The most important technique for exposing early-stage breast cancer is terahertz imaging. It aids in lowering the number of breast cancer-related fatalities and enhancing the quality of life. An essential component of developing the THz imaging system for high-quality photos is choosing the right sensor. In this article, a wideband antenna for microwave imaging of breast tissue with an operating frequency of 30 GHz (107 GHz to 137 GHz) is constructed and analyzed. An aperture-coupled antenna with an optimized ground aperture is proposed and analyzed, which made it possible to obtain better and consistent impedance matching in the wideband spectrum. The variation of backscattered signal energy in body tissue is assessed with healthy breast tissue and in the presence of malignant cells. A significant difference in energy scattering is observed for both situations. The suggested antenna’s linear and stable time domain characteristics make it an appropriate component for THz imaging technology. Terahertz imaging is growing rapidly as a noninvasive technology for the detection of unhealthy and cancerous cells in breast tissue. THz imaging is a non-ionizing and low-power technology, making it biologically hazardous and free for in vitro and in vivo operations. One THz frequency requires an energy level of about 4.14 meV, whereas the minimum X-ray energy level is 0.12 Kev. Therefore, THz imaging is safe and reduces the ionization effects as compared to conventional methodologies. This non-ionizing behavior is an important and critical feature that advances THz systems in healthcare applications [In THz imaging systems, variations in water contents of healthy and unhealthy cells are analyzed to diagnose the existence of specific cancer cells and to measure the depth, location, and severity of damaged tissues. Unhealthy tumors and cancer cells have high water levels, and THz photons are highly sensitive to water absorption. It helps in the characterization of THz radiation in biological and medical imaging . Pulsed Antennas with small planar geometry, high directivity, broad bandwidth, and on-chip fabrication capabilities have the potential to be a promising component of the imaging system as a THz radiation source. Photoconductive antennas (PCA) are the most commonly used antennas for biomedical imaging in the THz range. A high-resistance thin film of semiconductor materials such as gallium arsenide (GaAs) or zinc oxide (Zno) with two metallic contacts is used to design the PCA ,8. MicroArraying of planar and patch antennas is used to improve the gain ,16. Howe(1)It features a planar and straightforward structure that can be easily traced onto non-planar breast tissue.(2)Its wider impedance bandwidth allows for radiation penetration into tissue at different depths.(3)It offers higher gain.(4)It displays good sensitivity to unhealthy tissue.The present study introduces and investigates an aperture-coupled THz antenna with a polyamide substrate for the purpose of breast cancer detection in medical imaging. Drawing from the relevant literature, THz imaging is based on the principle that changing the electrical properties of bodily tissue leads to alterations in the backscattered parameters. Cancerous tissue, possessing a higher dielectric constant than normal breast tissue due to its high water content, is the focus of an investigation, with the deviation of the S11 signal being analyzed. The antenna developed in this study contributes to the field in the following ways:The paper is divided into the following sections: the introduction and literature review are found in r) of 3.5. The configuration of the designed THz antenna is represented in An aperture-coupled THz antenna is operated over a wide-frequency spectrum of 30 GHz (from 0.107 THz to 0.137 THz). In the aperture-coupled structure, a metallic ground plane is sandwiched between the two dielectric substrates. The radiating element is designed on the upper side of the first substrate layer, and the microstrip feed line (to excite the radiator) is designed on the lower surface of the second substrate layer. To generate the coupling effect between microstrip line and radiating element, a rectangular slot (working as the aperture) is engraved in the ground. The antenna is designed using a low-permittivity dielectric substrate polyamide of permittivity , 16 mm of a fat layer, a tumor radius of 2 mm, εr = 67, and conductivity of 47 S/m [The definitive objective of the proposed structure is to extricate the unhealthy tissues inside a breast by detecting the dissimilarity in the |S11| signal  of the antenna between healthy and unhealthy tissues. In order to accomplish this, an equivalent breast tissue phantom in a spherical shape with a 20 mm radius  is considered. It consists of a 2 mm skin layer  frequencies. This is accomplished by utilizing a rectangular patch as the main element of the antenna, along with an optimized coupling slot that facilitates the desired performance. To assess the antenna’s effectiveness in the terahertz (THz) range, an equivalent tissue phantom is used as the testing medium. The tissue phantom serves as a representative model for the evaluation of the antenna’s performance. When the antenna is positioned on heterogeneous tissue, it exhibits a notable ability to differentiate the backscattered signal. This means that the antenna is capable of distinguishing between different signals reflected by various types of tissues. It is worth noting that the electrical characteristics of the tissue tend to change with the variations in the magnitude of the backscattered signal. This property can be effectively utilized to obtain imaging information. By analyzing the variations in the |S11| parameter, which quantifies the reflection coefficient of the antenna, significant differences can be observed. Specifically, in a face-to-face scenario at 125 GHz, there is a maximum deviation of 5 dB, while, in a side-by-side scenario at the same frequency, the maximum deviation is 7 dB. These deviations indicate the potential for acquiring valuable information through the backscattered signal. Moreover, the distribution of the electric field generated by the antenna is assessed to evaluate the presence of a nonuniform power distribution. This analysis helps to determine if there are any areas where the power concentration is significantly imbalanced, which could affect the antenna’s overall performance. Additionally, the linearity of the timing signals is evaluated using the received and transmitted pulses. By examining the timing characteristics of these pulses, it is possible to determine whether the antenna operates linearly and accurately in terms of timing. This assessment is crucial to ensure reliable and precise signal transmission and reception. In summary, the configuration of the aperture-coupled patch antenna is carefully designed to achieve a broad impedance bandwidth and stable performance at UWB frequencies. By utilizing an equivalent tissue phantom, the antenna’s performance in the THz range was evaluated, particularly in terms of its ability to differentiate backscattered signals. The variations in the electrical characteristics of the tissue, caused by changes in the backscattered signal magnitude, can be exploited for imaging purposes. The analysis of the |S11| parameter reveals significant deviations, allowing for valuable information extraction. The distribution of the electric field and the linearity of timing signals were also examined to assess the power distribution and signal timing accuracy, respectively.


Introduction: Triply periodic minimal surface (TPMS) is widely used in the design of bone scaffolds due to its structural advantages. However, the current approach to designing bone scaffolds using TPMS structures is limited to a forward process from microstructure to mechanical properties. Developing an inverse bone scaffold design method based on the mechanical properties of bone structures is crucial.Methods: Using the machine learning and genetic algorithm, a new inverse design model was proposed in this research. The anisotropy of bone was matched by changing the number of cells in different directions. The finite element (FE) method was used to calculate the TPMS configuration and generate a back propagation neural network (BPNN) data set. Neural networks were used to establish the relationship between microstructural parameters and the elastic matrix of bone. This relationship was then used with regenerative genetic algorithm (RGA) in inverse design.Results: The accuracy of the BPNN-RGA model was confirmed by comparing the elasticity matrix of the inverse-designed structure with that of the actual bone. The results indicated that the average error was below 3.00% for three mechanical performance parameters as design targets, and approximately 5.00% for six design targets.Discussion: The present study demonstrated the potential of combining machine learning with traditional optimization method to inversely design anisotropic TPMS bone scaffolds with target mechanical properties. The BPNN-RGA model achieves higher design efficiency, compared to traditional optimization methods. The entire design process is easily controlled. Bone is a crucial part of the human body, serving various functions such as body support, protection of internal organs, and mineral storage. With the increasing aging population, the number of people suffering from joint diseases is also rising, leading to a greater demand for external repair techniques for bone defects . CurrentThe current research on TPMS bone scaffold mainly focuses on optimizing its structure to achieve the target performance. For example, , systemaTo realize the inverse design, the machine learning (ML) based method is resorted due to its low computational cost, high adaptability to various physical problems, and good independence from physical models. ML-based method is a data-driven method, and its effectiveness depends on the amount of prepared data and the algorithm employed . BesidesThe present study aimed to inversely design complex bone scaffolds using anisotropic TPMS structures. The target for inverse design was the partial elasticity matrix of bone. A mapping relationship between structural parameters and mechanical properties using the back propagation neural network (BPNN) neural network was established in our design method. Then, a regenerative genetic algorithm (RGA) was embedded in machine learning for inverse search to obtain the desired structure . Finallyx and z directions is kept “2”). A more complicated structural design can be made if the numbers in the three directions are different.Arrangement anisotropy refers to the fact that the number of TPMS unit cells arranged in each direction is different while the length of the structure in each direction remains constant. The whole structure had different mechanical properties, such as Young’s modulus, in different directions. As shown in Anisotropic TPMS structures can be generated by controlling the parameters of the surface equations. The anisotropic Primitive scaffold was characterized using the following equation:x, y, and z directions, respectively, as detailed in the literature  images . The 2D terature . The stiterature . The anaterature . This feThe ML method is largely database dependent, so enough data are required to perform the inverse design. To generate the data set for ML to predict the equivalent stiffness matrix of TPMS, we obtained the 3D scatter plot from the TPMS surface equation and used the scatter plot to generate the unit node information. The range of variation of the number of structural arrangements is set to 3–8. The range of variation of porosity is set to 50%–75%. The element node information was imported into ANSYS  to generate the FE model. Moreover, FE calculations were performed according to different boundary conditions. TPMS surface parameters and the results of FE calculation were used as the training set of neural networks.As shown in the was 0.30 . It shouwas 0.30 .X and returns the hypothesis value of the output vector ML can be used to quickly predict problems that are previously difficult to solve . The BPN 1 and 4 .A typical BPNN is shown in Through two hidden layers, this approximation is mapped into the output variable Two neural networks, BPNN1 and BPNN2, were trained after determining the optimal structure of the neural network. The BPNN1 was used to train the mapping relationship between the structure and the compressive modulus, and BPNN2 was used to train the mapping relationship between the structure and the shear modulus.The RGA was employed to search for the TPMS configuration, of which the equivalent stiffness matrix was closest to the objective one, as illustrated in TPMS structures with different porosities, arrangement numbers, and cell types were generated as the initial population. The first step in the genetic algorithm was to evaluate the fitness of the individuals in the population. Individuals with lower fitness scores were more likely to undergo cross-mutation, while those with higher fitness scores may also undergo cross-mutation but with a lower probability. After completing the mutations and crossovers, the parents and children were sorted together. The top 2,000 individuals with the highest fitness were selected as the new population, and the next round of mutation and crossover sorting was carried out. The best-adapted individual was recorded each time, and if the error did not decrease after 20 consecutive iterations, it was considered that the best individual had been found. At this point, the outputs were the structural design parameters.We generated 8,000 TPMS configurations and computed their equivalent stiffness matrixes using the FE method, among which 7,000 configurations were used in the training set and 1,000 configurations in the test set. Once the database was established, it was crucial to determine the optimal BPNN structure, including the number of hidden layers and neurons in each layer. The quality of the neural network structure was evaluated based on the mean absolute percentage error (MAPE). The MAPE for the stiffness of each architecture is shown in The convergent behavior of the selected BPNN architecture is shown in The stiffness matrix of porous bone was calculated from CT images. However, due to the anisotropy of porous bone, there were still nine different design goals, even after omitting items close to zero. The inverse design of the spine bone focused on the structure’s performance in compression rather than shear. Therefore, the compressive moduli in three directions (The corresponding TPMS structure was Gyroid, the structural porosity was 67%, and the numbers in the The results of the simulation demonstrated that the BPNN-RGA method could achieve the inverse design with a maximum absolute error of 0.03 GPa and a maximum relative error percentage of 7.00%, when the design targets were the compression and the shear modulus in three directions. The secondary design target had the most significant error percentage due to its small magnitude.When the design targets were reduced to three, such as the compressive modulus in three directions, the relative error percentage became almost negligible . TherefoThe data presented in this article was obtained through the analysis of porous bone CT images. While its validity had been confirmed , it shoux, y, and z directions were 3, 6, and 6, respectively. The maximum error of the structure is less than 3.00%. In addition to the inverse design of porous bone with a small modulus, low porosity bone with a relatively large modulus was also inverse designed. Design goals for low porosity bone from existing literature (As shown in terature .As shown in In the present study, a BPNN-RGA model was developed to design complex bone scaffolds, and a simulation error analysis was performed to verify the accuracy of the model. The present study indicates that complex bone scaffolds can be designed efficiently and accurately using the BPNN-RGA model.In some ways, the design method based on neural networks was more efficient than other optimization methods. A comparative discussion with other bone scaffold design methods is given below: The randomization method based on computer aided design has been proven effective in simulating real bone through the randomization process . HoweverExcept randomization, comparison with topology optimization methods is also a focus . Utilizein vivo involves the recruitment and penetration of cells from the surrounding bone tissue and vascularization (In addition, the selectivity of the design results are also the highlights of the BPNN-RGA model. When the design error was set to approximately 5.00%, we found various unit cells suitable for inverse design with the same design goals, which provided us with more options to select. The difference between the different types of TPMS structures, if they all met the design objectives, was the porosity. A successful implant must meet mechanical requirements that match the surrounding tissue to reduce stress shielding and prevent mechanical failure. However, except these considerations, it is also important to consider cell attachment and growth, as well as the transportation of nutrients and metabolic wastes for optimal biocompatibility . Bone rerization . The porrization .The limitation of the BPNN-RGA model is that the design field is limited to four TPMS structures. When there are more than six targets in the inverse design, some inaccuracies may occur due to the design field. To address this issue, one of the methods is to increase the variety of TPMS structures, and the other method is to increase the number of single-cell arrays. Furthermore, the efficiency of the BPNN-RGA model is reflected in its repeated use. The time cost of building the model may be higher if it is used only once. This is also a common feature of all machine learning models.In this paper, a new inverse design method BPNN-RGA was proposed to inversely design anisotropic bone scaffolds. In this method, anisotropy was introduced into the arrangement of bone scaffolds based on traditional TPMS structures, and the bone stiffness matrix calculated from CT images was used as the inverse design target. The results of the FE calculation were used in neural network training to find the mapping relationship between the structural parameters and the elastic modulus. RGA was used in inverse design to find the structure meeting the target modulus. Multiple bone data were used to verify the universality and accuracy of the BPNN-RGA method. The results showed that the average error was less than 3.00%, when the design targets was three mechanical performance parameters and about 5.00% when the design targets was six. Compared with the traditional optimization method, the proposed BPNN-RGA model achieves high design efficiency. Moreover, the results of the design have the characteristics of stability and selectivity.


Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques. With the continuous advancement of smart vehicles, advanced driving assistance systems (ADASs) are being utilized more frequently ,2,3. AmoACC systems typically employ hierarchical control, which is composed of three layers: perception, decision and execution ,7. The pThe operation of Adaptive Cruise Control (ACC) is influenced by various factors such as driving scenarios, engine characteristics and braking system characteristics. It can be reduced to a nonlinear optimal tracking control problem with external disturbance. In the development of the ACC system’s decision layer, several methods are applied, such as PID control, MPC control and fuzzy control. Among them, PID control is favored by the industry due to its low hardware configuration requirements and good control robustness when encountering interference. M. Canale et al.  used a rCurrently, two methods are primarily used for the adaptive cruise execution layer control. One method involves establishing a high-precision longitudinal dynamic model, which requires obtaining several accurate vehicle parameters for dynamic modeling ,19. Howe(1)In the dynamic normal wheel load observer, Random Forest (RF) is adopted for feature recognition and dimensionality reduction and the processed data are input to the fully connected neural network (FCNN) for estimating the dynamic normal wheel load of the vehicle, which provides a new idea for estimating the vehicle’s dynamic parameters.(2)Fuzzy rules are combined with model predictive control methods to overcome the poor adaptability of model predictive control methods to nonlinear problems. The control parameters can be adjusted according to fuzzy rules to adapt to dynamic changes, thereby enhancing the controller’s robustness.(3)In the executive layer’s control, the integral-separate PID method is used to avoid oscillation caused by the integral action and enhance the system’s response speed, improving the system’s dynamic performance. The braking safety of the vehicle is further increased by designing a rule-based ABS control method.In conclusion, this paper proposes a Fuzzy Model Predictive Control strategy for the Adaptive Cruise Control system based on a dynamic normal wheel load observer. In this control strategy, the observer provides accurate reference for brake force distribution, and the Fuzzy Model Predictive Controller ensures the ACC system’s balance between tracking performance and comfort and adapts to changing driving conditions, while the executive controller ensures the system’s response speed and execution accuracy. Finally, the joint simulation verifies the effectiveness of the observer and ACC system’s control. The main contributions of this paper are as follows:The rest of the paper is organized as follows. This paper focuses on the rear-mounted rear-drive intercity bus, as illustrated in The vehicle parameters are shown in m denotes the vehicle mass, v denotes the vehicle longitudinal velocity, r denotes the wheel rolling radius, A denotes the windward area, f denotes the road rolling resistance co-efficient, During vehicle operation, it is necessary to balance the driving torque provided by the vehicle with various resistances, including air resistance, slope resistance and acceleration resistance. The equation expressing the driving force is presented in Equation .(1)TtqiWhen the vehicle is in a driving state, based on the current vehicle’s required acceleration, Equation  can be tEstablishing an accurate engine model is challenging due to the complexity of its physical characteristics and the numerous factors that affect its working characteristics. In this paper, based on experimental data from a diesel engine, the relationships between engine speed, torque and throttle opening were established, as During the driving process of the vehicle, Equation  can be uEquation .(3)αdesThe brake system is comprised of a compressor that serves as a high-pressure air source, a storage cylinder, a double channel axle modulator (DAM) for regulating brake pressure, a brake air chamber, brake calipers and brake discs that convert pressure into brake torque. Pneumatic disc brakes offer several advantages such as a simple structure, lightweight, low noise and fast heat dissipation, which can contribute to improving vehicle active safety. The braking torque on a vehicle equipped with this brake can be computed using Equation  as folloTb=2PbAbηEquation .(5)Tb=KCorresponding to the driving state, the braking torque of the vehicle can be expressed as Equation .(6)Tb=δGiven the expected brake torque, the corresponding expected brake pressure value Equation .(7)βdesThe tire is the sole component of a vehicle that makes contact with the ground and transmits both force and torque. Longitudinal force, lateral force and aligning torque of a vehicle arise from the interaction between the tire and the ground. Therefore, tire model accuracy is essential for creating an accurate vehicle model. Currently, tire models fall into three categories: theoretical, semi-empirical and empirical models. For this paper, the Dugoff tire model was selected to calculate tire force due to its applicability and meeting the required state estimation. The Dugoff tire model equation is shown as Equations –11).(8.(811).The slip rate represents the proportion of sliding to rolling in the vehicle’s overall motion. During braking, the rolling component of the wheel decreases while the sliding component increases as the braking intensity increases. The formula for calculating the longitudinal slip rate is presented in Equation .(12)Sr=The ACC system implements a hierarchical control strategy, which consists of three main layers: perception, decision and execution. Vehicles equipped with ACC systems, during their driving process, obtain information from the perception layer through vehicle-mounted sensors and state observers. After a determination of the system’s working mode, the vehicle is determined to be in either constant speed cruise mode or follow mode. The PID control method is used for constant speed cruise mode. For follow mode, first the weighting factors applicable to the current scenario are obtained based on preset fuzzy rules and applied to the optimization solution of the objective function. Then, the control quantities obtained from the MPC controller are output and sent to the execution layer, finally achieving the vehicle’s Adaptive Cruise Control function.The dynamic characteristics of vehicles and the mechanical behavior of their tires exhibit strong nonlinearity. The traditional model-based frameworks typically linearize the state equations to compute tire forces, which provide accurate results when the tire is in the linear region, but they are inefficient for nonlinear conditions ,22. ConsA is the passband gain of the filter and B is the bandwidth ratio of the bandpass.The output data of vehicle sensors may contain a certain level of noise due to the variation in their sampling frequency and the vibration that occurs during driving. Therefore, it is essential to filter, resample and interpolate the data collected by the vehicle for accurate analysis. One approach to filtering anomalous data points is to use the band-pass filter technique. This method involves combining a high-pass filter and a low-pass filter either in series or parallel, resulting in a filter that permits only signals within a specific frequency range while suppressing signals beyond the cutoff frequency. The transfer function of this filter can be expressed using Equation .(13)h(sThe discretized difference equation for the bandpass filter is shown in Equation .(14)yn=i-th interval, which can be obtained by solving a system of linear equations.To ensure the continuity and smoothness of the data, the method of cubic spline interpolation is used to achieve the unification of different signals in the time and frequency domains, as shown in Equation .(15)Sx=The coefficient matrix of the system of linear equations is a tridiagonal matrix that can be solved efficiently via the catch-up method.The dataset used for neural network models often involves a considerable number of features, leading to increased complexity and difficulty in optimizing the model. To resolve this issue, the random forest technique can be implemented to remove irrelevant features from the dataset and improve the performance of the model by efficiently selecting critical features as input for the neural network.j is expressed in Equation  score for each feature. During each tree’s training process, a node within the tree randomly selects a subset of samples from its related parent node, forming out-of-bag (OOB) samples. The OOB importance value calculates the accuracy of the model’s prediction performance for each sample. Using the OOB importance scores for individual features, it becomes feasible to select substantial inputs for the neural network. The calculation of the OOB importance score for feature Equation .(16)OOBEquation .(17)OOBFinally, the features are ranked based on their OOB importance values and standard deviation values and the subset of features with high importance and good stability is selected as the final feature subset.b-th decision tree; b-th decision tree.In the RF feature screening in this paper, the results of the decision tree output were obtained using the mean method and the regression characteristics were obtained as shown in Equation .(18)VFuRandom forest feature selection provides the dataset for regression analysis of dynamic vertical wheel load parameters using a fully connected neural network (FCNN). The structure of the network, as displayed in x is the original data, The dataset is randomly partitioned into two groups consisting of a training set and a validation set. Further, to enhance training stability and rate, the training data require normalization. In this paper, Min-Max Scaling normalization, shown in Equation , is utilThe backpropagation algorithm updates the network parameters during training by using the training set data. The network’s regression performance is evaluated by utilizing the testing set after reducing the training error onto convergence. The parameter regression formula based on the fully connected neural network is demonstrated in Equation .(20)y=fx is the input feature vector, y is the regression output value, f is the ReLU activation function. During the training process, the gradient descent algorithm is used to continuously optimize the parameters In Equation , x is thIt should be noted that this method of observing dynamic vertical wheel loads is not only applicable to highway vehicles, but also to industrial vehicles with relatively simple driving scenarios, such as forklifts. Although these vehicles have lower driving speeds, they can still exhibit changes in wheel normal loads. The observed results of dynamic vertical wheel loads can be applied to the stability issues of forklifts, such as warning of vehicle tip-over ,24.The ACC system comprises a decision layer, which serves as the system’s nucleus. This layer facilitates both intelligent perception and response to complicated traffic conditions and dynamic information throughout car driving. The decision layer’s functionality includes selecting vehicle control modes and outputting expected acceleration levels to the execution layer. It derives this based on the kinematic and dynamic information received from the perception layer. Thus, it reduces the driver’s workload while increasing driving safety and comfort.The operation of the Adaptive Cruise Control (ACC) system involves an initial determination of the current mode of the system. If there are no vehicles detected within the radar range or if the speed of the target vehicle ahead is higher than the speed set by the host vehicle, it operates in constant speed cruise mode. However, if there is a target vehicle ahead, it switches to follow mode. A detailed illustration of this procedure is provided in The Anti-Windup PID method is used to control the constant speed cruise mode. This involves using the relative speed difference and host vehicle speed as input variables and the desired longitudinal acceleration as the output variable. A saturation function is then applied to the output to limit extreme acceleration values that might otherwise affect ride stability. The primary goal of this function is to maintain vehicle stability and ensure passenger comfort.The implementation of the MPC method for follow mode control necessitates the provision of the state variables needed by the controller through the perception layer. As a result of the vehicle’s inertia, there exists a time lag between the expected acceleration and the current acceleration. To represent the hysteresis properties exhibited by the two accelerations, a primary order inertial link was introduced which is described in Equation .(21)adeIn the calculation of the desired relative spacing arameter ,26,27. TEquation . CombineEquation .(22)th=k.Select real-time relative spacing Equation .(24)xyJ=∑Q and the comfort weight coefficient R will directly affect the performance of the objective function. The following ability refers to the ability of a vehicle equipped with an ACC system to converge to the relative distance error and relative velocity error between itself and the target vehicle, while the comfort weight coefficient affects the control variable u, that is, the host vehicle acceleration. By adjusting Q and R, the system control parameter matching for coping with complicated scenarios can be achieved.From Equation , it can The control matrix constraints of the ACC system are organized as Equation .(29)M¯≤Based on the above analysis, the multi-objective optimized ACC system control algorithm is turned into an online quadratic optimization problem with constraints as shown in Equation .(30)minQ and R, which correspond to the following performance index and comfort performance index, respectively. The comfort performance index weight factor was fixed as Q. The error between the actual and the desired relative spacing Q was utilized as the output variable for fuzzy controller design. Fuzzy linguistic variables were employed to establish the weight factor Q of the followability index, as shown in Equation (The effectiveness of Adaptive Cruise Control (ACC) depends on how well it can navigate the complex traffic environment encountered by a vehicle. To ensure control robustness and scenario adaptability in varying driving scenarios, variable system control parameters for ACC are required. This paper proposes the use of a weight factor for the comprehensive performance index function that is updated in real time based on the vehicle’s driving state. The weight factor comprises two components, Equation .(31)δFtor is the rolling radius of the wheel.The second step is to calculate the braking torque of vehicle demand, as shown in Equation .(33)TtoFinally, the wheel braking force distribution coefficient is calculated, as shown in Equation .(34)λi=Therefore, the braking torque of each wheel can be expressed in the form of Equation .(35)TbiAfter obtaining the acceleration requirements and braking torque requirements from the decision-making level, the integral-separated PID method is used to control the actuator. The control quantity of the output of the integral-separated PID control can be expressed by Equation .(36)u systems. The target vehicle scenario is prevalent in high-speed road sections and suburban areas with fewer traffic lights. In this scenario, the ACC system must regulate the vehicle’s speed to smoothly track the target vehicle ahead. The target vehicle insertion scenario requires the ACC system to adjust the vehicle’s longitudinal speed using control strategies to prevent a collision. In the target vehicle braking scenario, the ACC system must promptly brake to avoid collisions. All of these simulation test scenarios compare the Fuzzy Model Predictive Control (MPC) method proposed in this paper with the conventional MPC method that uses fixed weight factors.The initial conditions of the simulation of the following target vehicle scenario are set to the target vehicle driving with variable speed according to the sinusoidal wave-form, the initial velocity of the ACC vehicle is 75 km/h, the initial relative distance is 55 m and the initial velocity of the target vehicle is 60 km/h. The simulation results under this scenario are shown in Q is stabilized to 1 because the vehicle velocity error and the vehicle distance error gradually converge. According to In the initial stage of the simulation, the actual relative distance is greater than the desired relative distance, but since the velocity of the host vehicle is higher than the target vehicle, it needs to take braking to adjust the velocity of the host vehicle. Therefore, in the first 2 s at the beginning of the simulation, the ACC system focuses more on the followability index, which is reflected in In the simulation of the target vehicle insertion scenario, the initial conditions of the simulation are set as follows: the initial speed of the ACC vehicle is 60 km/h, the original target vehicle is traveling at 70 km/h in front and the initial distance between the two vehicles is 30 m. At 20 s, the vehicle maintains a constant speed of 60 km/h in the next lane, suddenly cuts in and becomes the new target vehicle, at which time the distance between the host vehicle and the target vehicle is 25 m. The simulation results under this condition are shown in In the first 20 s after the simulation starts, both the Fuzzy MPC algorithm and the normal MPC algorithm can follow the vehicle in front well. At the 20 s mark, the actual relative distance is less than the desired relative distance and the speed of the host vehicle is higher than the target vehicle due to the cut-in of the front vehicle, which creates a risk of collision between the two vehicles. From In the simulation of this scenario, the initial conditions of simulation for the ACC vehicle and the target vehicle are set as follows: the initial speed of the ACC vehicle is 65 km/h. The target vehicle travels at a constant speed of 50 km/h and brakes suddenly at the 17th second with an acceleration of 3 s and then maintains a constant speed. The simulation results under this condition are shown in In the beginning, the speed of the host vehicle followed the target vehicle and in the 7th second, the speed of the two vehicles converged and the distance between the two vehicles also converged to the expected value. At 17 s, the target vehicle braked suddenly and the ACC system braked and effectively avoided a rear-end collision, ensuring the safety of the vehicle. From This paper proposes a control strategy for the ACC system of commercial vehicles in actual driving scenarios based on machine learning state observers and Fuzzy MPC, aiming to improve its performance. It consists of a vehicle dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. The design based on the machine learning state observer estimates the dynamic normal wheel load parameters during vehicle travel and applies them to the longitudinal dynamic control of the ACC vehicle, determining the prerequisite for brake force allocation based on the estimation results. This allocation method can increase the braking deceleration by 18% under fixed brake pedal opening. The Fuzzy Model Predictive Controller dynamically adjusts control parameters through fuzzy processing of performance index weight factors to ensure system safety. To verify the proposed strategy, a joint simulation platform was established in the TruckSim-Simulink environment and the system performance was simulated and tested in typical scenarios. The results show that the proposed method improves speed-tracking accuracy  by 41.38% and distance-tracking accuracy by 41.31% in the following target vehicle scenario. In the target vehicle insertion scenario, while improving tracking accuracy, the convergence speed of speed and distance tracking is also increased. It can be proved that compared with previous MPC Adaptive Cruise Control strategies, the ACC system control strategy based on machine learning state observer and Fuzzy MPC can respond to changes in vehicle driving scenarios more quickly and stably. The proposed execution layer control method was verified under simulation conditions on a low-adhesion road, a bisectional road and a split road; the results showed that it can handle changing road conditions well and further improve the safety performance of the ACC system.Future work will focus on two aspects. First, further research will focus on the robustness of the strategy to parameter uncertainties and external disturbances, especially the applicability of the strategy to different vehicle models and its robustness under more detailed driving scenarios (such as vehicle passing over speed bumps). Second, the proposed control strategy will be validated in real-world vehicle tests.


Evidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based on an improved pignistic probability function. Firstly, the improved pignistic probability function could redistribute the probability of multi-subset proposition according to the weight of single subset propositions in a basic probability assignment (BPA), which reduces the computational complexity and information loss in the conversion process. The combination of the Manhattan distance and evidence angle measurements is proposed to extract evidence certainty and obtain mutual support information between each piece of evidence; then, entropy is used to calculate the uncertainty of the evidence and the weighted average method is used to correct and update the original evidence. Finally, the Dempster combination rule is used to fuse the updated evidence. Verified by the analysis results of single-subset proposition and multi-subset proposition highly conflicting evidence examples, compared to the Jousselme distance method, the Lance distance and reliability entropy combination method, and the Jousselme distance and uncertainty measure combination method, our approach achieved better convergence and the average accuracy was improved by 0.51% and 2.43%. As an uncertain reasoning method, evidence theory ,2 needs To address the issue of conflicting evidence fusion, scholars have presented many outstanding studies. When there are conflicts in original evidence, traditional DS theory cannot be applied and it needs to be improved. In recent years, a large number of scholars and experts have improved evidence theory from two aspects. The first is to improve the fusion rules of DS evidence theory. Sun promoted the concept of credibility, believing that the credibility of all evidence is equal, and modified the Dempster rule by weighted summation . Yang esIn this field, domestic and foreign scholars and experts have achieved excellent results in a particular range. However, in the application of single target recognition, there is only a single result, i.e., U be a domain set representing all values of X, and all elements in U are not integrated. Then, U is called the recognition framework of X.Let The research objects in scientific theory compose a nonempty set, which is called a domain.Definition 1.Let U be a recognition framework, then the function satisfies the following conditions:(1) ;(2) .A, A is called a focal element.Then, U, and the focal elements are Hypothesis K is the conflict factor, which reflects the degree of conflict between evidence. Then,N evidence as Define the system identification framework as A is the proposition of original evidence and B is a simple subset proposition in A.In single target recognition, the fusion recognition result often only has a certain target. In the framework of DS evidence theory, when evidence contains multiple subset propositions, the fusion result also contains multiple subset propositions, which increases the computational complexity. This work improves the pignistic probability function to transform the multiple subset propositions into single subset propositions, in which the BPA of multiple subset propositions is distributed according to the weight of single subset propositions. The weight is allocated according to the information of each single subset proposition provided by the evidence itself, which reduces the computational complexity and information lost in the process of transforming the pignistic probability function  from theAfter the pignistic probability function is conserved, the BPA is converted into a single subset BPA The distance between evidence  can effeThe Manhattan distance between two pieces of evidence is calculated as: The distance between evidence is negatively correlated with the support.The calculation of the evidence support of The support degree is obtained based on the Manhattan distance between evidence. The support is normalized to obtain the support coefficient of the evidence The angle between the two pieces of evidence can be used to structure the consistency between the evidence subjects, and the results obtained can be used to measure the similarity between the two evidence subjects. The formula for the evidence angle  is as foThe larger value of The similarity between evidence is measured based on the evidence angle. The similarity is normalized to obtain the similarity coefficient of the evidence In evidence theory, the amount of information content that evidence carries can be measured by information entropy. The higher the information entropy, the more information the evidence carries, and the lower the probability of occurrence in the real world, the higher the uncertainty. The calculation formula for information entropy is as follows:The uncertainty coefficient of each piece of evidence The evidence fusion coefficient integrating the Manhattan distance, the evidence angle, and the reliability entropy is: The fusion coefficient All the initial evidence is replaced with The flow graph of the method proposed in this paper is shown in In the following, we verify the effectiveness of the method through two conflicting examples: those that only contain single-subset propositions and those that contain multiple-subset propositions.An example of single-subset proposition conflicting evidence can be found in reference . An evidThis example is a simple subset proposition, and the conversed BPA is obtained by Formula (1). Apply Formulas (5)–(8) as follows to obtain the evidence support coefficient, the distance matrix, and support matrix as: The support based on the Manhattan distance between evidence is: The evidence support coefficient Apply Formulas (9)–(11) as follows to obtain the the evidence similarity coefficient The evidence similarity coefficient Apply Formulas (12) and (13) as follows to obtain the evidence uncertainty coefficient The support coefficient and similarity coefficient represent the certainty of evidence, and the uncertainty coefficient represents the uncertainty of evidence. The final evidence fusion coefficient is obtained by applying Formulas (14) and (15): Modify the evidence againFormula (16) is applied for fusion four times, and the fusion results are shown in Suppose there is a multi-sensor-based target recognition system, then the recognized targets are According to Formula (3), the conversed BPA is as shown in As calculated by Formulas (4)–(15), the coefficients are shown in We obtained the final evidence of the BPA: Evidence fusion was performed four times by the Dempster rule, and the fusion results are shown in As shown in the figure above, applying the Dempster fusion rule leads to counter-intuitive results.The fusion results of single- and multi-subset conflicting evidence are discussed in this section. Analyzing Analyzing Experiments have shown that the method proposed in this article can effectively extract mutually supportive features between various evidence sources when there are sufficient evidence sources and can achieve good results.In this article, we proposed a novel evidence combination method based on an improved pignistic probability function. Considering evidence characteristics and information richness, this paper proposes a novel method to solve the problem of highly conflicting evidence fusion in DS evidence theory. Through experiments, it has been shown that we have achieved good results in dealing with single target recognition problems and an improved fusion accuracy of the evidence theory framework in target fusion recognition. Evidence theory has a strong ability to handle uncertainty problems. Our next work will further investigate how evidence theory can be extended and applied in the real world.


Composite beam theory was previously developed to establish an analytical solution for determining the transfer length of prestressed fiber-reinforced polymers (FRP) tendons in pretensioned concrete members. In the present study, a novel finite element (FE) modeling approach is proposed to provide further verification of the developed analytical method. The present FE model takes into account the friction coefficients obtained from pull-out tests on the FRP tendons and prestressed concrete members. Convergence analysis of two numerical simulations with different mesh densities is carried out as well. The results demonstrated that the transfer length predicted by the fine FE model with a friction coefficient of  Along with the strikingly rapid development of composite materials in the field of civil engineering, the applications of fiber-reinforced polymers (FRP) in strengthening concrete structures have attracted more and more attention. CFRP has higher mechanical properties, excellent fatigue resistance, corrosion resistance, and creep resistance. BFRP and GFRP have relatively low prices and wide sources. However, their long-term performances in service environments may degrade ,2,3. TheComposite beam theory, proposed by Granholm  in 1949 As an important numerical technique, FEA has been widely used to study the behavior of prestressed concrete beams ,29,30. MOne of the most compelling advantages of FEA over other analytical solutions is that a simulation associated with fewer assumptions may be closer to the corresponding experimental outcomes. In addition, visualizations of the pre- and postprocessing of FEA can help engineers easily find vulnerabilities in the design. Despite some obvious advantages, mesh convergence is a critical issue that must be taken into account in the process of developing FE models. In this work, a comparative study is conducted between numerical simulation with fine and coarse meshes to illustrate the effect of mesh density on convergence. Another noteworthy point is that the concrete model used for numerical simulation is based on the linear elastic assumption. Although the concrete damaged plasticity (CDP) model from Abaqus  has ofteIn order to provide convenient use in engineering practice, the key technical challenge of this study is to develop a general form of the governing equations specifically for FRP-strengthening concrete members in terms of composite beam theory. Taking account of the empirical bond–slip relationship between FRP tendons and the concrete matrix, governing differential equations are derived in terms of the equilibrium of axial force acting on each element as well as the balance of the overall bending moment. Using the FEA commercial software Abaqus , a compaUnderstanding the nature of bond behavior plays a critical role in assessing how the prestress force is transferred from the prestressing FRP tendons to the concrete. A large amount of research ,30,34,35Friction can be defined as a relationship that is responsible for transmitting the shear and normal forces between contacting bodies, i.e., prestressing tendons and the surrounding concrete matrix. According to the commercial FE program Abaqus , frictioterature ,35. Howeterature  reports It is worth mentioning that the current work using FEM to estimate the transfer length is based on the friction coefficient specifically for FRP tendons in pretensioned prestressed concrete members. Previous studies on finite element analysis ,29,30 ofFor another description of the bond-behavior model, the local bond stress–slip relationship al model  developeal model ,37,38,39P) model , the modP) model . The BEPFor the mBEP model, terature ,5,7,23. Thick-wall cylinder theory  depends (1)Linear elastic constitutive behavior and small displacement are applied to each component in the developed analytical models;(2)FRP’s bending resistance is negligible compared to that provided by the concrete member;(3)Slender beams are considered and therefore Euler–Bernoulli beam theory can be applied to each component of a composite structure, and, therefore, the shear deformation is neglected through the cross section of the concrete beam and FRP components, respectively.The following assumptions and limitations are specifically proposed for FRP-strengthening concrete members using the general approach of composite beam theory:A differential element of the composite beam is composed of the concrete and FRP, as illustrated in As mentioned above, the analysis of the behavior of FRP-strengthening concrete members is based on partial composite action. To some extent, this means that relative movement between FRP and the surrounding concrete, namely slip Thus, Equation (9) indicates that the slip In terms of the As a result, the governing equation relevant to the axial force is generated in the form:The known external moment Furthermore, it can be observed that the slip The reason for this change is that the structural behavior is limited to elastic and small displacement, the constitutive relation is assumed to be linear, and each component of the composite beam has the same deflection Substituting Equation (15) into Equation (14) results in the expression for the bending moment as follows:Consequently, plugging 7 s0.337 :(22)s″ −It is critical to estimate the transfer length of prestressed concrete beams, not only because it affects the bending and shear strength of the structure, but also because it is valuable for designers to understand it for structural detailing. The effective prestressing force is transferred from the prestressed tendons to the concrete in the transfer zone, in which the distance is related to the transfer length. The conventional experimental investigation used to measure the transfer length mainly depends on the strain change in the concrete or the prestressed tendons before and after release. The determination of transfer length is implemented in terms of the strain distribution along the length of the beam using the 95% Average Maximum Strain (AMS) method. For the analytical solution, the proposed model relied upon a novel and theoretically pure composite beam theory developed specifically for pretensioned prestressed concrete members with FRP tendons, the details of which were provided in the previous work . Based oI) units :(25)s(x)Transfer length tionship  resultinExperimental studies on the transmission of prestressing force conducted by Nanni et al.  are utilC3D8R with eight-node linear brick is employed for modeling the concrete in Abaqus, which is a reduced integration element with hourglass control. The C3D6 element, which is a six-node linear triangular prism, is used for the FRP tendons. The geometries of these two elements are illustrated in In order to optimize the computing time and the results precision, the mesh around the FRP tendons is very refined, and the mesh density increases as the distance from the midspan increases. The quarter symmetry FE model of a pretensioned concrete beam with boundary conditions is represented in The interaction between the concrete and FRP tendons modeled by FE consists of three parts: tangential behavior, normal behavior, and cohesive behavior. According to the concept of the Coulomb friction model, as previously described, the tangential behavior between two contacting surfaces is defined in accordance with four different friction coefficients In order to investigate the dependence of nonlinear solutions obtained from the proposed FE model on the mesh size, two models with different mesh densities are simulated in Abaqus. By means of the solution technique of full Newton and automatic control of the time increment, the mesh convergence study is performed on the three-dimensional FE model of a pretensioned concrete beam using coarse mesh and fine mesh, respectively. The cross sectional and longitudinal views corresponding to both the fine and coarse models are shown in To further demonstrate the mesh convergence and the feasibility and accuracy of the 3D FEA solution, a comparison is made between the FE results and the strain profile from the experiment . Figure Based on the 95% AMS method, comparisons of transfer length predicted by the present FE models with measured results  are summFor further proving the performance of the application of composite beam theory on predicting the transfer length for prestressed FRP tendons strengthening pretensioned concrete members, the present FE model is used to compare with the previously developed analytical solutions. As can be seen from Since it is not possible to exactly match the values measured from testing, the local bond–slip relationship between the FRP tendons and the concrete is taken into account to predict the transfer length using closed-form solutions from Equation (26) in the analytical model. This results in an error between the predictions and the test values of 7% and 26% for high pretension (100% force release) and low pretension (50% force release), respectively. In this perspective, the accuracy of the transfer length obtained from the theoretical solution in terms of partially composite action is superior to that of the numerical simulation by using a Coulomb friction model based on FEA.In addition, extensive studies ,42 have terature , in whicterature  during tOn the other hand, it is obvious to see that a remarkable increase in interface slip occurs when the value of the friction coefficient decreases as shown in Through normalizing the parameters, the influence of the friction coefficient In analytical solutions using composite beam theory, the BEP expression is chosen as the bond–slip relationship Testing is considered to be the best way to predict a phenomenon and obtain necessary information. However, large-scale testing is time-consuming, expensive, and has many limitations. With this consideration, along with the need to further verify the accuracy and feasibility of the previously developed method, a three-dimensional FE model of pretensioned concrete members with prestressing FRP tendons was developed. Despite the numerous numerical research on reinforced concrete beams strengthened with conventional FRP bars, none of the existing studies considered the effects of friction coefficients on the transfer length. To bridge this gap in the literature, the different friction coefficients between FRP tendons and the surrounding concrete obtained by experimental studies are fully considered in this study. Based on the data reported from the pullout test, fine and coarse FE models were implemented for convergent analysis, respectively.A general form of the governing equations has been presented specifically for FRP-strengthening concrete members in terms of composite beam theory. Associating with the knowledge of the local bond stress–slip relationship Comparisons with the experimental data demonstrate good agreement, which indicates that the proposed FE model with fine mesh is acceptable. The measured transfer length for high pretension agrees with the prediction from the fine FE model with the friction coefficient Although friction plays a key role in the interaction between concrete and prestressed tendons, the slip-prediction comparisons show that if the adhesion and mechanical interlocking are ignored, the bond behavior cannot be accurately evaluated;The transfer-length prediction is strongly dependent on the adopted function form of the bond–slip relationship In addition, the general approach of composite beam theory, as the key innovation of this research, is derived for providing convenient use in engineering practice, specifically for FRP-strengthening concrete members. Lastly, a comparison between the analytical solution and the FE simulation is carried out and discussed. The main accomplishments and conclusions are as follows:Therefore, it is necessary to adopt a new method to describe the bond behavior between concrete and FRP tendons in the future FE simulation. In this process, adhesion, friction, and mechanical interlocking must be fully considered in order to provide more accurate predictions and facilitate engineering applications.


Mold heating is an essential process in plastic injection molding. Raising the temperature of the mold before injecting liquefied plastic can ease the mold-filling process. A cooling channel can be used to transport high-temperature fluids for this purpose, such as hot water or oil. This dual purpose is a cost-effective solution for heating the mold because the target temperature is easily achieved using this method. In addition, a conformal cooling channel (CCC) can provide more efficient mold heating than a straight cooling channel. This study used the response surface methodology to determine the optimum CCC shape for heat distribution in a mold, and the simulation results confirmed its optimization. The average temperature of the mold using a CCC was better than that using a straight cooling channel, and the heat zone was uniform across the mold surface.  Recently, additive manufacturing (AM) has emerged as a promising manufacturing method that, in many cases, could replace traditional manufacturing methods ,2,3. TheThe injection-molding process has been widely used to manufacture plastic products of various shapes and sizes owing to its high manufacturing productivity and low associated costs. This process can be separated into four steps: filling/injection, packing, cooling, and ejection. The cooling step is the most important of these four processes and constitutes the most extensive injection-cycle process. Contrastingly, this step severely affects the mold temperature, directly impacting shrinkage; S.-C. Nian et al.  showed tTo prevent the injection issue as mentioned above, the mold temperature is the important factor. Many methods could be used to raise the mold temperature so the melted plastic could easily fill out the mold and prevent the injection issue. The effect of mold temperature on the filling ability of melted plastic has been studied. By using external induction to raise the mold temperature, the filling percentage of the product increased from 10.2% to 100% when heating the mold with the 5 mm inductor coil in 5 s . The gasAdding to these concerns, the ejection of the part from the mold can only happen when the entire cavity has cooled to below the ejection temperature. Consequently, cooling time hinges on both the highest temperature point within the cavity and its local cooling rate. In general, traditional cooling channel systems (straight cooling channels) are shown to be effective with simple product molds of uniform thickness, but in the case of complex product molds with varying thicknesses, conformal cooling channels (CCCs) are considered as they better conform to the mold surface.Recently, there have been many studies on CCCs, focusing on their effectiveness during the cooling process and the impact this has on product quality. Shaiful et al.  analyzedCasting ,19,20: WMilled groove method ,23,24,25Laminated tooling ,28,29,30Powder-based additive manufacturing ,32,33,34Hybrid additive/subtractive manufacturing ,36,37: TTo address the challenge of manufacturing conformal cooling channels, several solutions ,16,17 haUnleashing creative design possibilities: WAAM facilitates the construction of intricate geometries that traditional methods find challenging or impossible. Once these complex structures are erected, milling steps in to refine and perfect the part. This collaborative effort results in delivering precision and enhancing the quality of the meticulously designed shapes.Material and cost efficiency: WAAM, a form of directed energy deposition (DED) technology, provides stellar material usage efficiency. Since it primarily engages the deposition of wire-fed materials, the resultant waste is negligible, leading to a significant reduction in raw material costs. Further, the subtractive nature of milling is carefully regulated to cut down waste, fostering even greater cost-efficiency.Time-efficient production: The impressive deposition rates of WAAM facilitate a swift production of large components or molds. Subsequently, milling refines these products rapidly and accurately. This amalgamation of processes potentially leads to substantial time savings, especially when compared to the utilization of either technique in isolation.Enhancement of mechanical properties: The layer-by-layer additive procedure inherent to WAAM can culminate in the refinement of mechanical properties, brought about by rapid cooling and grain refinement. This enhancement can be especially observable in the case of alloyed materials.Repair and remanufacturing capabilities: The synergy of WAAM and milling also proves beneficial for mold repair or remanufacturing. It adds material precisely where required and mills it to achieve the perfect, final shape. This strategy prolongs the lifecycle of molds or parts, eliminating the need for a complete overhaul.Hybrid flexibility: The integration of both additive and subtractive methods within a single setup paves the way for remarkable flexibility. By transitioning between processes as required, manufacturers can fine-tune different aspects of production, such as material usage, surface finish, and dimensional accuracy.To address the aforementioned limitations in existing techniques, this research proposes a new method that combines the wire arc additive manufacturing (WAAM) method with conventional milling . This inWhile this method brings about several advantages, it is also critical to consider potential challenges such as managing heat input to prevent warping or distortion, overseeing the transition between additive and subtractive processes, and ensuring the compatibility of equipment.As a solution, additive manufacturing, such as metal three-dimension printing, could create this type of cooling channel. Park and Dang  have devAs previously mentioned, most of the research on CCCs has focused on analyzing the cooling process. However, it is essential to note that mold heating plays a critical role in the injection process. It has been found that higher mold temperatures result in better flow characteristics for melting plastic and filling the mold. In this study, we have used a CCC in the heating process and optimized it by carefully considering the temperature distributions of the mold at the end of both the heating and cooling steps. We utilized the response surface methodology to optimize the cooling channels to achieve this optimization.In previous studies on mold-temperature control for injection molding ,45,46,47In this study, we aimed to optimize the cooling geometry to improve the temperature distribution on the mold face. Thus, only the mold plate was designed, simulated, and subjected to experimentation. We selected one product in the cavity and two cooling channels. The cooling channel design should consider the thickness of the experimental product; therefore, the channel diameter is based on the product thickness. If the channel diameter is d (mm), the distance between the centerline of the two channels should be between 3d and 4d, and the distance from the channel centerline to the mold surface should be between 2d and 3d. Guidelines for the cooling parameters are shown in Hot water was recirculated during the heating process to raise the mold temperature. The water temperature was controlled by a temperature control machine and maintained at 90 °C at the inlet of the cooling channel. At the same time, a manifold collected water at the outlet and recycled it back to the inlet through ducting.For the mold (solid domain), the material used for the cavity was aluminum alloy. The heat-transfer process included conduction from water in the cooling channel to the mold surface. Heat loss occurred due to heat transfer from the mold surface to the surrounding environment through free convection. The ambient temperature was estimated to be 30 °C. The heat transfer schematic is shown in The simulation was based on the experimental setup described. Without loss of generality or oversimplification, the simulation will only focus on the cavity. The simulation used two domains: the solid domain (mold) and two fluid domains (two cooling channels were separated into two domains and used with the same settings). The thermal properties of each material used in the simulation are listed in As mentioned, the temperature of the cavity surface will directly impact part quality and the filling ability of melted plastic; therefore, this surface is observed and discussed in this study. The temperature was taken on the surface of the mold in contact with the part, shown as a green surface in Response surface methodology (RSM) is an effective method of evaluating the influence of experimental variables on the response. The relationship between the variables and the response is expressed as a surface, also known as the “response surface.” This surface can be represented as a curved surface with two variables in three-way space. However, the response surface becomes a hypersurface in multidimensional space with the addition of experimental variables. This method can advantageously represent the relationship between observed variables and the response through a mathematical model, which can optimize the response and determine individual responses. Parameters include a set of variables to achieve the required value. The mathematical model showing variables and reactions used in this method can be expressed as follows:Y: response;In this equation:These components are not required based on the relation between response and variables. Typically, to correctly determine the equation result, the initial model must fully include these components. A mathematical model was derived using experimental results to find the coefficients in which all components were re-evaluated by the statistical method, and statistically insignificant parts were removed. The mathematical model was built this way until only statistically substantial elements remained.To find the regression equation coefficients, the figure of variables and responses throughout each experiment was used. Design of experiments (DOE) is an efficient experimental strategy for determining the relationship between variables and responses. The most widely accepted experimental designs are full and fractional factorial, with the number of levels depending on the relationship between variables and responses. A complete factorial design consists of all possible combinations between each level of the variables. Therefore, the number of experiments observed from a complete factorial design is numerous, especially in the case of many variables. In this study, with the number of variables being seven and the number of levels being three, the number of experiments observed from a complete factorial design could be 37 = 2187. Instead of a complete factorial design, a fractional factorial design could be a compelling DOE for this study. There are two primary types of DOE for response surface designs: central composite design (CCD) and Box–Behnken design ,49,50,51P8) and the average temperatures (P9). The mathematical model only included significant components at 5% . The regression equation obtained from each response is presented below:The experimental data will be imported into Ansys software and simulated to observe the result. In this study, the simulation result and the response differed between the maximum and minimum temperatures  and (3), all the components, including linear, quadratic, and two-way interaction, appeared in the regression model. Through the analysis of the variance table, the p-value of each element is smaller than 0.05, which means all component appearances on the regression equation have statistical significance. The coefficient of determination (R-sq) also has very high values (98.82% for P8 and 99.80% for P9), proving that the regression equation fits the data. The mathematical model can predict the value of response with each deterministic variable, and the regression equation can also be used to optimize the response value.Optimization with a single response value was relatively easy to find. The optimum value of each response was obtained using numerical methods for regression (2) and (3). However, this study aims to find the optimum shape of cooling; with this cooling shape, the temperature could be distributed uniformly on the mold surface. If only one response has been used to find the optimum cooling form, this shape may bring the highest temperature on the mold surface, but the different temperatures may be large and contrary. These numerical results can be seen in Solving this issue, the desirability function approach could be used. This function was developed by Harrington and his partner  and thenid: desirability function of response i;ik: the importance coefficient of response i.In this function:id of response I could be presented as the function below:The desirability of each response received values from 0 to 1. The desirability When the optimum object is at the maximum:When the optimum object is at the minimum:iL: the upper bound value;iU: the lower bound value;iT: the desirability value;iw: weight of each response.In this formula:The value of the weight when describing the expected function will determine the shape of the graph representing this function. When we choose a weight larger than 1, we emphasize the importance of attaining an objective function close to the desired value. Conversely, when the weight is less than 1, we extend the acceptance more when the objective function is far from the desired value. In this study, the P8 (temperature difference) optimized object was depreciation, and the P9 (average temperature) optimized object was maximization. The optimization process is illustrated in the figure. A regression equation based on the CCD method was used to find the optimum point. The optimization process with the desirability function approach could be easily solved using an added tool such as Minitab, and the progress is shown in The optimization plots for each response are shown in The result in The optimum point of each variable obtained from the optimization process was used to modify the shape of the cooling channel and rerun the simulation on Ansys software to verify the values predicted. In addition to the heating process, the simulation analyzed the cooling process. In the cooling process, water at 30 °C was used. A comparison between the prediction and simulation values of the heating process is shown in Two models were manufactured to verify the simulation results for two types of cooling channels (CCC and straight cooling channel). The fabrication of the cavity mold with a linear cooling channel was simple, but the process was more difficult with the CCC mold. In this research, the mold plate with two CCC was manufactured by the WAAD combined with the milling method. The proposed MAG welding system introduces a metal deposition process via gas metal arc welding (GMAW) to manufacture a mold plate featuring two conformal cooling channels. Essentially an additive manufacturing (AM) method, it involves the deposition of a weld bead in a track-by-track and layer-by-layer manner. To facilitate this, a machine setup was constructed by affixing a GMAW torch to a computer numerical control (CNC) machine, as depicted in For the creation of the conformal cooling channel (CCC), two inserts were pre-manufactured, each one possessing a profile that matched the optimized parameters . Throughout the experiment, the CCC mold temperature remained higher than that of the straight cooling channel.The same conditions were used for the cooling process, and the results were similar to the simulation. First, looking at the experimental result in The simulation and experimental results could be compared, as presented in This study determined the effectiveness of cooling channel geometry on the heat transfer process using the response surface methodology of two regression equations, which were modeled for average and varying temperatures with upper and lower bounds. The optimum geometry for the cooling channel was defined using these two equations. The effectiveness of the CCCs was demonstrated through analysis and verified experimentally. The average temperature was consistently higher when the CCC heated the mold. Additionally, the high-temperature heat zone in a mold that used a CCC was more significant than in molds using conventional cooling channels.


A composite optical bench made up of Carbon Fiber Reinforced Polymer (CFRP) skin and aluminum honeycomb has been developed for the Tunable Magnetograph instrument (TuMag) for the SUNRISE III mission within the NASA Long Duration Balloon Program. This optical bench has been designed to meet lightweight and low sensitivity to thermal gradient requirements, resulting in a low Coefficient of Thermal Expansion (CTE). In addition to the flight model, a breadboard model identical to the flight one has been manufactured, including embedded fiber Bragg temperature and strain sensors. The aim of this is to explore if the use of distributed fiber Bragg gratings (FBGs) can provide valuable information for strain and temperature mapping of an optical instrument on board a space mission during its operation as well as its on-ground testing. Furthermore, surface-mounted strain FBG sensors and thermocouples have been installed in the optical bench for intercomparison purposes. This paper presents the results obtained from a thermal vacuum test consisting of three thermal cycles with stabilization steps at 100 °C, 60 °C, 20 °C and −20 °C. Experimental results provide information about how FBG embedded temperature sensors can provide a proper and quick response to the temperature changes of the optical bench and that embedded FBG strain sensors are able to measure micro-deformation induced in a close-to-zero CTE optical bench. Since Kenneth O. Hill et al. reported the first work on photosensitivity in Ge-doped core optical fibers ,2 in 197The TuMag optical bench has been designed in CFRP to meet the following design drivers: lightness and high dimensional stability in the presence of thermal gradients. In the breadboard model, some FBGs have been embedded in the top skin of the optical bench panel. Due to their small size, the FBG sensors present good compatibility with CFRP structures. The optical fiber, in which the FBGs are inscribed, can be routed inside the CFRP skin in such a way that it is imperceptible to the eye and touch. Furthermore, the use of embedded FBGs avoids the need for wires and adhesives on the surface that could interfere with the optical experiments. FBGs provide thermo-elastic strain monitoring and have advantages over electrical gauges: electromagnetic interference immunity, no distance-dependent wires, and no need for return fibers. The temperature sensors based on FBGs are able to work in harsh environments and at cryogenic temperatures , as wellThis work has been divided into several sections. The next section begins with a brief introduction to the theoretical fundamentals of temperature and strain measurement using embedded FBGs and describes the optical bench and the design of the sensors embedded in it. This section ends with a brief presentation of the facilities in which the test was carried out and a description of the method to be used for the analysis of the results. This method is explained in detail in A FBG consists of a periodic modulation of the index of refraction of the core of an optical fiber ,2. Such The peak wavelength mentioned above is sensitive to both elongation and temperature changes. On the basis of Equation (1), considering changes in temperature and deformation, the Bragg wavelength changes are given by ;(2)∆λ=2From Equation (2), it is possible to obtain Equation (3), which provides the mathematical expression for the variation of the FBG peak wavelength with respect to temperature and strain:Finally, It has to be noted that in Equation (3), the strain is defined by:Combining Equations (3), (5) and (6), Equation (7) can be obtained, which describes the wavelength shift for a FBG due to temperature changes and strain:In the particular case of an FBG sensor that is not subjected to any mechanical load, there are no mechanically induced deformations in the FBG, and the wavelength shift does not depend on mechanical strain, so that Equation (7) can be expressed as:For practical purposes, Equation (8) is the mathematical representation of an FBG sensor working as a temperature sensor.k is also strain and temperature dependent ® PerFORM material by DSM Company . The performance of these connectors has been evaluated after panel curing and during thermal testing.The upper skin of the optical bench contains eight surface-mounted fiber connectors. The connector housings have been manufactured by Epsilon Optics Ltd.  in a 3D-printed thermoplastic based on SomosThe following figure presents a diagram of the installation of the FBG sensors on the optical bench.All the FBG embedded sensors have been installed in the upper skin of the TuMag optical bench breadboard. The FBGs are drawn tower gratings of 4 mm length, inscribed using the femtosecond technique and manufactured in polyimide-coated low-attenuation fiber by FBGs Technologies GmbH .Next, the description of both the temperature and strain sensors is presented in detail.There are six FBG-embedded temperature sensors in six independent optical fibers, labeled T1, T2, T3, T4, T5 and T6. The expected temperature sensitivity is 10 pm/K, and the wavelength peak at room conditions is presented in In order to meet the compensation sensor requirements outlined in The embedded strain FBG sensor consists of an array of seven FBGs with wavelengths between 1520 nm and 1550 nm, spaced at 5 nm.During the process of installing the inserts, the embedded FBGs S5, S6 and S7 were lost due to an issue with the machining of the optical bench. This problem also caused a loss of reflectivity in the sensors S1, S2, S3 and S4.−6 mbar ambient pressure  optical interrogator with eight measuring channels. This interrogator features 1 pm wavelength stability and 1 pm repeatability according to supplier specifications. An in-house-manufactured vacuum-compatible fiber optic feedthrough was used to connect the embedded FBGs to the interrogator. Six channels were used for the FBG temperature sensors and the other two for the FBG strain sensors.The optical bench was also equipped with two heaters for a thermal conductivity test. The temperature effects of the heaters were observed by the thermocouples and the embedded FBGs, as shown below.Data detection for FBG was carried out in the range of 1460 nm to 1620 nm. Peak wavelength detection was performed using two different, complementary methods. The peak wavelength of each FBG was monitored at 100 Hz using the LUNA Peak Detection application of the Enlight 1.18.8.0 Analysis Software. Additionally, the spectral response of the FBGs of each channel was recorded every thirty minutes with a step resolution of 8 pm. Information regarding the location of the FBG peak could also be extracted from the spectral data. A tailored MATLAB algorithm was used to extract the peak, even for peaks of 3 or 4 dBm. The method used to extract the peak wavelength from the spectrum data is explained in In this section, the raw temperature data are presented. We can also work with spectral data to obtain the peak wavelength of each sensor in the stabilization stages. An example of such data can be found in As for temperature sensors, the peak wavelength of the strain sensor data was recorded in two ways: from the peak detection application of the LUNA interrogator and from the FBG spectrum in the stabilization stages of the thermal test. For the embedded sensors, some data were missing due to an issue during manufacturing when the fiber was cut by the drilling tool, resulting in a Fabry–Perot effect that reduced the reflectivity of the strain sensor in the line. Additionally, the issue with the surface connector contributed to signal loss at low temperatures. Missing data have been retrieved using a tailored MATLAB algorithm. The strain results have been calculated using Equation (9) from the data in According to Equation (8), the slope of the linear fit of the temperature data  is the eThe sensor T1 is placed perpendicular to the surrounding carbon fibers, and therefore, the stainless-steel tube may be deformed by the surrounding carbon fibers. The deformation of the tube could block the free movement of the FBG inside the tube. To solve this problem, two possible solutions could be considered for future developments: The first is to always place the FBG temperature sensor always parallel to the surrounding carbon fibers, and the second could be to increase the stiffness of the FBG temperature sensor tube.From the previous paragraph, it can be concluded that the linear fit of the peak wavelength data can be a good tool to evaluate the quality of the FBG temperature sensors. For small temperature ranges, close to room temperature, the inverse linear fit slope can be a good approximation in order to translate wavelength peak data into temperature data. However, since the fiber thermo-optical coefficient and the coefficient of thermal expansion are not constant with temperature , the invThe orange ellipses shown in It is interesting to note that, as shown in As shown in Although The data in To understand the results of According to theory ,23, embeEmbedded FBG strain sensors present several advantages compared to surface-mounted ones, the most important of which is improved measurement accuracy.® polymers of the connector housing, the epoxy resin of the potting, and the stainless steel of the connector could have produced stress in the connectors, which could have been the cause of the connector fracture. Therefore, a new design of surface-mounted connectors, smaller than the previous ones and made entirely of stainless steel, is proposed.The performance of the FBG sensors was checked in lab conditions after the optical bench insert machining, and they were found to be working properly. After the thermal test, a visual inspection was carried out, and it was observed that some of the surface-mounted connectors had cracks. It is hypothesized that the differential dilatation between the PerformA CFRP optical bench has been developed for the TuMag instrument for the SUNRISE III mission, meeting demanding requirements for the location of the inserts and very stringent constraints on dimensional stability with respect to temperature changes. Additionally, a breadboard optical bench identical to the flight model but including embedded FBG sensors to measure temperature and strain has been manufactured. The objective of the development is to provide information about the deformation and temperature of optical instruments onboard a space mission during ground testing and operation for future missions.The design of FBG temperature sensors has been presented, showcasing a compromise solution between the miniature sensor housing diameter and the stacking of the carbon fiber in the optical bench. FBG temperature sensors have been characterized, and a third-order polynomial calibration has been proposed. Comparison of the temperature measurements by the FBG sensors and the thermocouples has yielded good agreement. The embedded FBG temperature sensors are very good candidates for temperature monitoring in CFRP optical benches, optomechanical parts, or assemblies.The optical bench strain has been measured with embedded and surface-mounted FBG strain sensors, and the results obtained have been compared. It can be concluded that embedded strain sensors can provide better solutions to monitor optical bench strain for optical instruments than surface-mounted ones.Implementing the improvements identified during the tests for surface-mounted connectors and embedded temperature sensors is the next step. The high maturity of the sensor design and the measuring strategy demonstrate a high potential for space applications.