors are estimated. In the case of thin-walled specimens, this approach would provide distorted results for the whole experiment.The average values of the specimen width and thickness  than for the x and y axis orientation, hence the adoption of two ranges of unit strains (strain). The results of the static tensile tests for the PLA specimens are shown in The values of the elasticity modulus E were calculated automatically by LabMaster using the regression method for the prints oriented in the X and Y directions with deformations The results of the static tensile strength tests performer for PLA-CF prints are shown in mR and the maximum percentage deformation mɛ of the PLA and PLA-CF specimens observed at a maximum tensile force are given in The ultimate tensile strength The values of the elasticity modulus E estimated using the regression method from the data of the static tensile strength tests obtained for the PLA and PLA-CF specimens are provided in Scanning electron microscopy was used to examine the flat surface of the specimens before and after tensile tests in the fracture area. The axonometric view of a specimen after failure magnified ×1000 in −the differences between the nominal (designed) and actual dimensions of the 3D printed thin-walled specimens dependent on their orientation on the build plate ;−mR, the maximum deformation mɛ in percentage ΔaX,Y= 1.4 mm  and the entation , b¯X = 5a) and width (Δb) were calculated for each series of measurements from the data given in The relative errors for thickness  direction is lower than that of the other types of specimens. The main forces are the adhesive forces bonding the layers of filament, which are smaller than the cohesive ones. The 1.4 mm thick PLA-CF specimens were reported to have higher strength than the other specimens, which confirms the suitability of this composite to build models characterized by higher mechanical properties.The investigations, including microscopic observations, revealed voids inside the specimens, as was the case with the PLA specimens 1.8 mm in thickness, and a large gap between the neighboring layers of filament. The microscopic examinations of the PLA-CF specimens showed that carbon fibers could break or slide out of the base material due to the stresses acting on them. Carbon fibers that were well embedded in the base material, i.e., PLA, also transferred tensile stresses, so the specimens printed in the X or Y orientation had higher strength than those built in the Z orientation. The PLA specimens reinforced with CF generally had higher strength than the specimens made of pure PLA. However, carbon fibers reduced the strength of elements printed in the Z orientation because carbon filaments were placed in the direction perpendicular to the tensile stresses. As a result, they did not transfer stresses along their axes; the filaments were weakened in the cross-section. Microscopic studies also showed that carbon fibers were responsible for a greater number of problems in the area of bonding of the filament layers. Similar conclusions were drawn by Valvez et al. , who obsThe tests described here revealed that the elasticity modulus E was dependent on the printing direction, the specimen thickness, and the type of printing material. Generally, the values of the elasticity modulus were lower for the specimens built in the Z direction than for those printed in the X or Y orientation , the elasticity modulus was lower for the thicker reference specimens (4 mm) than for the thinner ones, as shown in The problem is difficult to interpret unequivocally.Summing up the considerations on the mechanical properties of thin-walled PLA and PLA-CF prints built using the FDM technology, we can conclude that like all new engineering materials, these innovative materials showing orthotropic properties should be tested thoroughly considering various aspects of their fabrication. The analysis of the tensile test results and the microscopic data was undertaken to formulate the following conclusions.The FDM technology has a high potential to use different materials. It is possible, for example, to modify the base material by reinforcing it with carbon fiber, which may improve or worsen the mechanical properties of the print, depending on the orientation.The analysis of the mechanical properties of the thin-walled specimens showed that the tensile strength of the thinner specimens (1.0–1.8 mm in thickness) was generally much higher than that of the 4 mm reference specimens. The results can be useful to both design and manufacturing engineers as well as those dealing with the standardization of this 3D printing technology.The base material, PLA, has anisotropic properties; its behavior changes depending on the print orientation. This observation is important because when carbon fiber is used, the composite, i.e., PLA-CF, becomes a typical orthotropic material.The research results presented here can be used in future studies on the FDM printing method to further reduce the number and size of voids and gaps and to modify the base material by adding different components, e.g., carbon fiber.It is vital to properly match the printing parameters to the material used. The printer and the software used in the study were suitable for the purpose.The infill density parameter was found to be responsible not only for the amount of material used on the inside of the object printed but also for the presence of voids and gaps contributing to the material degradation when under tension. Furthermore, there were microckracks in the bonding zone along the direction of the maximum tangential stress, which also led to the material degradation when loaded.The test results show that the elasticity modulus of thin-walled models varies depending on the printing direction and the specimen thickness. This relationship was particularly visible for the thinnest specimens, i.e., those 1 mm in thickness.


A large amount of semantic content is generated during designer collaboration in open-source projects (OSPs). Based on the characteristics of knowledge collaboration behavior in OSPs, we constructed a directed, weighted, semantic-based knowledge collaborative network. Four social network analysis indexes were created to identify the key opinion leader nodes in the network using the entropy weight and TOPSIS method. Further, three degradation modes were designed for (1) the collaborative behavior of opinion leaders, (2) main knowledge dissemination behavior, and (3) main knowledge contribution behavior. Regarding the degradation model of the collaborative behavior of opinion leaders, we considered the propagation characteristics of opinion leaders to other nodes, and we created a susceptible–infected–removed (SIR) propagation model of the influence of opinion leaders’ behaviors. Finally, based on empirical data from the Local Motors open-source vehicle design community, a dynamic robustness analysis experiment was carried out. The results showed that the robustness of our constructed network varied for different degradation modes: the degradation of the opinion leaders’ collaborative behavior had the lowest robustness; this was followed by the main knowledge dissemination behavior and the main knowledge contribution behavior; the degradation of random behavior had the highest robustness. Our method revealed the influence of the degradation of collaborative behavior of different types of nodes on the robustness of the network. This could be used to formulate the management strategy of the open-source design community, thus promoting the stable development of OSPs. In contrast to a traditional product design process, the open-source design (OSD) community is spontaneously organized by diverse community members according to their interests and needs. To achieve common work goals for specific open projects, designers use collective wisdom, analyze knowledge, and carry out distributed task collaboration . These dSimilar to other forms of networks , important nodes in the OSD community network—known as “opinion leaders”—dominate knowledge sharing, information dissemination, public opinion orientation, behavior, and decision-making guidance ,5. TheseThough the definition of opinion leaders differs between network types, all definitions reflect that opinion leaders have an influence on people’s attitudes, opinions, and behaviors ,18. In tThe information interaction analysis method identifies opinion leaders in the network using the semantic analysis of user comments or the sentiment analysis published by users. This method is more suitable and prevalent in social networks because it is mainly based on comment data. For example, Chen et al.  proposedSome scholars have created opinion leader identification models that combine the above two methods. For example, Clat  establisSince the combination of these two identification methods can identify opinion leaders from a more comprehensive index, we consider both the network structure and semantic content when identifying opinion leaders in the OSD community. However, compared with the content publishing community, the OSD community places more emphasis on knowledge collaboration. As such, there are many differences in the selection of identification indexes and the identification methods of opinion leaders. Therefore, this paper first proposes a semantic-based KCN construction method, to allow semantic information to be embodied in the KCN. Based on the constructed network, the opinion leader identification index considers the degree of interaction between nodes, that is, the ability of nodes themselves to influence other nodes. The communication location of nodes is considered from the perspective of the overall macrostructure of the network . For these indexes, an entropy weight and TOPSIS algorithm are used to evaluate the users in the community one by one to identify the opinion leaders.As information and viruses spread in the network, the propagation of users’ behavior in the network follows certain rules. The traditional propagation model has great reference significance for the study of behavioral propagation in the network . For exaRobustness refers to the ability of the system to maintain its functions or characteristics when it is subjected to external interference or destruction . ResearcWe found that most current studies on the robustness of OSCs adopt the nodal-based degradation approach. When the nodes are deleted, the edges directly connected to the nodes are also deleted, resulting in the ineffective use of edge strength information. In OSCs, designer nodes will not withdraw directly from the project or community after negative interference; they will first be reflected in the decrease of collaboration intensity with other designer nodes. Additionally, due to the existence of social influence and homogeneity, the degradation of opinion leaders’ behavior in OSCs leads to the degradation of other nodes or edges. The influence of this behavior propagation phenomenon on network performance—where the degradation of individual nodes or edges causes the degradation of further nodes or edges—is rarely paid attention to.In view of these problems, this paper first constructs a semantic-based OSC KCN by combining two key factors among designers: comment content and comment frequency. Four network features are selected and the entropy weight and TOPSIS evaluation method is adopted to identify the opinion leader nodes in the network. Then, based on the identified opinion leaders, the SIR epidemic model is applied to design the propagation mode of the degradation of opinion leaders’ collaborative behavior. Finally, robustness analysis is carried out using empirical data from the Local Motors open car design community. This includes analysis on the degradation of (a) opinion leaders’ collaborative behavior, (b) main knowledge contribution behavior, and (c) main knowledge dissemination behavior. Based on research results, we present suggestions for robustness protection.Calculate the keyword candidate set Construct the co-occurrence matrixCalculate the candidate word weight Arrange in descending order according to the calculated weightCalculate the content intensity of each designer: follow step (1) to segment the overall comment content of each designer in the project to obtain a set of keyword candidates for each designer Obtain In the OSD community, the large-scale collaborative behavior of designers  is the key process to complete product design. To construct a directed, weighted, semantic-based KCN: (1) the knowledge collaboration behaviors of designers in the large-scale collaboration process are screened out, including semantic content at the knowledge production level and social behavior at the knowledge circulation level; (2) the semantic content at the knowledge production level is represented by collaborative content intensitylgorithm  to extraIn this paper, we used Local Motors, an OSC for car design, as the research object. The Local Motors community has an avant-garde design concept of “production for customers”, where designers can freely choose design projects of interest, exchange creative models, and propose design solutions. In this mode, the design scheme can be tested, retested, and perfected in this collaboration cycle. After finalization, it can be co-manufactured with the customers who buy the products.We chose project LF-01, which has the largest number of participants and can best reflect the characteristics of this community, for our research. This project was established in January 2014, and as of 16 November 2016, the project contained 673 designers and 7757 instances of communication. The network features of LF-01 are consistent with most projects; therefore, it can suitably represent the situation of most OSPs.To better simulate two-way dissemination and two-way collaboration behaviors among community members, this paper uses a directed weighted network graph to model. Community designers are represented by node set The small-world characteristic shows that the network has some “shortcut” connections to connect different subgroups, that is, there are some key collaborations in the knowledge collaborative relationship of many participants, and they play a key role in reducing the network distance. Therefore, it is necessary to study the opinion leaders in the network. The scale-free characteristic indicates that during the evolution of the project network, new participants tend to connect to larger nodes in the original network. The disassortative characteristic reflects that nodes with lower degree values are more inclined to establish connections with nodes with higher degree values. Therefore, it is also necessary to study the influence of the following effect on network performance.Identifying opinion leaders in OSP can be seen as finding the most important nodes in the KCN. Generally, the importance of nodes can be measured by the centrality index . The impDuring the communication and collaboration of design projects, designers in the OSD community can influence other designers’ views and behaviors, and can even stimulate creativity through comments, so as to finally complete the product design process. This can be regarded as the ability of a network node to influence other nodes. In social network analysis, centrality is the most important index with which to evaluate node attributes. It is used to measure a node’s importance and collaboration ability with other nodes. There are three centralities to note: (1) strength centrality, that is, the sum of the edge weights of the node pointing to other nodes, which reflects the node’s active knowledge collaboration ability; (2) closeness centrality, that is, the reciprocal of the shortest path average value between nodes, which shows the ability of one node to get rid of the control of others; and (3) betweenness centrality, that is, the number of times a node acts as an “Intermediary“, which measures the ability of a node to control the collaboration with others. Further, opinion leaders are often in the structural hole position in the overall macrostructure of the network. They have more information resources and cross various groupuscule through knowledge collaboration. In this position, opinion leaders occupy the best communication location, which can filter information, disseminate information, and control the direction of public opinion . Therefo1.Strength centrality.The simplest measure of the centrality of a vertex in a network is just the degree of the vertex. In directed weighted networks, vertex strength is divided into in-strength and out-strength. The out-strength of node o node i . Table 2The cumulative distribution and fitting function of in- and out-strength are plotted in a logarithmic coordinate system, as shown in 2.Closeness centrality.Closeness centrality is the reciprocal of the sum of the length of the shortest paths between the node and all other nodes in the network , and it The top 10 nodes of in-closeness and out-closeness centrality are completely different, indicating that there are different types of designers in the network. For example, designers who have professional knowledge and are well known by others will choose more meaningful collaboration and have low initiative. Designers who actively seek collaboration cannot get an effective response. Therefore, it is necessary to identify the important nodes in the network.3.Betweenness centrality.Betweenness centrality is the number of short paths between nodes in the network that pass through a given node . “IntermFurther observing 4.Structural hole.Structure hole mainly refers to the fact that there is no direct connection between the two nodes in the network, and they must be connected through a third party. The third party occupies the structural hole position in the relationship network and plays the role of “network bridge”. The existence of structural holes means the existence of opinion leaders.In this paper, four structural hole indexes as proposed by Burt  are seleThe decision scheme set: Of the evaluation indexes selected in this paper, only the hierarchy in the structural hole is used as the cost index, and the others are used as the gain index. To eliminate the impact of different index dimensions, the evaluation results are normalized toThe entropy weight method is selected to determine the index weight. The weight of the The set of maximum value of each evaluation index is positive ideal schemeAccording to the above results, the nearness Finally, the final evaluation index of each decision scheme is calculated, which represents the importance of nodes in the network. This paper considers the top five nodes to be the opinion leaders in the community.In this paper, a node-importance synthesis method for multi-attribute decision making based on entropy weight and TOPSIS  is selecHere, we use an OSP of Local Motors as an example to illustrate the opinion leader identification method. First, a directed, weighted, semantic-based KCN is constructed. Four indexes are calculated using UCINET analysis software and SPSSAU software: node-strength centrality (SC), closeness centrality (CC), betweenness centrality (BC), and structural hole (SH). Then, after the evaluation results of all nodes under the four different indexes are standardized, the original decision matrix is obtained, as shown in Y, is:The respective information entropies of the four indexes are 0.8855, 0.9465, 0.8745 and 0.9334, the respective weights of these indexes are Next, the positive and negative ideal scheme is generated:The distance D and closeness Z from each decision scheme to the ideal scheme are calculated. Sorted according to closeness Z, the specific results are shown in Having identified the opinion leaders of the KCN, this section now further describes the influence of the opinion leaders on other nodes, that is, the propagation effect of opinion leaders’ collaborative behavior.The classical SIR epidemic model  considerAt timeFrom the semantic-based KCN modelNetwork robustness can be defined as the degree of retention of network performance when network nodes or edges fail . The impTo reflect the degree of network connectivity retention after the network is attacked, the relative network connectivity size Network efficiency describes the difficulty of information dissemination. Network efficiency is expressed as the sum of the efficiency of all nodes in the network, where node efficiency is the reciprocal of the shortest path length between two nodes :(18)E=1nhe nodes ; consequFrom the above analysis, the weighted efficiencyTo reflect the degree of knowledge collaborative efficiency retention after the network is attacked, the relative knowledge collaborative efficiency size Degradation mode design is the key to robustness analysis. For the open-source design community, the KCN will not be subject to external attacks. The main risks and disadvantages of the network come from (1) designers voluntarily leaving the community, which is called node degradation, and (2) designers’ willingness to collaborate is reduced, which is called the degradation of knowledge collaboration behavior. Most existing research is based on the degradation of the nodes themselves, and little research exists on the degradation of knowledge collaboration behavior or the propagation mode of opinion leaders’ behavior degradation in the community. In OSCs, high-strength nodes are very important for maintaining network connectivity, and they represent the main knowledge contribution nodes. The high betweenness node plays an important role in maintaining the information dissemination speed of the network, and it represents the main knowledge dissemination node in the network. By ranking nodes according to their point strength and betweenness, and reducing the weights of the directed edges in turn, we can describe the collective degradation of the main knowledge contribution behavior and the main knowledge dissemination behavior.In addition, opinion leaders are the most influential nodes in the network. Under the influence of the external environment, dissatisfaction with the community ecology and their own factors, their knowledge collaborative intensity may decrease; if so, their propagation characteristics lead to the degradation of other nodes’ collaborative behavior. Therefore, this should be reflected in the design of the degradation mode of opinion leaders’ collaborative behavior. In this paper, an SIR model is used to simulate the propagation mode of the opinion leaders’ degradation behavior and calculate the change of network performance. The simulation process is shown in Considering the characteristics of the above three kinds of nodes and their roles in the network, we design three degradation modes for collaborative behavior, as shown in Based on the construction of a semantic-based KCN, the identification of opinion leaders, and the design of a knowledge collaboration behavior degradation mode, the index changes of network robustness under random degradation, and the three degradation patterns are simulated using Python 3.7 software. Originpro 9.0 software is used to create the contrast chart of the experimental results.This paper starts from The paired T-test is conducted on the change trend data of the index values under the four behavioral degradation modes collected from the simulation experiment, as shown in Through further observation of the change of the index value we find that when Due to the following effect of opinion leaders on other designers, the index value of the network under the degradation mode of opinion leaders’ collaborative behavior drops to 50% of the original network at time When comparing the degradation patterns of main knowledge contribution behavior (VS) and main knowledge dissemination behavior (BS), we find that the top five nodes in the original network partially overlap and the decline degree of the initial index value is basically the same. With the continuous evolution of the network, the index value is more sensitive to the degradation of the main knowledge contribution behavior; therefore, protecting the main knowledge contribution nodes should also be the focus of robust management.A semantic-based KCN was established by taking the weighted processing of collaboration content and collaboration frequency among designers in the OSD community as edge weights.The index system of strength centrality, closeness centrality, betweenness centrality, and structure hole was constructed. The multi-attribute comprehensive evaluation method of entropy weight and TOPSIS was used to identify the opinion leaders, and the SIR model of opinion leader’s behavior influence was proposed to describe the propagation of opinion leader’s behavior.Based on the behavior characteristics of designers in the OSD community, we designed three degradation modes based on opinion leaders’ collaborative behavior, main knowledge dissemination behavior and main knowledge contribution behavior. Robustness analysis experiments were conducted based on empirical data from the Local Motors OSD community. The analysis results showed that network robustness was the lowest under the degradation mode of opinion leaders’ collaborative behavior, followed by the degradation modes of main knowledge contribution behavior and main knowledge dissemination behavior. The degradation mode of random behavior had the highest robustness. Under the degradation mode of opinion leaders’ collaborative behavior and the propagation mode of their degraded behaviors, network performance can quickly drop to its lowest point. The continuous restoration of collaborative behaviors by nodes can cause the network performance to rise after a sustained period at its lowest state, but this increase is limited.Based on the characteristics of knowledge collaboration in OSCs, we analyzed the network robustness under the degeneration model of knowledge collaboration behavior. Our research contributions are as follows:The community should effectively identify the nodes of opinion leaders and encourage the knowledge collaboration behavior of such nodes with core influence. Research shows that a harmonious community environment, perfect incentive mechanism, and competitive coordination mechanism can effectively improve the collaboration behavior of nodes ,63. The Community managers should also pay attention to the protection of main knowledge contribution designers and main knowledge dissemination designers to avoid the degradation of their collaborative behavior. For example, the star sign should be set for the high-quality knowledge contributed by designers, and the case databases should be set up for the active users to consult and so on; this should increase the sense of worth of the main knowledge contributors. Further, the main knowledge dissemination designers should be encouraged to join in with the community management work, and attention should be paid to their opinions on the development of the community to increase their sense of belonging and achievement.A talent introduction mechanism should be set to attract more designers to join the community to collaborate knowledge. Further, after the degradation of designers’ behaviors, resources should be first concentrated to restore the collaborative behaviors of core influential nodes to reduce the rapid decline of network performance.Based on the analysis results, the following management implications were also obtained:This paper constructed a semantic-based KCN to effectively identify opinion leaders and studied the network robustness under different behavior degradation modes. This method is particularly suited to organizations that prioritize large-scale knowledge collaboration, such as OSP or OSD communities. One limitation is that our work is aimed at the single project network. In the OSC, the same designer can participate in multiple projects, and the degradation of designers’ collaborative behavior may have an impact on multiple projects. Therefore, we will carry out further research on the robustness of the OSC’s multi-project KCN in the future.


Nowadays, hydrogels are found in many applications ranging from the industrial to the biological . According to the specific needs of individual applications, it is necessary to be able to modify the properties of hydrogel materials, particularly the transport and mechanical properties related to their structure, which are crucial for the potential use of the hydrogels in modern material engineering. Therefore, the possibility of preparing hydrogel materials with tunable properties is a very real topic and is still being researched. A simple way to modify these properties is to alter the internal structure by adding another component. The addition of natural substances is convenient due to their biocompatibility and the possibility of biodegradation. Therefore, this work focused on hydrogels modified by a substance that is naturally found in the tissues of our body, namely lecithin. Hydrogels were prepared by different types of crosslinking . Their mechanical properties were monitored and these investigations were supplemented by drying and rehydration measurements, and supported by the morphological characterization of xerogels. With the addition of natural lecithin, it is possible to modify crucial properties of hydrogels such as porosity and mechanical properties, which will play a role in the final applications. Hydrogels are hydrophilic polymers with a three-dimensional network structure that have the ability to absorb a large volume of water due to the presence of hydrophilic moieties, which makes them particularly suitable materials for biomedical applications  . SelectiAn equally important property of hydrogels is their ability to simulate and mimic biological systems such as the extracellular matrix (ECM), which is, in fact, a structural support network composed of diverse proteins, sugars, and other components. ECM regulates cellular processes including survival, growth, proliferation, migration, and differentiation . EngineeSince the 3D network structure of hydrogels is mainly responsible for their mechanical properties and porous microstructure, one of the possibilities of how to modify, upgrade, or tailor properties of hydrogels is to incorporate hydrophobic or micellar domains into the gel structure .Pure hydrophobic association (HA) hydrogels refer to physically crosslinked hydrogels formed by hydrophobic interactions, which account for 5–20% of the total amount of polymer. The bulk of hydrophobic association hydrogels are produced by micellar copolymerization . For insAn alternative approach to enhance the toughness of the hydrogel network is to introduce particles as additional crosslinking points  . Latex pSince inorganic nanoparticles have a high specific surface area, their incorporation into the hydrogel network could also improve its mechanical behavior relating to surface structure and charging . At the On the other hand, the embodiment of polymeric nanoparticles provides the ability to encapsulate both hydrophobic and hydrophilic substances . In addiThe properties of hydrogels, as mentioned previously, can be adapted not only through the appropriate choice of materials and crosslinking techniques, but also by modifying the internal structure of the gel by using a structure modifier such as lecithin during the preparation process. It should be remembered that lecithin is a typical amphiphilic phospholipid mixture primarily containing distearoylphosphatidylcholine, which possesses good biocompatibility and capability to enhance the bioavailability of co-administered drugs . LecithiAmong the different types of lecithin-based systems, the most common platforms in this area are liposomes and microemulsions . LiposomSubstantial research is focused on modifying the internal structures of hydrogels, however, to the best of our knowledge, there has previously been no systematic study investigating the preparation and targeted modification of the internal structures of biocompatible hydrogels that focused on the use of natural amphiphilic substances and their crucial  application properties.Thus, this work focuses on the effect of the structure modifier lecithin  and its concentration on the resultant mechanical properties of differently crosslinked hydrogels. The results of this work could provide a deeper understanding of the interactions between lecithin and the hydrogel network, and, alternatively, between lecithin and model drugs. Lecithin aggregates in hydrogels can also be viewed as a model of phospholipid structures (like cell membranes) occurring in real tissues, and thus as a model of their potential impact on the rheological or transport properties of the extracellular matrix.On the basis of the prior experience of our team and in an attempt to investigate the effect of different crosslinking strategies on the final properties of hydrogels, the following materials were selected: agarose as a physically crosslinked hydrogel, alginate crosslinked by polyvalent ions as an ionically crosslinked hydrogel, and PVA-chitosan as a chemically crosslinked hydrogel.As stated in Agarose was a representative of the physically crosslinked hydrogel matrix, whose properties were affected by lecithin content. Hydrogel samples after preparation as well as samples after the drying and rehydration procedure were studied . From the results obtained by scanning electron microscopy (SEM), the effect of lecithin addition could be observed in sectional view. The surfaces of these xerogels were smooth and with no visible pores on the micrometer scale. In sectional view, the lecithin-free xerogel exhibited a layered structure of polymer fibers with no visible interferences see . The samFor these xerogels, gas sorption measurements were also performed . The low−) interact with the polyvalent ions (Ca2+) to form a bond  acid units of alginate  during the drying process.The rehydrated samples followed a similar trend with respect to the moduli values, where these values decreased with increasing lecithin concentration. Average moduli values in LVR reported in the table below  were higThe rheograms obtained during the frequency sweep tests (expressed as viscoelastic moduli on applied frequency) c obeyed 2+-lecithin interactions, which influence the hydrogel structure and thus enable it to better hold water. As for the swelling after drying, it can be observed that the samples with higher lecithin concentrations were able to absorb water more rapidly and to a higher capacity, which is again due to the modified hydrogel network due to the presence of lecithin.The drying curves for the alginate-lecithin systems were very similar almost irrespective of the lecithin content, in contrast to the drying dependence obtained for the freshly prepared hydrogels of alginate solely a. The di2 resulted after air-drying. Instead, the surface of xerogels with 1 and 2 wt.% lecithin is practically devoid of crystalline aggregates, with some degree of roughness, which led to a more compact structure of these mixed systems in their dry state. The morphological characteristics microscopically revealed are in accordance with the decreasing tendency of the specific surface values  could be related to Ca2+ consumption in favorable electrostatic interactions with lecithin anions, which means that the crystalline structures observed in the case of alginate xerogels without lecithin and for those with 0.5 wt.% lecithin could be due to the excess of CaCl2 contained in these explored samples. SEM images taken for xerogels prepared by ionic crosslinking show the effect of lecithin on the surface morphology of the samples see . SurfacePoly and chitosan crosslinked by the epichlorohydrin was a representative of the chemically crosslinked hydrogel matrix. Epichlorohydrin reacts with either the hydroxyl group of PVA or amino group of chitosan to form a highly reactive intermediate. This intermediate product reacts with another hydroxyl (PVA) or amino group (chitosan) to form the crosslinked structure. Study of these hydrogels, both in their freshly prepared state, after air-drying at 40 °C and their subsequent rehydration and as xerogels, showed some physico-mechanical properties altered by the lecithin content  on three differently crosslinked hydrogels . The bulk of this work was to study differences between the gels investigated immediately after preparation and the corresponding rehydrated xerogels (prepared by swelling). By choosing the lecithin content, we were able to modify some of the mechanical properties of the hydrogels with a modified internal structure, especially in the case of the rehydrated ones. In this regard, the addition of lecithin had the strongest influence in enhancing the strength of chemically crosslinked PVA-chitosan gels, which is partially consistent with the mesh size and by the amount of water absorbed into their structure after being previous air-dried. Apart from the rheological data and those obtained from the kinetics of water loss during hydrogel dehydration, these conclusions were supported by the scanning electron microscopy and gas sorption experiments performed on the xerogels. For this type of material, even though gas sorption appears to be inappropriate, however, it serves to confirm the non-porous structure of the xerogels.In this work, we determined that the addition of phospholipid lecithin into the hydrogel matrix can alter their mechanical properties, which might be highly beneficial knowledge for the use of such hydrogels in particular applications. However, the transport properties also need to be investigated. Therefore, further transport experiments are required, which are absolutely crucial for a better understanding of such hydrogel materials and how they can be used in final applications.Hydrogels with distinct gelation mechanisms   were stuThe materials and their concentrations and ratios were selected on the basis of data previously reported ,22,23,24x) during drying was calculated using the following formula:mt is the weight of the gel at time t, and m0 is the weight of the hydrogel in the swollen state.The ability to hold, release, and absorb water was tested by different approaches. Water loss was monitored by means of simple drying tests. All samples were dried either in the laboratory dryer at 40 °C and regularly weighed, or in a semi-automatic moisture analyzer , where the weight was recorded automatically. The relative weight of the hydrogel (ma) was calculated by:mt is the weight of the hydrogel at time t, and mx is the weight of the xerogel. The hydrogel samples were regularly weighed on analytical scales to study their swelling kinetics.Often very small weight losses of water from the hydrogel samples made using drying scales more difficult. For this reason, drying kinetics were mostly studied using the combination of laboratory driers and analytical scales, upon which samples were weighed every twenty minutes. After the samples were dried to the xerogel form, they were inserted into a water bath, where they were kept until they reached their maximum water absorption capacity. The degree of water absorption , which was an essential parameter for ongoing frequency sweep tests, strain sweep tests were conducted first within the amplitude strain range of 0.01–1000% under a constant frequency of oscillation of 1 Hz in at least two repetitions, using a freshly loaded sample for each test. From these measurements, a strain of 0.1% was chosen as a suitable value of deformation for ongoing frequency tests, because this strain value lays within the LVR for all fresh and rehydrated samples. The range of oscillating frequencies for the frequency sweep tests was set to 0.01–100 Hz. Like the former strain sweep tests, the frequency sweep tests were also conducted in at least two repetitions. A summary of settings for both rheology tests is presented in Routine techniques that are usable for the characterization of the internal structures of many materials  have some limitations in the study of hydrogels. One of the most limiting factors is that the structures of hydrogels are mostly studied in a dried state. The internal structures of a hydrogel in the presence of water and in the absence of water must certainly differ. Moreover, the preparation of the hydrogel in its dried state is also critical because the dispersion medium (water) must be removed (mostly by evaporation or by sublimation if lyophilization is used). Unfortunately, both of these processes (evaporation as well as sublimation) have a significant impact on the final xerogel morphology. Simply, the fragile internal structure of the hydrogel may be critically damaged by the removal of the dispersion medium. Thus, such a resulting structure  revealed by scanning electron microscopy often has low informative value with respect to the internal structure of the hydrogel in its swollen state. Therefore, an alternative way to determine the pore size  must be found. An interesting solution to this problem is offered by the rheological characterization of the hydrogel, which involves the calculation of the mesh size.G and relaxation time λ) from the frequency sweep oscillation measurements in accordance with the Maxwell model [Mesh size, as one of the most critical parameters in hydrogel characterization, was calculated by means of relaxation spectra (relaxation moduli ll model . The freρx represents the crosslinking density (mol·m−3)) and provides information on the density of the junction in the swollen hydrogel form. G (Pa) is the sum of 4 relaxation moduli, R (J·mol−1·K−1) represents the universal gas constant, and T is the thermodynamic temperature in Kelvins.Typical relaxation spectra can be found in the ξ is the mesh size (unit: m) and NA represents Avogadro’s number.If all criteria are met , finallSince the structure affects properties that are crucial for hydrogel applications, determining the hydrogel morphology is one of the most important characterizations. There are many direct (microscopy) and indirect (scattering-based) methods to characterize hydrogel morphology . SeveralFor the characterization of dry samples, the specific surface area (the Brunauer–Emmett–Teller (BET) approach) is typically determined. The specific surface area is not suitable for characterizing hydrogels because of the already mentioned artifacts that appear during the preparation of dried samples. However, it can be used, for example, for the characterization of materials used in a dried state and that can form hydrogels (adsorbent) , or for The structure of the xerogels was studied in this work. Specifically, scanning electron microscopy and gas sorption were chosen as suitable techniques for determining the internal architecture of xerogels. Since the mechanical properties were studied for hydrogels right after preparation and also for swollen hydrogels after dehydration, it seemed convenient to investigate the structural properties of the hydrogels in these forms. Since this form is a dry form, it was possible to avoid deformation of the structure caused by the preparation of hydrogels for scanning electron microscopy.To determine changes in hydrogel structure, xerogels of all prepared samples were subjected to direct visualization using scanning electron microscopy. The samples were dried in a laboratory dryer at 40 °C. A few small specimens were taken from each studied sample to maintain objective observation. These specimens were subsequently gold-coated in a sputtering device (POLARON) and investigated using a ZEISS EVO LS 10 scanning electron microscope.Both the surface morphologies and sectional images of samples were recorded. Observations were realized in secondary electron (SE) mode and the accelerating voltage was set to 5 kV to avoid charging of the samples.0. The obtained data were processed by NovaWin software and specific surface area was calculated by the multi-point BET method.A NOVA 2200e high-speed gas sorption analyzer (Quantachrome Instruments) was used to determine the specific surface area. The samples were weighed into a measuring cell (0.05–0.1 g). The measuring cell was placed in a degassing station, where the degassing process was carried out at 75 °C for 20 h. After cooling, the degassed sample was weighed to four decimal places. The samples were placed in a measuring station. The adsorption and desorption isotherms were measured under liquid nitrogen (77 K) from 0.05–0.95 of the relative pressure P/P


Additively manufactured composite specimens exhibit anisotropic properties, meaning that the elastic response changes with respect to orientation. Both in-plane and out-of-plane mechanical properties are important for designing purpose. Recent studies have characterised the in-plane performance. In this study, however, through-thickness tensile strength of 3D polymer composites were determined by printing of continuous carbon fibre reinforced thermoplastic polyamide-based composite, manufactured using a Markforged Two 3D printer. This paper discusses sample fabrication and geometry, adhesive used, and testing procedure. Test standards used to determine out-of-plane properties are tedious as most of the premature failures occur between the specimens and the tabs. Two types of samples were printed according to ASTM flatwise tension standard and the results were compared to determine the geometry effect on the interlaminar strength. This test method consists of subjecting the printed sample to a uniaxial tensile force normal to the plane. With this method, the acceptable failure modes for tensile strength must be internal to the structure, not between the sample and the end tabs. Micro-computed tomography (µCT) was carried out to observe the porosity. Surface behaviour was studied using scanning electron microscopy (SEM) to see the voids and the distribution of the fibres in the samples. The results showed consistent values for tensile strength and elastic modulus for Araldite glue after initial trials (with some other adhesives) to determine a suitable choice of adhesive for bonding the samples with the tabs. Circular specimens have higher tensile strength and elastic modulus as compared to rectangular specimens. Fused deposition modelling (FDM) is one of the most widely used 3D printing technologies for fabricating complex geometry with the capacity to compete in strength and stiffness with conventional processing techniques . In the Fibre-based polymer composites are increasing for several applications in the transportation industry, construction industry, and in the medical field, as they are lightweight with favourable strength and stiffness properties. Normally, the fibres in composite materials are positioned so that it can resist the applied loads in the printing direction. Normal stresses developed from axial loads are resisted by the resin and fibre. Several researchers studied the in-plane mechanical properties  using continuous reinforced polymer composites . In-planZhang et al.  studied This paper focuses on the fabrication of the specimens and the testing procedure that are employed in conducting the experimental investigation to evaluate the elastic modulus and the through-thickness strength (interlaminar) of the parts manufactured using FDM technique. AM is the latest manufacturing process added which has its own design opportunities and challenges. Therefore, it is necessary to evaluate the properties by applying standard test methods and procedure. Information regarding the equipment used, test fixture, and the bonding of the specimen to the tabs are also discussed. The main aim of this work is to evaluate the properties in the z-direction; a lot of data is available on the in-plane mechanical properties (x and y-direction) of 3D printed polymer composites, but further work is required to determine and understand the weaker out-of-plane response to assist designers in modelling using numerical tools and allow for safe component design.Test samples were fabricated according to ASTM standards  for throIn polymer-based composites fabricated using FDM technique, fibre reinforcement happens in a plain-wise fashion, that is, a layer-by-layer format, which exhibits anisotropic properties . This caThe through-thickness test of the 3D printed samples were carried out using a tensile machine with a test speed of 0.5 mm/min. Two strain gages were mounted to the centre of the specimens to measure average strain. Using the elastic region of the stress-strain curve, the elastic modulus was calculated. Several factors that influenced the through-thickness tests were bonding (facing) material, adhesive type, samples fabrication technique, specimen geometry, specimen alignment, and speed of testing. The Araldite two-part epoxy was considered suitable for this test type as it provides high mechanical properties along with excellent environmental resistance. Prior to bonding, the composite was dried, and the bonding surfaces wiped with a suitable cleaning solvent that did not affect the surfaces physically or chemically. Initially, some trial tests were conducted using different speeds until the ultimate strength of the material and the compliance of the system were known. The standard head displacement rate was 0.50 mm/min, which was kept constant for all the test specimens. Through-thickness (flatwise) tests were carried out to determine the strength for each type of configuration. Initially the value of stress achieved was 7 MPa for the non-fracture samples as the failure did not in the gage length of the specimen. Several factors could be involved in the test failure due to the debonding between the sample and the steel tabs. Firstly, it might be because of the adhesive, as it might not be cured fully before testing, or it had not reached the ultimate strength. Secondly, the surface of the specimens might have not been roughened enough to provide a better surface area for bonding. After this, the adhesive was changed and the surface was roughened more deeply, as nylon is notoriously difficult to bond due to presence of slip additives within the material, which make it difficult for the adhesive to bond properly the nylon surface, thus causing premature failure (delamination) in the glue and nylon surface. For better adhesion, the outer layers of nylon were removed, and the tabs were bonded to the fibre-reinforced layers which gave more rough area for bonding, and it caused the failure in the desired area. Environmental conditions were kept in mind, as they might affect the tabs chemically if not selected properly. As in these tests, the samples were subjected to uniaxial tensile force that was normal to the plane of the composite material.  samples , four st samples  were mouPorosity affects the strength and stiffness of the fibre composite material, but it is difficult to measure its contribution due to other parameter effects, like printing speed, infill density and pattern, and temperature, which can be measured or characterized using experimental techniques. The increase in the porosity content in the AM technique is directly proportional to the size of the reinforcement. However, the increase of fibre content improves the dimensional stability of the printed part through minimizing shrinkage and distortion .Another standard that is used to determine the strength and modulus is ASTM C 297. Strain gauges were bonded to the specimen to measure the elastic modulus correctly. Thermogravimetric analysis (TGA) was carried out using Q600  as shown in Micro-computed tomography (CT) was carried out to observe the fibre orientation and level of porosity inside the specimen using a SkyScan 1275 Bruker machine. The images were taken keeping the X-ray detector at 3 MP (1944 × 1536) with a 75 µm pixel size and a 20–100 kV range. A 10 W X-ray source with a <5 µm spot size at 4 W target power was used. Data Viewer, an imaging software of Bruker, was used to process the images. From the CT analysis of the single fibre, as shown in Through-thickness tests were carried out to determine the mechanical properties of 3D printed samples after a successful set of experiments to measure both elastic modulus and strength. A maximum value for tensile strength of 12 MPa and an elastic modulus value of 2.94 GPa was measured for the circular specimen; for the rectangular specimen, a tensile strength of 7.8 MPa and an elastic modulus of 2.8 GPa were obtained. The maximum value of tensile strength that is obtained by other researchers is 7 MPa, while the elastic modulus has not been calculated so far, to the best of the author’s knowledge. From the microscopic examination, it was observed that some areas are resin rich, and some fibres were missing, which reduced the overall strength of the composite samples.One of the conclusions that was noticed from these experiments is that the geometry had an effect and correlation on the strength in the z-direction. More work needs to be done in the future to minimize the printing defects, as well as to increase the amount of fibre contents. A poor fibre matrix interface was among the main causes of reduced mechanical performance. To have more accuracy for elastic modulus, strain gages must be mounted on each side of the specimen.