Bending forming method and device of high-level computing power autonomous driving FPC sensor module

Abstract

The present invention relates to a bending forming method and device for a high-order computing power autonomous driving FPC sensor module, the method comprising obtaining physical property information of the FPC sensor module, performing stress framework analysis on the physical property information based on a preset Neo‑Hookean hyperelastic algorithm to obtain a corresponding initial property data set; performing bending analysis on the initial property data set based on a preset moment balance algorithm to obtain corresponding bending process parameters; performing target multi-axis bending simulation analysis on the FPC sensor module based on the bending process parameters to obtain initial forming information; performing gradient cold pressing and vibration suppression analysis on the deformation characteristics of the initial forming information, and constructing an architecture to obtain a corresponding anti-vibration optimized architecture; constructing a scheme based on the electrical performance optimization architecture and the anti-vibration optimization architecture combined with the initial forming information to obtain a corresponding module forming scheme. The present invention can more accurately evaluate the deformation characteristics of the module under different stress states.

Description

Technical Field

[0001] The present invention relates to the technical field of FPC sensor modules, and in particular to a bending forming method and device for a high-order computing power autonomous driving FPC sensor module. Background Art

[0002] With the rapid development of autonomous driving technology, high-order computing power FPC sensor modules, as core components of vehicle-mounted sensing systems, play an increasingly important role in modern smart cars. With the continuous improvement of vehicle intelligence and automation, how to ensure that FPC sensor modules maintain stable performance and reliable signal transmission quality in complex vehicle environments has become one of the key research topics in the industry. The existing bending and forming methods of FPC sensor modules mainly focus on single physical deformation characteristics, such as material stress or bending angle, but ignore the multi-dimensional challenges faced by the modules in actual applications, such as vibration interference and electrical performance attenuation. This overly simplified forming method often leads to problems such as unstable signals and insufficient vibration resistance during actual use of the module, which seriously affects the overall performance and reliability of the vehicle-mounted sensing system. Summary of the invention

[0003] The main purpose of the present invention is to provide a bending forming method and device for a high-level computing power autonomous driving FPC sensor module, which can more accurately evaluate the deformation characteristics of the module under different stress states.

[0004] To achieve the above object, the present invention provides a bending and forming method of a high-order computing power autonomous driving FPC sensor module, comprising:

[0005] Acquire physical property information of the FPC sensor module, perform stress framework analysis on the physical property information based on a preset Neo-Hookean hyperelastic algorithm, and obtain a corresponding initial property data set;

[0006] Performing bending analysis on the initial characteristic data set based on a preset moment balance algorithm to obtain corresponding bending process parameters;

[0007] Performing a target multi-axis bending simulation analysis on the FPC sensor module based on the bending process parameters to obtain initial forming information;

[0008] Performing gradient cold pressing and vibration suppression analysis on the deformation characteristics of the initial forming information, and constructing an architecture to obtain a corresponding anti-vibration optimized architecture;

[0009] Performing impedance matching analysis on the anti-vibration optimization architecture to obtain a corresponding electrical performance optimization architecture;

[0010] A solution is constructed based on the electrical performance optimization architecture and the anti-vibration optimization architecture in combination with the initial molding information to obtain a corresponding module molding solution.

[0011] Furthermore, the physical property information of the FPC sensor module is obtained, and a stress framework analysis is performed on the physical property information based on a preset Neo-Hookean hyperelastic algorithm to obtain a corresponding initial property data set, including:

[0012] The conductive layer thickness, insulating layer thickness and covering layer thickness of the FPC sensor module are measured and collected to obtain interlayer structure parameters;

[0013] Conducting copper foil conductivity, dielectric constant and shear modulus testing on the FPC sensor module to obtain material characteristic parameters;

[0014] A Neo-Hookean hyperelastic calculation matrix is ​​constructed according to the interlayer structure parameters and the material characteristic parameters to obtain a stress calculation matrix;

[0015] Performing piecewise linear interpolation calculation on the stress calculation matrix to obtain multi-dimensional stress distribution data;

[0016] Calculate the stress response function according to the multi-dimensional stress distribution data to obtain material deformation rate data;

[0017] Performing hyperelastic coefficient calibration on the material deformation rate data to obtain a calibration coefficient;

[0018] Performing stress-strain curve fitting on the calibration coefficient to obtain characteristic stress data;

[0019] The characteristic stress data is calibrated using Neo-Hookean parameters to obtain the initial characteristic data set.

[0020] Furthermore, the bending analysis of the initial characteristic data set is performed based on a preset moment balance algorithm to obtain corresponding bending process parameters, including:

[0021] Extracting material elastic modulus parameters and Poisson's ratio parameters from the initial characteristic data set to obtain corresponding material characteristic parameters;

[0022] Decomposing the lateral moment and longitudinal moment of the FPC sensor module according to the material characteristic parameters to obtain corresponding moment component data;

[0023] Performing lateral moment balance algorithm configuration processing on the moment component data to obtain corresponding lateral bending force parameters;

[0024] Performing longitudinal moment balance algorithm configuration processing on the moment component data to obtain corresponding longitudinal bending force parameters;

[0025] Performing bending force synthesis processing on the FPC sensor module according to the transverse bending force parameter and the longitudinal bending force parameter to obtain corresponding resultant force data;

[0026] Performing moment balance verification processing on the resultant force data to obtain corresponding equilibrium state parameters;

[0027] Calculating the bending force of the FPC sensor module according to the equilibrium state parameters to obtain corresponding force control parameters;

[0028] Performing bending angle mapping processing on the force control parameter to obtain a corresponding angle control parameter;

[0029] Calculating the bending rate of the FPC sensor module according to the angle control parameter to obtain a corresponding rate control parameter;

[0030] The force control parameter, the angle control parameter and the speed control parameter are integrated to obtain the bending process parameter.

[0031] Further, the target multi-axis bending simulation analysis is performed on the FPC sensor module based on the bending process parameters to obtain initial forming information, including:

[0032] Performing boundary surface subdivision processing on the FPC sensor module according to the bending process parameters to obtain corresponding grid topology data;

[0033] Performing non-uniform interpolation calculation on the grid topology data to obtain a corresponding deformation control point coordinate set;

[0034] Performing quaternion rotation interpolation operation based on the deformation control point coordinate set to obtain a corresponding spatial rotation trajectory;

[0035] Performing discrete curvature analysis on the spatial rotation trajectory to obtain a corresponding stress distribution function;

[0036] Solving and analyzing the deformation energy of the stress distribution function in each axial bending process to obtain the corresponding energy change trend;

[0037] Performing bending trajectory curve fitting on the energy change trend to obtain a corresponding motion parameter set;

[0038] The motion parameter set, the energy variation trend and the space rotation trajectory are subjected to a forming analysis to obtain corresponding initial forming information.

[0039] Furthermore, the deformation characteristics of the initial forming information are subjected to gradient cold pressing and vibration suppression analysis, and an architecture is constructed to obtain a corresponding anti-vibration optimization architecture, including:

[0040] Performing multi-dimensional tensor decomposition on the initial forming information to obtain a corresponding deformation characteristic matrix;

[0041] Calculating the node stress distribution of the deformation characteristic matrix to obtain corresponding stress distribution data;

[0042] Performing gradient pressure optimization calculation on the stress distribution data to obtain corresponding gradient pressure data;

[0043] Perform stress relaxation calculation according to the gradient pressure data to obtain corresponding cold pressing characteristic parameters;

[0044] Performing Rayleigh damping modal analysis according to the cold pressing characteristic parameters and the deformation characteristic matrix to obtain corresponding natural frequency data;

[0045] Performing vibration response optimization on the natural frequency data to obtain corresponding vibration suppression data;

[0046] Performing constrained optimization on the vibration suppression data to obtain corresponding anti-vibration architecture parameters;

[0047] The architecture is constructed according to the anti-vibration architecture parameters, the gradient pressure data and the vibration suppression data to obtain the anti-vibration optimized architecture.

[0048] Further, the Rayleigh damping modal analysis is performed according to the cold pressing characteristic parameters and the deformation characteristic matrix to obtain corresponding natural frequency data, including:

[0049] Performing mass distribution calculation on the deformation characteristic matrix to obtain a corresponding mass matrix;

[0050] Perform elastic modulus analysis according to the cold pressing characteristic parameters to obtain a corresponding stiffness matrix;

[0051] Performing a main frequency calculation on the mass matrix to obtain a first control frequency;

[0052] Performing frequency response calculation on the stiffness matrix to obtain a second control frequency;

[0053] Calculating the damping ratio according to the first control frequency and the second control frequency to obtain a corresponding damping coefficient;

[0054] Linearly combining the mass matrix and the stiffness matrix, and constructing a Rayleigh damping matrix according to the damping coefficient to obtain a system damping matrix;

[0055] The frequency is solved according to the system damping matrix to obtain the natural frequency data.

[0056] Furthermore, the impedance matching analysis is performed on the anti-vibration optimization architecture to obtain a corresponding electrical performance optimization architecture, including:

[0057] Performing electromagnetic interference analysis on the anti-vibration optimization architecture to obtain corresponding electromagnetic interference frequency characteristic data;

[0058] Decomposing and calculating the electromagnetic interference frequency characteristic data based on a preset frequency domain coupling balance algorithm to obtain a corresponding frequency domain coupling matrix;

[0059] Performing multi-layer impedance analysis of the signal transmission path of the FPC sensor module according to the frequency domain coupling matrix to obtain a corresponding impedance distribution characteristic diagram;

[0060] Extracting harmonic components from the impedance distribution characteristic diagram to obtain corresponding impedance harmonic component data;

[0061] Iteratively calculating the impedance harmonic component data to obtain corresponding impedance matching optimization parameters;

[0062] Performing segmented impedance compensation calculation on the signal transmission layer of the FPC sensor module according to the impedance matching optimization parameters to obtain a corresponding impedance compensation data set;

[0063] Performing electromagnetic field simulation analysis on the impedance compensation data set to obtain corresponding electromagnetic field spatial distribution characteristics;

[0064] Performing a performance evaluation on the electromagnetic field spatial distribution characteristics to obtain a corresponding electrical performance evaluation result;

[0065] The electrical performance optimization architecture is obtained by performing optimization and reconstruction according to the electromagnetic field spatial distribution characteristics and the electrical performance evaluation results.

[0066] Furthermore, the scheme construction based on the electrical performance optimization architecture and the anti-vibration optimization architecture combined with the initial molding information to obtain a corresponding module molding scheme includes:

[0067] Performing impedance distribution mapping processing on the electrical performance optimization architecture to obtain an impedance density distribution matrix;

[0068] Performing stress field superposition analysis on the anti-vibration optimization architecture according to the impedance density distribution matrix to obtain comprehensive stress distribution data;

[0069] Performing multi-dimensional vibration modal decomposition on the comprehensive stress distribution data to obtain a set of eigenvectors;

[0070] Performing geometric topological reconstruction on the initial forming information according to the feature vector group to obtain topological reconstruction data;

[0071] Performing multi-parameter coupling optimization on the topology reconstruction data to obtain process parameter data, wherein the process parameter data includes a bending angle control parameter, a pressure control parameter, a temperature control parameter and a time control parameter;

[0072] A scheme is constructed according to the process parameter data to obtain the module molding scheme.

[0073] The present invention provides a bending and forming device for a high-order computing power autonomous driving FPC sensor module, which is applied to the bending and forming method of the high-order computing power autonomous driving FPC sensor module described in any one of the above items, comprising:

[0074] An acquisition module, the acquisition module is used to obtain physical property information of the FPC sensor module, perform stress framework analysis on the physical property information based on a preset Neo-Hookean hyperelastic algorithm, and obtain a corresponding initial property data set;

[0075] An analysis module, the analysis module is used to perform bending analysis on the initial characteristic data set based on a preset moment balance algorithm to obtain corresponding bending process parameters;

[0076] An association module, the association module is used to perform a target multi-axis bending simulation analysis on the FPC sensor module based on the bending process parameters to obtain initial forming information;

[0077] A processing module, the processing module is used to perform gradient cold pressing and vibration suppression analysis on the deformation characteristics of the initial forming information, and to construct an architecture to obtain a corresponding anti-vibration optimization architecture;

[0078] A control module, the control module is used to perform impedance matching analysis on the anti-vibration optimization architecture to obtain a corresponding electrical performance optimization architecture;

[0079] An execution module is used to construct a solution based on the electrical performance optimization architecture and the anti-vibration optimization architecture in combination with the initial molding information to obtain a corresponding module molding solution.

[0080] The present invention provides a bending and forming method and device for a high-order computing power autonomous driving FPC sensor module, which has the following beneficial effects:

[0081] By introducing the Neo-Hookean hyperelastic algorithm to systematically analyze the physical characteristics of the FPC sensor module, and combining the torque balance algorithm for bending analysis, the deformation characteristics of the module under different stress states can be more accurately evaluated, thereby improving the accuracy of bending forming and providing a more reliable technical basis for module design and manufacturing. By performing gradient cold pressing and vibration suppression analysis on the initial forming information, the module's anti-vibration performance is finely controlled, effectively solving the problem of vibration stability of the sensor module in the vehicle environment. The electrical performance is optimized based on impedance matching analysis technology to ensure that the module can maintain stable signal transmission quality under different working conditions, thereby improving the overall reliability of the system. By comprehensively analyzing the anti-vibration optimization architecture and the electrical performance optimization architecture, a more reasonable module molding scheme is formulated, thereby effectively improving the performance of the sensor module in actual applications. At the same time, by considering the application characteristics of the module in different vehicle environments, the molding process parameters can be flexibly adjusted, so that the module can better adapt to complex and changing application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a flow chart of a bending and forming method of a high-order computing power autonomous driving FPC sensor module provided by the present invention;

[0083] Figure 2 This is a structural diagram of a bending and forming device for a high-order computing power autonomous driving FPC sensor module provided by the present invention.

[0084] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0086] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0087] Reference Figure 1 As shown, the present invention provides a bending forming method of a high-order computing power autonomous driving FPC sensor module, comprising:

[0088] Step S1: obtaining physical property information of the FPC sensor module, performing stress framework analysis on the physical property information based on a preset Neo-Hookean hyperelastic algorithm, and obtaining a corresponding initial property data set;

[0089] Step S2: performing bending analysis on the initial characteristic data set based on a preset moment balance algorithm to obtain corresponding bending process parameters;

[0090] Step S3: performing a target multi-axis bending simulation analysis on the FPC sensor module based on the bending process parameters to obtain initial forming information;

[0091] Step S4: performing gradient cold pressing and vibration suppression analysis on the deformation characteristics of the initial forming information, and constructing an architecture to obtain a corresponding anti-vibration optimization architecture;

[0092] Step S5: performing impedance matching analysis on the anti-vibration optimization architecture to obtain a corresponding electrical performance optimization architecture;

[0093] Step S6: construct a solution based on the electrical performance optimization architecture and the anti-vibration optimization architecture in combination with the initial molding information to obtain a corresponding module molding solution.

[0094] Based on the above steps, the detailed process is as follows:

[0095] Step S1: Perform comprehensive physical property measurement and data collection on the FPC sensor module, including the module's geometric size parameters (length, width, thickness) and basic physical parameters of each layer of material, such as Young's modulus, Poisson's ratio, density, etc. For laminated structures, it is necessary to record in detail the characteristic parameters of each layer of material, such as the copper foil layer, insulation layer, adhesive layer, as well as the interface bonding strength and interlayer shear strength.

[0096] After obtaining these basic data, the Neo-Hookean hyperelastic algorithm is used to establish the material constitutive equation, which needs to consider the nonlinear characteristics of the material during large deformation. By constructing a strain energy density function containing volume change terms and shape change terms, combined with the calculation of the principal strain invariant, a complete stress-strain relationship is established. Considering the influence of temperature on material properties, temperature-related terms are added to the model. Finally, a stress framework analysis is performed, a finite element mesh model is established, and the mesh units are reasonably divided, and then boundary conditions and load conditions under actual working conditions are applied. The stress distribution cloud map is obtained through nonlinear solution, and characteristic data such as deformation field and stress field are output. These data will serve as important inputs for subsequent bending analysis.

[0097] Step S2: After obtaining the material properties and stress analysis results, this step mainly conducts mechanical analysis of the bending process. A mechanical model of the bending process is established, focusing on the influence of the bending angle and curvature on the material deformation. By calculating the neutral layer position of each layer of material, analyzing the stress distribution and strain distribution during the bending process, and establishing an accurate moment balance equation group.

[0098] Based on the results of moment balance analysis, key process parameters are determined, including minimum bending radius, bending temperature range, bending rate, and pressure distribution requirements. The determination of these parameters requires comprehensive consideration of the mechanical property limits of the material and the actual process feasibility. At the same time, parameter sensitivity analysis is performed to establish a process parameter optimization model, and the optimal process parameter set is output under the consideration of various process constraints.

[0099] Step S3: After determining the process parameters, carry out detailed bending simulation analysis. Build an accurate three-dimensional geometric model, input the obtained material properties and actual contact conditions into the model. Define the geometric characteristics and boundary conditions of the bending tooling according to the process requirements, and establish a complete multi-step bending process model.

[0100] Quasi-static bending analysis is performed to calculate the evolution of stress and strain during the entire bending process, with a focus on analyzing the interlayer stress distribution. The forming effect is comprehensively evaluated by setting reasonable forming quality evaluation indicators. The final output includes complete forming information including the displacement field of key nodes and stress and strain distribution data, and process improvement suggestions are given based on the analysis results. These forming information will serve as the basic data for subsequent optimization analysis.

[0101] Step S4: Perform gradient cold pressing and vibration suppression analysis on the deformation characteristics of the initial forming information. After obtaining the initial forming information, perform deformation characteristic analysis on the FPC sensor module. By analyzing the deformation data of the module during the bending process, focus on the stress concentration and deformation at each key position to determine the area that needs to be focused on cold pressing. According to these deformation characteristics, determine the appropriate cold pressing temperature range and pressure range, analyze the stress release of the material during the cold pressing process, and finally determine the optimal cold pressing process parameters, including specific temperature, pressure and time requirements. At the same time, analyze the vibration sources that the FPC sensor module may encounter in the actual application environment, calculate the displacement response of each key point, and determine the key areas that need vibration suppression. Based on the above analysis results, design a reasonable support structure layout plan, plan the distribution position of the fixed points, and finally form a complete anti-vibration optimization architecture plan.

[0102] Step S5: Perform impedance matching analysis on the anti-vibration optimization architecture. Based on the obtained anti-vibration optimization architecture, analyze the impedance changes in each area of ​​the FPC sensor module. By determining the critical path of signal transmission, possible impedance mismatch points are identified. Then adjust the layout of the transmission line, optimize the distribution of the ground layer, and determine the impedance control requirements of each key node. On this basis, perform electrical performance evaluation, analyze signal integrity indicators, evaluate electromagnetic interference levels, and verify impedance matching effects. Determine the final line layout plan, formulate a complete impedance control strategy, and form a complete electrical performance optimization architecture. This architecture needs to ensure that all electrical performance requirements are met while ensuring mechanical performance.

[0103] Step S6: Based on the electrical performance optimization architecture and the anti-vibration optimization architecture combined with the initial molding information, the solution is constructed. In the final solution construction stage, all the analysis results of the first five steps are used. By evaluating the mutual influence between various performance indicators, the key optimization goals are determined. On this basis, a specific optimization plan is formulated, including determining the specific parameters of the molding process, formulating quality control points, and establishing process specifications for the molding process. At the same time, a corresponding verification plan is designed, including test verification methods, performance testing standards, and quality evaluation systems. Finally, a complete process flow document is output, detailed operating specifications are formulated, and a complete quality control system is established. The entire plan needs to ensure that both mechanical and electrical properties can meet the expected requirements, while having good feasibility and controllability.

[0104] The present invention provides a bending forming method for a high-order computing power autonomous driving FPC sensor module. By introducing the Neo-Hookean hyperelastic algorithm to systematically analyze the physical characteristics of the FPC sensor module, and combining the torque balance algorithm for bending analysis, the deformation characteristics of the module under different stress states can be more accurately evaluated, thereby improving the accuracy of bending forming and providing a more reliable technical basis for module design and manufacturing. By performing gradient cold pressing and vibration suppression analysis on the initial forming information, the refined control of the module's anti-vibration performance is achieved, and the vibration stability problem of the sensor module in the vehicle environment is effectively solved. The electrical performance is optimized based on impedance matching analysis technology to ensure that the module can maintain stable signal transmission quality under different working conditions, thereby improving the overall reliability of the system. By comprehensively analyzing the anti-vibration optimization architecture and the electrical performance optimization architecture, a more reasonable module forming scheme is formulated, thereby effectively improving the performance of the sensor module in practical applications. At the same time, by considering the application characteristics of the module in different vehicle environments, the forming process parameters can be flexibly adjusted so that the module can better adapt to complex and changeable application scenarios.

[0105] In one embodiment, the physical property information of the FPC sensor module is obtained, and a stress framework analysis is performed on the physical property information based on a preset Neo-Hookean hyperelastic algorithm to obtain a corresponding initial property data set, including:

[0106] The acquisition of physical property information and the analysis process of the Neo-Hookean hyperelastic algorithm specifically include multiple technical links. When measuring the interlayer structural parameters, a high-precision digital micrometer is used to measure the thickness of the conductive layer, insulating layer and covering layer of the FPC sensor module. The measurement accuracy is controlled within the range of ±0.001mm, and each layer is measured 5 times at different positions to take the average value as the final data.

[0107] Material characteristic parameter detection uses a professional four-probe tester to measure the conductivity of copper foil, the test voltage is set to 10V, and the test current range is 0-100mA; an impedance analyzer is used to measure the dielectric constant, and the test frequency is set to scan in the range of 1kHz-1MHz; a dynamic mechanical analyzer is used to measure the shear modulus, and the shear rate is controlled at 0.1-10s -1 within the range.

[0108] When constructing the Neo-Hookean hyperelastic calculation matrix, a 9×9 stiffness matrix is ​​established based on the obtained interlayer structural parameters and material characteristic parameters. The matrix elements contain material constants such as Young's modulus and Poisson's ratio. The calculation domain is meshed through finite element discretization, and the mesh size is set to 0.1mm to ensure the calculation accuracy. The stress distribution data is obtained using the piecewise cubic spline interpolation algorithm, and the stress components such as principal stress and shear stress are calculated at each grid node.

[0109] The material deformation rate data calculation is based on the Neo-Hookean constitutive equation, and the mapping relationship between the stress response function and the strain is established. A linear relationship is used in the small deformation area (strain <5%), and a nonlinear hyperelastic model is used in the large deformation area (strain ≥5%). During the hyperelastic coefficient calibration process, the stress-strain experimental data is obtained through a uniaxial tensile test, and the calibration coefficients such as material parameters C10 and D1 are determined by combining the least squares method.

[0110] The Mooney-Rivlin model is used for stress-strain curve fitting, and the curve fitting accuracy requires R2>0.95. When extracting characteristic stress data, a characteristic point is taken every 5% in the range of 0-100% of strain to form a characteristic stress data set. During the Neo-Hookean parameter calibration process, the characteristic stress data is substituted into the constitutive equation, and the optimal material parameter combination is determined by the iterative optimization method, so that the error between the calculated value and the experimental value is less than 3%.

[0111] After the Neo-Hookean parameter calibration is completed, an initial characteristic data set containing the following elements is formed: basic material parameters (including calibrated Young's modulus E, Poisson's ratio v value, initial shear modulus μ0), Neo-Hookean model parameters (including strain energy density function coefficient C10, bulk modulus parameter D1), characteristic stress strain point data (including characteristic point stress values ​​within the strain range of 0-100%), interlayer structure parameters (three-layer thickness data) and material parameters (conductivity, dielectric constant, shear modulus). These data constitute a complete initial characteristic data set.

[0112] This embodiment achieves accurate description and prediction of the mechanical behavior of the FPC sensor module by establishing a complete physical property information acquisition and Neo-Hookean hyperelastic analysis system. High-precision measurement and multi-dimensional material parameter characterization are used, combined with the Neo-Hookean hyperelastic theory to construct a stress calculation matrix, ensuring the accurate acquisition of interlayer structural parameters and material properties. The segmented linear interpolation calculation is introduced into the stress response analysis, and the mapping relationship between stress distribution and material deformation is established, which improves the accuracy of stress prediction. Through the calibration of hyperelastic coefficients and stress-strain curve fitting, a complete material property data set is established, which provides a reliable basis for the optimization of the bending forming process. It overcomes the problems of inaccurate stress prediction and blind parameter optimization in the traditional bending process, and significantly improves the process level and product quality stability of the bending forming of the FPC sensor module.

[0113] In one embodiment, a bending analysis is performed on the initial characteristic data set based on a preset moment balance algorithm to obtain corresponding bending process parameters, including:

[0114] When processing the initial characteristic data set, the material elastic modulus parameter and Poisson's ratio parameter are extracted by material mechanics analysis method. The elastic modulus parameter characterizes the stress-strain relationship of the material in the elastic deformation stage, and the Poisson's ratio parameter characterizes the ratio relationship between the transverse and longitudinal deformation of the material when it is deformed by force. Specifically, the material elastic modulus parameter is obtained by the slope of the stress-strain curve, and the Poisson's ratio parameter is obtained by the negative ratio of the transverse strain to the longitudinal strain.

[0115] After obtaining the material characteristic parameters, the external torque applied by the FPC sensor module is decomposed. The lateral torque Mx acts in the width direction of the FPC sensor module, and the longitudinal torque My acts in the length direction. The torque decomposition uses the vector analysis method to project the resultant torque M into the two orthogonal directions of the lateral and longitudinal directions to obtain the torque component data [Mx, My].

[0116] Configure the moment balance algorithm for the moment component data. The lateral moment balance algorithm is based on Hooke's law and takes into account the elastic deformation characteristics of the material to calculate the external force Fx required for lateral bending. The longitudinal moment balance algorithm is also based on Hooke's law and calculates the external force Fy required for longitudinal bending. The moment balance algorithm ensures that the external force and the internal stress of the material are in balance during the bending process.

[0117] According to the lateral bending force parameter Fx and the longitudinal bending force parameter Fy, the parallelogram law of force is used to synthesize and obtain the resultant force acting on the FPC sensor module. The resultant force data includes the magnitude and direction angle of the resultant force.

[0118] The torque balance verification is performed on the resultant force data to verify whether the FPC sensor module is in a stable equilibrium state under the action of the resultant force. The verification process is based on the minimum potential energy principle and calculates the total potential energy of the system. When the total potential energy reaches the minimum value, the system is in a stable equilibrium state. The equilibrium state parameters include the potential energy value and the equilibrium stability index.

[0119] The bending force is calculated based on the equilibrium state parameters. The force control parameters include three dimensions: force size, action time and action position. The force calculation takes into account the stress-strain relationship of the material to ensure that the elastic limit of the material is not exceeded.

[0120] The force control parameters are mapped to the bending angle space, the force-angle correspondence is established, and the angle control parameters are obtained. The angle mapping is based on the deflection theory in material mechanics and takes into account the elastic deformation characteristics of the material.

[0121] The bending rate is calculated based on the angle control parameters, which include angular velocity and linear velocity. The rate calculation takes into account the strain rate sensitivity of the material to avoid material damage caused by excessive bending.

[0122] The force control parameters, angle control parameters and rate control parameters are integrated in multiple dimensions to form a complete set of bending process parameters.

[0123] This embodiment achieves precise control of the mechanical characteristics of the FPC sensor module during the bending process by extracting material characteristic parameters and performing torque decomposition analysis on the initial characteristic data set, effectively avoiding the problem of module damage caused by improper parameter control in traditional bending methods. Based on the torque balance algorithm, the lateral and longitudinal torques are independently configured, so that the force during the bending process is more uniform, which significantly improves the forming quality of the FPC sensor module. By establishing a force-angle mapping relationship and introducing rate control parameters, fine adjustment of the bending process is achieved, overcoming the defect of inaccurate angle control in traditional bending methods. The use of a multi-dimensional parameter integration method to formulate bending process parameters not only ensures the stability of the bending process, but also improves production efficiency and reduces material loss rate.

[0124] In one embodiment, a target multi-axis bending simulation analysis is performed on the FPC sensor module based on the bending process parameters to obtain initial forming information, including:

[0125] Based on the bending process parameters, the FPC sensor module is subjected to target multi-axis bending simulation analysis to obtain initial forming information.

[0126] When the boundary surface of the FPC sensor module is subdivided, an adaptive meshing algorithm is used to divide the boundary surface into mesh units of varying sizes according to the geometric characteristics of the FPC sensor module. During the meshing process, the mesh size in the area with a large curvature change is smaller, and the mesh size in the area with a small curvature change is larger, thereby ensuring that the mesh quality meets the calculation accuracy requirements. The mesh topology data is obtained through this subdivision process, including information such as node coordinates and unit connection relationships.

[0127] After obtaining the grid topology data, the Hermite interpolation algorithm is used to perform non-uniform interpolation calculations on the grid nodes. The position, tangent vector and curvature information of the nodes are considered in the interpolation calculation process, and different interpolation weights are used for different areas to generate a deformation control point coordinate set. This coordinate set contains the position information of the key control points of the FPC sensor module during the bending process.

[0128] Based on the obtained deformation control point coordinate set, quaternion rotation interpolation operation is performed through spherical linear interpolation algorithm. By calculating the quaternion difference between adjacent control points, a continuous rotation transformation sequence is constructed to obtain the rotation trajectory of the FPC sensor module in space. The rotation trajectory reflects the posture changes of each part of the sensor module during the bending process.

[0129] For the obtained spatial rotation trajectory, the curvature analysis is performed by using discrete differential geometry methods. By calculating the curvature value of each point on the trajectory curve, a curvature distribution function is established, which reflects the stress concentration area of ​​the FPC sensor module during the bending process. The calculation of the stress distribution function takes into account the influence of factors such as material properties and thickness.

[0130] During the bending process, the deformation energy of the stress distribution function is solved. By calculating the strain energy and elastic potential energy in each axial bending process, an energy change trend function is established. This function describes the energy accumulation and release law of the FPC sensor module during the bending process, providing a basis for optimizing the bending process.

[0131] For the obtained energy change trend, the bending trajectory curve is fitted by using the least square method. The motion parameter set describing the bending process is obtained through fitting, including dynamic parameters such as angular velocity and angular acceleration. These parameters are used to control the motion characteristics of the bending device.

[0132] After obtaining the motion parameter set, energy change trend and spatial rotation trajectory, the FPC sensor module is subjected to forming analysis through finite element analysis. The analysis process comprehensively considers the material constitutive relationship, boundary conditions and loading conditions, predicts the deformation behavior and residual stress distribution of the sensor module, and obtains the initial forming information reflecting the forming characteristics of the sensor module.

[0133] This embodiment achieves an accurate description of complex surfaces and improves the accuracy of simulation analysis by performing boundary surface subdivision processing and grid topology data construction on the FPC sensor module. The use of non-uniform interpolation calculation and quaternion rotation interpolation operations ensures the continuity of the spatial position and posture changes of each control point during the bending process, effectively avoiding stress concentration and local deformation during the bending process. Through discrete curvature analysis and deformation energy solution, the stress distribution and energy change trend are accurately predicted, providing a reliable basis for optimizing the bending process parameters. The bending trajectory curve is fitted based on the energy change trend, and an accurate set of motion parameters is obtained, which improves the controllability of the bending forming process. The motion parameter set, energy change trend and spatial rotation trajectory are comprehensively analyzed to achieve a comprehensive prediction of the forming characteristics of the FPC sensor module.

[0134] In one embodiment, gradient cold pressing and vibration suppression analysis are performed on the deformation characteristics of the initial forming information, and an architecture is constructed to obtain a corresponding anti-vibration optimization architecture, including:

[0135] The initial forming information is decomposed into three tensor matrices using the eigenvalue decomposition method, including the deformation amplitude matrix, phase matrix and direction matrix. The deformation characteristic matrix is ​​obtained by combining these three matrices. The deformation characteristic matrix contains characteristic information such as the deformation trend, deformation direction and deformation degree of the FPC sensor module during the bending process.

[0136] Based on the deformation characteristic matrix obtained, the finite element analysis method is used to calculate the stress distribution of each node. In the finite element analysis, the FPC sensor module is divided into several grid units, and the stress-strain constitutive equation is established. The stress value of each node is obtained through iterative calculation to form stress distribution data. The node stress value must meet the von Mises yield criterion, that is, the equivalent stress does not exceed the yield strength of the material.

[0137] The gradient pressure optimization of stress distribution data is carried out by using the pressure gradient descent method. The objective function is to minimize the maximum stress value, and the constraint condition is to maintain the forming accuracy. Through iterative optimization calculation, the pressure value of each pressure point is adjusted until the objective function converges to obtain the optimal gradient pressure data. The gradient pressure data reflects the size and distribution of pressure required to be applied to each area during the bending process.

[0138] Based on the obtained gradient pressure data, stress relaxation calculation is performed. The stress relaxation equation is established to calculate the degree of stress relaxation under different temperature and time conditions, and the cold pressing characteristic parameters are obtained. The cold pressing characteristic parameters include process parameters such as cold pressing temperature, pressure holding time, and cooling rate.

[0139] According to the cold pressing characteristic parameters and deformation characteristic matrix, the modal analysis method is used to calculate Rayleigh damping. By solving the characteristic equation, the natural frequency and vibration mode of the system are obtained, the mass matrix and stiffness matrix are established, the Rayleigh damping coefficient is calculated, and the natural frequency data is obtained. The natural frequency data reflects the dynamic characteristics of the system.

[0140] The vibration response of the natural frequency data is optimized, and the frequency domain analysis method is used to establish the transfer function and calculate the vibration response under different excitation frequencies. By optimizing the damping parameters, the vibration amplitude of the system is minimized within the operating frequency range, and the vibration suppression data is obtained. The vibration suppression data includes the optimal damping ratio and its distribution.

[0141] The vibration suppression data is constrained and optimized, stiffness constraints, mass constraints and space constraints are set, and the topology optimization method is used to optimize the structural layout and obtain the anti-vibration structure parameters. The anti-vibration structure parameters define the geometric size, layout position and material parameters of the supporting structure.

[0142] The architecture is constructed based on the anti-vibration architecture parameters, gradient pressure data, and vibration suppression data. The parametric modeling method is used to build a three-dimensional solid model, optimize the layout of the support structure, achieve the goal of uniform distribution of structural stress and minimize vibration response, and finally obtain an anti-vibration optimized architecture. This optimized architecture has good structural strength and anti-vibration performance, ensuring the accuracy and reliability of the FPC sensor module during the bending and forming process.

[0143] This embodiment accurately obtains characteristic information such as deformation trend, direction and degree of the FPC sensor module during the bending process by performing multi-dimensional tensor decomposition and combination operations on the initial forming information, laying a data foundation for subsequent optimization analysis. The node stress distribution is calculated using the finite element analysis method, and the pressure gradient descent method is combined for optimization to achieve uniform control of stress distribution and avoid structural damage caused by local stress concentration. Through Rayleigh damping modal analysis and frequency domain optimization, the optimal damping parameter configuration is obtained, which effectively suppresses system vibration and improves forming accuracy. The support structure is constructed based on the topology optimization method, and multi-objective constraint optimization of stiffness, mass and space is achieved. The vibration response is minimized while ensuring the structural strength, which significantly improves the vibration resistance and bending reliability of the FPC sensor module.

[0144] In one embodiment, Rayleigh damping modal analysis is performed based on the cold pressing characteristic parameters and the deformation characteristic matrix to obtain corresponding natural frequency data, including:

[0145] In the mass distribution calculation of the deformation characteristic matrix, the FPC sensor module is divided into several rows and columns of unit grids using unit grid division technology. The thickness, density and area of ​​each unit grid are set, and the mass matrix is ​​obtained through integral operation. The mass matrix is ​​composed of determinants, and each element represents the mass component of the corresponding unit. The construction of the mass matrix must ensure that the grid division accuracy is not less than 0.1 mm, and the measurement error of the density parameter should be controlled within plus or minus one percent.

[0146] For the elastic modulus analysis of cold pressing characteristic parameters, a stress-strain relationship model is established based on the material mechanics theory, and the pressure, temperature and other parameters in the cold pressing process are substituted into the calculation to obtain the elastic modulus. The finite element method is used to construct the stiffness matrix, which is composed of determinants, and each element represents the stiffness coefficient between unit nodes. During the elastic modulus analysis, the pressure control range is 0.5 to 2.0 MPa, and the temperature control range is 15 to 30 degrees Celsius.

[0147] In the main frequency calculation phase, the eigenvalue analysis method is applied to solve the mass matrix, and the first control frequency is obtained by calculating the equation where the determinant value of the product of the mass matrix and the square of the angular frequency is zero after subtracting the stiffness matrix. This process uses the matrix decomposition algorithm for numerical calculation, and the iterative convergence accuracy is set to ten to the negative sixth power.

[0148] The frequency response calculation applies simple harmonic excitation to the stiffness matrix and uses the frequency domain analysis method to obtain the frequency response function of the system, thereby determining the second control frequency. The bandwidth range of the frequency response analysis is set to zero to one thousand hertz, and the sampling interval is no greater than one hertz.

[0149] The damping ratio calculation is based on the first control frequency and the second control frequency, and the dual frequency method is used to determine the damping coefficient. The calculation formula is: the damping ratio is equal to the sum of the first damping coefficient divided by twice the angular frequency and the second damping coefficient multiplied by half the angular frequency. The damping ratio value range is usually controlled between 1% and 5% to ensure that the system has appropriate vibration attenuation characteristics.

[0150] The damping matrix is ​​constructed using a linear combination method, that is, the system damping matrix is ​​equal to the product of the first damping coefficient and the mass matrix plus the product of the second damping coefficient and the stiffness matrix. The positive definiteness of the matrix must be ensured during construction to ensure the physical feasibility of the calculation results. The construction accuracy of the system damping matrix directly affects the subsequent frequency solution results.

[0151] The natural frequency solution adopts the generalized eigenvalue analysis method, which calculates the product of the stiffness matrix minus the mass matrix and the square of the angular frequency, and then adds the product of the angular frequency and the damping matrix, and finally obtains the natural frequency data of the system by the equation where the product of the vibration mode vector is equal to zero. The solution process adopts a matrix iteration algorithm, and the calculation includes at least the first ten natural frequencies, and the calculation accuracy of each frequency is required to be better than 0.1 Hz. The obtained natural frequency data is used to guide the optimization of the bending forming process parameters of the FPC sensor module.

[0152] This embodiment establishes a complete frequency characteristic evaluation system by performing a refined bending and forming analysis on the high-order computing power autonomous driving FPC sensor module. Through the precise construction of the mass matrix and the stiffness matrix, an accurate description of the dynamic characteristics of the module structure is achieved, effectively avoiding the deformation error caused by improper parameter setting in the traditional method. The damping ratio calculation scheme based on the dual-frequency method enables the system to accurately control the vibration attenuation characteristics while maintaining structural stability, significantly improving the controllability of the bending and forming process. The use of the linear combination method of the Rayleigh damping matrix not only simplifies the calculation complexity, but also ensures the physical feasibility of the results, providing a reliable basis for the optimization of process parameters. Through the systematic analysis of the natural frequency data, the dynamic response characteristics of the module during the bending process can be accurately predicted, thereby effectively preventing problems such as uneven deformation and stress concentration, and improving the yield and reliability of the product.

[0153] In one embodiment, impedance matching analysis is performed on the anti-vibration optimization architecture to obtain a corresponding electrical performance optimization architecture, including:

[0154] During the impedance matching analysis of the anti-vibration optimization architecture, an electromagnetic interference scanning device is used to scan the anti-vibration optimization architecture in the full frequency band to obtain an electromagnetic interference frequency response curve, and a spectrum analyzer is used to collect and process the electromagnetic interference frequency response curve to form electromagnetic interference frequency characteristic data. The electromagnetic interference frequency characteristic data includes interference source location information, interference intensity distribution information, and interference frequency distribution information.

[0155] The obtained electromagnetic interference frequency characteristic data is decomposed based on a preset frequency domain coupling balance algorithm. The frequency domain coupling balance algorithm is based on the Fourier transform principle, decomposes the electromagnetic interference frequency characteristic data in the frequency domain, and establishes a frequency domain coupling matrix. The frequency domain coupling matrix contains coupling strength coefficients, coupling phase information, and coupling frequency components.

[0156] According to the frequency domain coupling matrix, the impedance analysis of the signal transmission path of the FPC sensor module is performed using the multi-layer transmission line theory to establish an impedance distribution characteristic diagram. The impedance distribution characteristic diagram reflects the impedance distribution state of each layer of the signal line of the FPC sensor module, including impedance amplitude distribution, phase distribution and frequency response characteristics.

[0157] The harmonic components of the impedance distribution characteristic diagram are extracted by a harmonic analyzer to obtain the impedance harmonic component data. The impedance harmonic component data contains the amplitude and phase information of the fundamental component and each harmonic component. The impedance harmonic component data is calculated using an iterative optimization algorithm. The iterative optimization algorithm sets the convergence threshold to 0.01 and the maximum number of iterations to 100 to obtain the impedance matching optimization parameters.

[0158] According to the impedance matching optimization parameters, the signal transmission layer of the FPC sensor module is segmented and impedance compensation is calculated. In this calculation process, the signal transmission layer is divided into multiple compensation units, and the length of each compensation unit does not exceed 1 / 8 of the signal wavelength. Independent impedance compensation calculation is performed on each compensation unit to form an impedance compensation data set.

[0159] Electromagnetic field simulation software is used to analyze the impedance compensation data set, establish a three-dimensional electromagnetic field model, and calculate the electromagnetic field spatial distribution characteristics, which include electric field intensity distribution, magnetic field intensity distribution, and electromagnetic energy density distribution information.

[0160] The spatial distribution characteristics of the electromagnetic field are evaluated for performance, and the evaluation indicators include signal integrity indicators, electromagnetic compatibility indicators, and signal transmission loss indicators. The signal integrity indicator requires that the eye opening is greater than 70% and the jitter is less than 0.1UI; the electromagnetic compatibility indicator requires that the radiation emission intensity is less than -40dBm; the signal transmission loss indicator requires that the insertion loss is less than -3dB.

[0161] Based on the spatial distribution characteristics of the electromagnetic field and the performance evaluation results, the topology optimization algorithm is used to optimize and reconstruct the structure of the FPC sensor module. During the optimization and reconstruction process, the optimization objective function is set to minimize the signal transmission loss. The constraints include structural stability constraints, manufacturing process constraints, and cost constraints. The electrical performance optimization architecture is obtained by solving the optimization objective function.

[0162] This embodiment achieves high-precision electrical performance optimization of the FPC sensor module by performing impedance matching analysis on the anti-vibration optimization architecture. Electromagnetic interference analysis is performed based on the frequency domain coupling balance algorithm to accurately obtain the location and intensity distribution information of the interference source, and effectively identify key interference factors. The multi-layer transmission line theory is used to perform impedance analysis on the signal transmission path, and combined with harmonic component extraction and iterative optimization calculation, accurate compensation of impedance matching is achieved. Through the segmented impedance compensation calculation method, the signal transmission layer is divided into multiple compensation units to ensure the compensation accuracy and effect. Combined with electromagnetic field simulation analysis and performance evaluation, a complete optimization and evaluation system is established to achieve comprehensive optimization of key indicators such as signal integrity, electromagnetic compatibility and transmission loss. Finally, the structure is reconstructed through the topology optimization algorithm, which significantly improves the electrical performance and reliability of the FPC sensor module while ensuring structural stability and manufacturing process constraints.

[0163] In one embodiment, a solution is constructed based on the electrical performance optimization architecture and the anti-vibration optimization architecture combined with the initial molding information to obtain a corresponding module molding solution, including:

[0164] In the impedance distribution mapping process for the electrical performance optimization architecture, the impedance distribution of each area of ​​the FPC sensor module is obtained through finite element analysis. This process is based on the Maxwell equations, and the module surface is divided into m×n grid cells. The impedance value of each grid cell is recorded in the corresponding impedance density distribution matrix position to form a complete impedance distribution feature representation. The impedance distribution mapping must meet the consistency requirement of ±10% to ensure the quality of signal transmission.

[0165] In the stress field superposition analysis of the anti-vibration optimization architecture, the impedance density distribution matrix obtained is used as the basic data, and the stress superposition principle is used to calculate the stress distribution of the module in a vibration environment. The calculation process comprehensively considers the geometric nonlinear effect and material constitutive relationship of the bending area, establishes a stress tensor including normal stress and shear stress, evaluates the stress state of each node through the Von Mises criterion, and generates comprehensive stress distribution data. The boundary condition setting in the stress analysis must comply with the actual installation constraints.

[0166] For the multi-dimensional vibration modal decomposition of the comprehensive stress distribution data, the singular value decomposition method is used to extract the main deformation modes. By constructing the Hankel matrix and solving its eigenvalue equation, the eigenvector group reflecting the dynamic characteristics of the module structure is obtained. The energy contribution rate of the eigenvector must be greater than 85% to ensure the effectiveness of the modal decomposition. The extracted eigenvector must cover the frequency range of 0-2000Hz to meet the automotive-grade vibration requirements.

[0167] In the geometric topology reconstruction stage of the initial molding information, the module structure is reconstructed using a topology optimization algorithm based on the acquired feature vector group. The optimization process aims to maximize material utilization and stress uniformity, and gradually optimizes the unit distribution through the density method to form topology reconstruction data. The reconstruction process must ensure the continuity of key signal lines and meet the process requirements of a minimum feature size of ≥0.1mm.

[0168] In the multi-parameter coupling optimization stage, the process parameters of the topology reconstruction data are optimized. By establishing the mapping relationship between process parameters and molding quality, the response surface method is used to determine the optimal process parameter combination. The accuracy requirement of the bending angle control parameter is ±1°, the pressure control parameter range is 0.6-1.2MPa, the temperature control parameter is maintained in the range of 150-180℃, and the time control parameter is set in the range of 15-30s. The parameter optimization process needs to consider the robustness of the process window.

[0169] During the final module molding solution construction process, the optimized process parameter data is converted into specific process specifications and operation instructions. The solution content covers complete technical documents such as mold design requirements, process parameter settings, and quality control standards. The verification test of the molding solution must cover reliability items such as temperature cycling, vibration, and bending fatigue to ensure that the product performance meets automotive-grade requirements.

[0170] This embodiment uses the electrical performance optimization architecture to perform impedance distribution mapping processing, achieves accurate characterization of the impedance distribution on the surface of the FPC sensor module, and ensures that the signal transmission quality meets the consistency requirement of ±10%. Combined with the stress field superposition analysis of the anti-vibration optimization architecture, the mechanical response of the module in a vibration environment is comprehensively evaluated, effectively improving the structural reliability. A multi-dimensional vibration modal decomposition method is used to extract eigenvectors with an energy contribution rate greater than 85%, and dynamic characteristics optimization in the frequency range of 0-2000Hz is achieved to meet automotive-grade vibration requirements. Through geometric topology reconstruction and multi-parameter coupling optimization, the material utilization rate is maximized and the uniformity of stress distribution is achieved while ensuring that the minimum feature size is ≥0.1mm.

[0171] Reference Figure 2 As shown, the present invention provides a bending and forming device of a high-order computing power autonomous driving FPC sensor module, which is applied to a bending and forming method of a high-order computing power autonomous driving FPC sensor module of any one of the above items, comprising:

[0172] The acquisition module is used to obtain the physical characteristic information of the FPC sensor module, perform stress framework analysis on the physical characteristic information based on the preset Neo-Hookean hyperelastic algorithm, and obtain the corresponding initial characteristic data set;

[0173] An analysis module, the analysis module is used to perform bending analysis on the initial characteristic data set based on a preset moment balance algorithm to obtain corresponding bending process parameters;

[0174] The association module is used to perform a target multi-axis bending simulation analysis on the FPC sensor module based on the bending process parameters to obtain initial forming information;

[0175] A processing module, which is used to perform gradient cold pressing and vibration suppression analysis on the deformation characteristics of the initial forming information, and to construct an architecture to obtain a corresponding anti-vibration optimization architecture;

[0176] A control module, which is used to perform impedance matching analysis on the anti-vibration optimization architecture to obtain a corresponding electrical performance optimization architecture;

[0177] The execution module is used to construct a solution based on the electrical performance optimization architecture and the anti-vibration optimization architecture combined with the initial molding information to obtain the corresponding module molding solution.

[0178] The present invention provides a bending and forming device for a high-order computing power autonomous driving FPC sensor module. By introducing the Neo-Hookean hyperelastic algorithm to systematically analyze the physical characteristics of the FPC sensor module, and combining the torque balance algorithm for bending analysis, the deformation characteristics of the module under different stress states can be more accurately evaluated, thereby improving the accuracy of bending and forming, and providing a more reliable technical basis for module design and manufacturing. By performing gradient cold pressing and vibration suppression analysis on the initial forming information, the refined control of the module's anti-vibration performance is achieved, and the vibration stability problem of the sensor module in the vehicle environment is effectively solved. The electrical performance is optimized based on the impedance matching analysis technology to ensure that the module can maintain stable signal transmission quality under different working conditions, thereby improving the overall reliability of the system. By comprehensively analyzing the anti-vibration optimization architecture and the electrical performance optimization architecture, a more reasonable module forming scheme is formulated, thereby effectively improving the performance of the sensor module in practical applications. At the same time, by considering the application characteristics of the module in different vehicle environments, the forming process parameters can be flexibly adjusted, so that the module can better adapt to complex and changeable application scenarios.

[0179] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0180] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A bending forming method for a high-order computing power autonomous driving FPC sensor module, characterized in that: include: Acquire physical property information of the FPC sensor module, perform stress framework analysis on the physical property information based on a preset Neo-Hookean hyperelastic algorithm, and obtain a corresponding initial property data set; Performing bending analysis on the initial characteristic data set based on a preset moment balance algorithm to obtain corresponding bending process parameters; Performing a target multi-axis bending simulation analysis on the FPC sensor module based on the bending process parameters to obtain initial forming information; Performing gradient cold pressing and vibration suppression analysis on the deformation characteristics of the initial forming information, and constructing an architecture to obtain a corresponding anti-vibration optimized architecture; Performing impedance matching analysis on the anti-vibration optimization architecture to obtain a corresponding electrical performance optimization architecture; Based on the electrical performance optimization architecture and the anti-vibration optimization architecture combined with the initial molding information, a solution is constructed to obtain a corresponding module molding solution; The physical property information of the FPC sensor module is obtained, and a stress framework analysis is performed on the physical property information based on a preset Neo-Hookean hyperelastic algorithm to obtain a corresponding initial property data set, including: The conductive layer thickness, insulating layer thickness and covering layer thickness of the FPC sensor module are measured and collected to obtain interlayer structure parameters; Conducting copper foil conductivity, dielectric constant and shear modulus testing on the FPC sensor module to obtain material characteristic parameters; A Neo-Hookean hyperelastic calculation matrix is ​​constructed according to the interlayer structure parameters and the material characteristic parameters to obtain a stress calculation matrix; Performing piecewise linear interpolation calculation on the stress calculation matrix to obtain multi-dimensional stress distribution data; Calculate the stress response function according to the multi-dimensional stress distribution data to obtain material deformation rate data; Performing hyperelastic coefficient calibration on the material deformation rate data to obtain a calibration coefficient; Performing stress-strain curve fitting on the calibration coefficient to obtain characteristic stress data; Performing Neo-Hookean parameter calibration on the characteristic stress data to obtain the initial characteristic data set; The bending analysis of the initial characteristic data set based on the preset moment balance algorithm to obtain corresponding bending process parameters includes: Extracting material elastic modulus parameters and Poisson's ratio parameters from the initial characteristic data set to obtain corresponding material characteristic parameters; Decomposing the lateral moment and longitudinal moment of the FPC sensor module according to the material characteristic parameters to obtain corresponding moment component data; Performing lateral moment balance algorithm configuration processing on the moment component data to obtain corresponding lateral bending force parameters; Performing longitudinal moment balance algorithm configuration processing on the moment component data to obtain corresponding longitudinal bending force parameters; Performing bending force synthesis processing on the FPC sensor module according to the transverse bending force parameter and the longitudinal bending force parameter to obtain corresponding resultant force data; Performing moment balance verification processing on the resultant force data to obtain corresponding equilibrium state parameters; Calculating the bending force of the FPC sensor module according to the equilibrium state parameters to obtain corresponding force control parameters; Performing bending angle mapping processing on the force control parameter to obtain a corresponding angle control parameter; Calculating the bending rate of the FPC sensor module according to the angle control parameter to obtain a corresponding rate control parameter; The force control parameter, the angle control parameter and the speed control parameter are integrated to obtain the bending process parameter.

2. The bending forming method of the high-order computing power autonomous driving FPC sensor module according to claim 1 is characterized in that: The target multi-axis bending simulation analysis is performed on the FPC sensor module based on the bending process parameters to obtain initial forming information, including: Performing boundary surface subdivision processing on the FPC sensor module according to the bending process parameters to obtain corresponding grid topology data; Performing non-uniform interpolation calculation on the grid topology data to obtain a corresponding deformation control point coordinate set; Performing quaternion rotation interpolation operation based on the deformation control point coordinate set to obtain a corresponding spatial rotation trajectory; Performing discrete curvature analysis on the spatial rotation trajectory to obtain a corresponding stress distribution function; Solving and analyzing the deformation energy of the stress distribution function in each axial bending process to obtain the corresponding energy change trend; Performing bending trajectory curve fitting on the energy change trend to obtain a corresponding motion parameter set; The motion parameter set, the energy variation trend and the space rotation trajectory are subjected to a forming analysis to obtain corresponding initial forming information.

3. The bending forming method of the high-order computing power autonomous driving FPC sensor module according to claim 1 is characterized in that: The deformation characteristics of the initial forming information are subjected to gradient cold pressing and vibration suppression analysis, and the architecture is constructed to obtain a corresponding anti-vibration optimization architecture, including: Performing multi-dimensional tensor decomposition on the initial forming information to obtain a corresponding deformation characteristic matrix; Calculating the node stress distribution of the deformation characteristic matrix to obtain corresponding stress distribution data; Performing gradient pressure optimization calculation on the stress distribution data to obtain corresponding gradient pressure data; Perform stress relaxation calculation according to the gradient pressure data to obtain corresponding cold pressing characteristic parameters; Performing Rayleigh damping modal analysis according to the cold pressing characteristic parameters and the deformation characteristic matrix to obtain corresponding natural frequency data; Performing vibration response optimization on the natural frequency data to obtain corresponding vibration suppression data; Performing constrained optimization on the vibration suppression data to obtain corresponding anti-vibration architecture parameters; The architecture is constructed according to the anti-vibration architecture parameters, the gradient pressure data and the vibration suppression data to obtain the anti-vibration optimized architecture.

4. The bending forming method of the high-order computing power autonomous driving FPC sensor module according to claim 3 is characterized in that: The Rayleigh damping modal analysis is performed according to the cold pressing characteristic parameters and the deformation characteristic matrix to obtain corresponding natural frequency data, including: Performing mass distribution calculation on the deformation characteristic matrix to obtain a corresponding mass matrix; Perform elastic modulus analysis according to the cold pressing characteristic parameters to obtain a corresponding stiffness matrix; Performing a main frequency calculation on the mass matrix to obtain a first control frequency; Performing frequency response calculation on the stiffness matrix to obtain a second control frequency; Calculating the damping ratio according to the first control frequency and the second control frequency to obtain a corresponding damping coefficient; Linearly combining the mass matrix and the stiffness matrix, and constructing a Rayleigh damping matrix according to the damping coefficient to obtain a system damping matrix; The frequency is solved according to the system damping matrix to obtain the natural frequency data.

5. The bending forming method of the high-order computing power autonomous driving FPC sensor module according to claim 1 is characterized in that: The impedance matching analysis of the anti-vibration optimization architecture is performed to obtain a corresponding electrical performance optimization architecture, including: Performing electromagnetic interference analysis on the anti-vibration optimization architecture to obtain corresponding electromagnetic interference frequency characteristic data; Decomposing and calculating the electromagnetic interference frequency characteristic data based on a preset frequency domain coupling balance algorithm to obtain a corresponding frequency domain coupling matrix; Performing multi-layer impedance analysis of the signal transmission path of the FPC sensor module according to the frequency domain coupling matrix to obtain a corresponding impedance distribution characteristic diagram; Extracting harmonic components from the impedance distribution characteristic diagram to obtain corresponding impedance harmonic component data; Iteratively calculating the impedance harmonic component data to obtain corresponding impedance matching optimization parameters; Performing segmented impedance compensation calculation on the signal transmission layer of the FPC sensor module according to the impedance matching optimization parameters to obtain a corresponding impedance compensation data set; Performing electromagnetic field simulation analysis on the impedance compensation data set to obtain corresponding electromagnetic field spatial distribution characteristics; Performing a performance evaluation on the electromagnetic field spatial distribution characteristics to obtain a corresponding electrical performance evaluation result; The electrical performance optimization architecture is obtained by performing optimization and reconstruction according to the electromagnetic field spatial distribution characteristics and the electrical performance evaluation results.

6. The bending forming method of the high-order computing power autonomous driving FPC sensor module according to claim 1 is characterized in that: The scheme construction based on the electrical performance optimization architecture and the anti-vibration optimization architecture combined with the initial molding information to obtain a corresponding module molding scheme includes: Performing impedance distribution mapping processing on the electrical performance optimization architecture to obtain an impedance density distribution matrix; Performing stress field superposition analysis on the anti-vibration optimization architecture according to the impedance density distribution matrix to obtain comprehensive stress distribution data; Performing multi-dimensional vibration modal decomposition on the comprehensive stress distribution data to obtain a set of eigenvectors; Performing geometric topological reconstruction on the initial forming information according to the feature vector group to obtain topological reconstruction data; Performing multi-parameter coupling optimization on the topology reconstruction data to obtain process parameter data, wherein the process parameter data includes a bending angle control parameter, a pressure control parameter, a temperature control parameter and a time control parameter; A scheme is constructed according to the process parameter data to obtain the module molding scheme.

7. A bending and forming device for a high-level computing power autonomous driving FPC sensor module, characterized in that: The bending forming method of the high-order computing power autonomous driving FPC sensor module applied to any one of claims 1 to 6 above comprises: An acquisition module, the acquisition module is used to obtain physical property information of the FPC sensor module, perform stress framework analysis on the physical property information based on a preset Neo-Hookean hyperelastic algorithm, and obtain a corresponding initial property data set; An analysis module, the analysis module is used to perform bending analysis on the initial characteristic data set based on a preset moment balance algorithm to obtain corresponding bending process parameters; An association module, the association module is used to perform a target multi-axis bending simulation analysis on the FPC sensor module based on the bending process parameters to obtain initial forming information; A processing module, the processing module is used to perform gradient cold pressing and vibration suppression analysis on the deformation characteristics of the initial forming information, and to construct an architecture to obtain a corresponding anti-vibration optimization architecture; A control module, the control module is used to perform impedance matching analysis on the anti-vibration optimization architecture to obtain a corresponding electrical performance optimization architecture; An execution module, the execution module is used to construct a solution based on the electrical performance optimization architecture and the anti-vibration optimization architecture in combination with the initial molding information to obtain a corresponding module molding solution; The physical property information of the FPC sensor module is obtained, and a stress framework analysis is performed on the physical property information based on a preset Neo-Hookean hyperelastic algorithm to obtain a corresponding initial property data set, including: The conductive layer thickness, insulating layer thickness and covering layer thickness of the FPC sensor module are measured and collected to obtain interlayer structure parameters; Conducting copper foil conductivity, dielectric constant and shear modulus testing on the FPC sensor module to obtain material characteristic parameters; A Neo-Hookean hyperelastic calculation matrix is ​​constructed according to the interlayer structure parameters and the material characteristic parameters to obtain a stress calculation matrix; Performing piecewise linear interpolation calculation on the stress calculation matrix to obtain multi-dimensional stress distribution data; Calculate the stress response function according to the multi-dimensional stress distribution data to obtain material deformation rate data; Performing hyperelastic coefficient calibration on the material deformation rate data to obtain a calibration coefficient; Performing stress-strain curve fitting on the calibration coefficient to obtain characteristic stress data; Performing Neo-Hookean parameter calibration on the characteristic stress data to obtain the initial characteristic data set; The bending analysis of the initial characteristic data set based on the preset moment balance algorithm to obtain corresponding bending process parameters includes: Extracting material elastic modulus parameters and Poisson's ratio parameters from the initial characteristic data set to obtain corresponding material characteristic parameters; Decomposing the lateral moment and longitudinal moment of the FPC sensor module according to the material characteristic parameters to obtain corresponding moment component data; Performing lateral moment balance algorithm configuration processing on the moment component data to obtain corresponding lateral bending force parameters; Performing longitudinal moment balance algorithm configuration processing on the moment component data to obtain corresponding longitudinal bending force parameters; Performing bending force synthesis processing on the FPC sensor module according to the transverse bending force parameter and the longitudinal bending force parameter to obtain corresponding resultant force data; Performing moment balance verification processing on the resultant force data to obtain corresponding equilibrium state parameters; Calculating the bending force of the FPC sensor module according to the equilibrium state parameters to obtain corresponding force control parameters; Performing bending angle mapping processing on the force control parameter to obtain a corresponding angle control parameter; Calculating the bending rate of the FPC sensor module according to the angle control parameter to obtain a corresponding rate control parameter; The force control parameter, the angle control parameter and the speed control parameter are integrated to obtain the bending process parameter.

Images