Assembly stress assessment method for PCBA (printed circuit board assembly) of automobile motor controller

Abstract

The invention relates to an assembly stress assessment method for an automobile motor controller PCBA, and the method comprises the steps: obtaining a PCB bare board model through modeling and simplification, and obtaining a stiffness matrix through finite element modeling analysis; then combining the limit tolerance of the mounting point, analyzing strain distribution by using a Monte Carlo method, calculating a Proba value and identifying an extreme value working condition; performing limit strain simulation based on the most dangerous working condition, and determining a high-strain region and component distribution in the region; and finally, calculating the failure probability of the component by combining the Proba value and the component distribution, completing the reliability evaluation of the assembly stress, and further guiding the Layout optimization of the PCB. According to the method, accurate simulation of assembly tolerance randomness, visual positioning of a high-strain area and device distribution and quantitative calculation of the failure probability can be achieved, the problems of welding spot cracks, component pin breakage and the like in the assembly process are effectively solved, the PCBA assembly reliability and the motor controller working stability are finally improved, and a clear and promising guidance basis is provided for PCB Layout optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicle electronic assembly, and particularly relates to a stress evaluation method for a printed circuit board assembly (PCBA) of a new energy vehicle motor controller in an assembly process. BACKGROUND

[0002] The motor controller of a new energy vehicle is one of the core components of a vehicle power system. As the "nerve center" of the motor controller, the printed circuit board assembly (PCBA) directly determines the working stability of the motor controller, and further affects the power performance, safety and service life of the vehicle. With the continuous development of electronic integration technology, the PCBA of the motor controller has gradually evolved towards the integration of driving and control, and the integration degree is continuously improved. Therefore, the precision and reliability requirements of the assembly process are becoming increasingly stringent.

[0003] Generally, the PCBA assembly is directly or indirectly fixed on a shielding plate, an electronic control box or an IGBT power module plastic shell through bolts. However, in actual assembly scenarios, due to the differences in the machining precision of parts, there is inevitably a certain height difference in the mounting surface of the printed circuit board (PCB). At the same time, the fluctuation of process parameters such as bolt tightening torque and assembly sequence in the assembly process can easily cause mechanical strain on the PCB. If the control of the strain during the assembly process is not proper, it will directly cause failure problems such as weld crack, component pin breakage and circuit layer peeling, which seriously affects the normal operation of the motor controller.

[0004] At present, the evaluation method of the assembly stress of the PCBA mainly relies on finite element simulation technology, that is, the stress is calculated by inputting fixed parameters such as part design size tolerance. However, this method has significant defects, which are as follows:

[0005] In the actual assembly process, the parameters such as bolt displacement tolerance, tightening torque and actual size deviation of parts have random distribution characteristics. However, the traditional fixed parameter simulation method cannot reflect the influence of these random disturbances on the assembly stress, which further causes a large deviation between the evaluation results and the actual situation.

[0006] At the same time, the traditional method can only carry out stress analysis based on a single or fixed tolerance combination. It cannot locate the most dangerous tolerance combination that causes the maximum strain (only the stress value can be output), cannot quantify the probability of exceeding the device strain limit and the failure probability, and cannot intuitively present the corresponding relationship between the high strain area and the device distribution. Therefore, the potential failure risk is difficult to be accurately identified, and the final evaluation results are also difficult to be directly used to guide the Layout optimization design of the PCB.

[0007] Therefore, an automobile motor controller PCBA assembly stress evaluation method is urgently needed to quickly obtain accurate and practical PCBA assembly stress evaluation results and provide effective support for motor controller reliability design. SUMMARY

[0008] The application aims at the corresponding deficiencies of the prior art, and provides an automobile motor controller PCBA assembly stress evaluation method.

[0009] The application is realized by the following scheme:

[0010] An automobile motor controller PCBA assembly stress evaluation method comprises the following steps:

[0011] 1) A PCBA three-dimensional model is established, and PCBA components are simplified to obtain a PCB bare board model;

[0012] 2) Finite element modeling is performed on the PCB bare board model, and the PCB bare board model is given corresponding material properties to obtain a stiffness matrix;

[0013] 3) The limit tolerance range of each mounting point of the PCBA is determined, and the stiffness matrix obtained in step 2) is combined to analyze the PCB strain distribution under random disturbance by using the Monte Carlo method, calculate the Proba value, and identify the most dangerous working condition corresponding to the maximum principal strain and the safest working condition corresponding to the minimum principal strain;

[0014] 4) According to the tolerance combination corresponding to the most dangerous working condition identified in step 3), limit strain simulation is performed on the PCB board to obtain a strain distribution cloud diagram of the PCB bare board, and further determine the high strain area and the component distribution state in the high strain area;

[0015] 5) According to the Proba value obtained in step 3) and the component distribution state of the high strain area determined in step 4), the failure probability of each component is calculated, and the assembly stress reliability evaluation of the PCBA under the design tolerance is completed.

[0016] Preferably, in step 2), the stiffness matrix is obtained in the following manner:

[0017] 2-1) Discretize the bare PCB model using finite element mesh, employing first-order quadrilateral elements and setting the global mesh size;

[0018] 2-2) Assign anisotropic material properties to the bare PCB board, and define shell unit properties and set shell unit thickness; the material properties include density, Young's modulus and Poisson's ratio;

[0019] 2-3) Establish RBE2 elements at each mounting point of PCBA, load displacement at the center node of each RBE2 element in sequence, and extract the stiffness matrix corresponding to each displacement loading point.

[0020] Preferably, in step 3), the limit tolerance range of each mounting point of the PCBA is determined, and combined with the stiffness matrix, the Monte Carlo method is used to analyze the PCB strain distribution under random disturbance, calculate the Proba value, and simultaneously identify the most dangerous working condition corresponding to the maximum principal strain and the safest working condition corresponding to the minimum principal strain. Specific steps include:

[0021] 3-1) Calculate the limit tolerance range of each mounting point of the PCBA by using the actual dimension chain of each component of the product;

[0022] 3-2) Use the Randn() function to generate standard normally distributed random numbers and construct a random perturbation vector, which satisfies the formula Where y is a random perturbation vector, a is a scaling factor, and , This represents the disturbance variance.

[0023] 3-3) Set a filtering threshold, if Then the random perturbation vector is stored as a sample set;

[0024] 3-4) Based on the total amount of the sample set and the random perturbation vector, calculate the proportion of the sample set in the random perturbation vector to obtain the Proba value, which represents the probability that the random perturbation falls within the sensitive region. The calculation formula is as follows:

[0025]

[0026]

[0027] In the formula, Let be the probability that a random disturbance falls within the sensitive region. for , As the filtering threshold, This represents the limit tolerance range for each mounting point on the PCBA.

[0028] 3-5) Set the number of Monte Carlo simulations to N, randomly select N samples from the sample set, generate M random vectors to form an N×M dimension RT matrix, and use the following formula to linearly superimpose the stiffness tensors to obtain the equivalent stiffness tensor:

[0029]

[0030] In the formula, To simulate the equivalent stiffness tensor affected by tolerances, The number of PCBA bolt mounting points. The random perturbation weights are drawn from the sample set. Let K be the stiffness matrix of the k-th mounting point on the PCB.

[0031] 3-6) Based on the equivalent stiffness tensor, use the two-dimensional principal strain formula to calculate the extreme values ​​of principal strain in each simulation; finally, identify the most dangerous working condition corresponding to the maximum principal strain and the safest working condition corresponding to the minimum principal strain, and record the tolerance combination corresponding to the most dangerous working condition.

[0032] Preferably, in step 4), based on the tolerance combination corresponding to the most dangerous working condition identified, the ultimate strain of the PCB board is simulated to obtain the strain distribution cloud map of the bare PCB board, and the high strain region and the component distribution state within the high strain region are further determined. The specific steps include:

[0033] 4-1) Load the tolerance combination corresponding to the most dangerous working condition identified into the center node of the RBE2 element of the bare PCB model, and use OptiStruct to perform extreme strain simulation to obtain the extreme strain distribution cloud map of the PCB.

[0034] 4-2) Overlay the PCBA Layout device diagram with the strain distribution cloud map to obtain the location range of the high strain region, and at the same time determine the distribution state of the components within the high strain region.

[0035] Preferably, in step 4-2), the high strain region is obtained as follows: a material allowable strain threshold is set. If the strain value of a certain area of ​​the PCB board exceeds the strain threshold, then the area is a high strain region.

[0036] Preferably, the distribution state of the components within the high strain region includes all components being in the high strain region, some components being in the high strain region, and the relative orientation of the components to the strain components.

[0037] Preferably, in step 5), based on the Proba value and the component distribution in the high-strain region, the strain exceedance probability of each component in the high-strain region is obtained, and the failure probability of each component is calculated to complete the assembly stress reliability assessment of the PCBA under design tolerances, so as to guide the layout optimization design of the PCB board. Specific steps include:

[0038] 5-1) Extract the strain extrema of all nodes on the PCB, and combine them with the range of high strain regions to select the strain extrema of each node within the high strain region.

[0039] 5-2) Based on the Proba value and the component distribution in the high-strain region, calculate the strain exceedance probability and failure probability of each component. The formula for calculating the failure probability is as follows:

[0040]

[0041] In the formula, This represents the probability of component failure. This represents the total number of nodes in the region where the component is located. This represents the number of nodes in the region where the strain exceeds the strain threshold. This represents the probability that a random disturbance falls within the sensitive region.

[0042] 5-3) Set a reliability threshold, summarize the failure probability and high strain region distribution of all components, and complete the assembly stress reliability assessment of the PCBA under design tolerances:

[0043] If the failure probability of all components is lower than the preset reliability threshold, then the assembly stress reliability of the PCBA meets the design requirements.

[0044] If the failure probability of a component is higher than the preset reliability threshold, the assembly stress reliability of the PCBA does not meet the design requirements, and the PCB layout design needs to be optimized based on the distribution state of the high strain region.

[0045] The beneficial effects of this invention are as follows:

[0046] (1) This invention effectively solves the shortcomings of traditional evaluation methods that rely on fixed parameter simulation and cannot reflect random disturbances such as bolt displacement tolerances and component size deviations in actual assembly. It not only accurately identifies extreme conditions but also quantifies the failure probability of each component, overcoming the limitations of traditional methods that only output stress values ​​and are difficult to locate potential failure risks. This provides clear guidance for subsequent PCB layout optimization design, reducing failures such as solder joint cracks and component pin breaks during assembly, significantly improving the assembly reliability of PCBAs and the operational stability and service life of motor controllers.

[0047] (2) This invention uses first-order quadrilateral elements to discretize the PCB bare board model, avoiding stress calculation deviations caused by improper mesh type selection; by assigning anisotropic material properties, it accurately simulates actual mechanical characteristics, solving the problem of the homogeneity assumption in traditional modeling not matching the actual material properties; by establishing RBE2 elements at each mounting point of the PCBA and extracting the stiffness matrix by loading displacement, it can realistically reproduce the rigid fixing constraint effect of bolts on the PCB, ensuring that the obtained stiffness matrix accurately reflects the mechanical response of the PCB under assembly constraints, thus providing reliable basic data support for subsequent Monte Carlo analysis of random disturbances.

[0048] (3) This invention employs the Monte Carlo method to achieve precise quantification of the randomness of assembly tolerances and efficient location of hazardous working conditions. Based on the actual dimensional chain of product components, the limit tolerance range is calculated, effectively ensuring that the tolerance input conforms to actual production and processing precision. By designing a random disturbance vector that conforms to the characteristics of a normal distribution, the random distribution of tolerances in actual assembly can be accurately simulated. By calculating the probability that random disturbances fall within the sensitive range, a crucial quantitative basis is provided for subsequent component failure probability calculations, filling the technical gap where traditional methods cannot quantify the probability of tolerance fluctuation risks. Through linear superposition of stiffness tensors and calculation using the two-dimensional principal strain formula, the working conditions and tolerance combinations corresponding to the maximum / minimum principal strains can be accurately located, providing the "worst-case" input for subsequent limit strain simulations and avoiding risk omissions caused by incomplete working condition coverage in traditional methods.

[0049] (4) This invention conducts extreme strain simulation based on the tolerance combination of the most dangerous working conditions, which can accurately focus on the stress distribution state of the worst scenario in the assembly process; by overlaying and comparing the extreme strain distribution cloud map of PCB with the PCBA layout device diagram, the location range of high strain area and the distribution state of components in the area can be directly identified, providing a clear spatial location basis for subsequent failure probability calculation, and greatly improving the accuracy of potential failure risk location.

[0050] (5) This invention accurately determines high-strain regions by setting a permissible strain threshold for materials, thereby unifying the criteria for judging high-strain regions and ensuring that the identification results of high-strain regions remain consistent across different evaluators and project scenarios. This improves the consistency and repeatability of the evaluation results and avoids evaluation deviations caused by inconsistent judgment criteria.

[0051] (6) This invention refines the distinction between the failure risks corresponding to the distribution states of components, clarifying the risk difference between components "partially in the high strain region" and "entirely in the high strain region" (entirely in the region carries a higher risk), as well as the stress difference between components "parallel to the strain components" and "perpendicular to the strain components" (parallel state carries a higher risk). Compared to traditional coarse-grained assessments, this invention provides a more detailed risk weighting basis for calculating the probability of component failure, making the assessment more closely aligned with actual stress scenarios and effectively avoiding misjudgments of risk caused by neglecting the relationship between strain direction and component position.

[0052] (7) This invention combines Proba value with the local node strain exceeding the limit in the high strain zone to accurately quantify the failure risk of individual components; by setting a reliability threshold, it can avoid cost waste caused by over-design and accurately locate the components that need optimization; finally, the method of this invention can clarify the specific direction of PCB layout optimization, making optimization more targeted, avoiding blind adjustments, greatly improving optimization efficiency and effect, and ensuring the reliability of PCBA in mass production assembly.

[0053] Definitions:

[0054] RBE2 element (Rigid Body Element 2): In this invention, it refers to a special element used in the field of finite element analysis (FEA) to simulate rigid connection constraints. Its core function is to achieve displacement synchronization in a specific area through master-slave node coupling, thereby reproducing the rigid fixed or rigid connection state in engineering. It is widely used in mainstream finite element simulation software such as HyperMesh and OptiStruct, for example, in scenarios such as stress assessment of new energy vehicle PCBA assembly and mechanical structure constraint modeling.

[0055] FR-4: Flame Retardant Type 4, is one of the core categories of glass fiber reinforced epoxy resin-based copper clad laminate (CCL), and is also the mainstream substrate for bare PCBs in the PCBA (Printed Circuit Board Assembly) of new energy vehicle motor controllers.

[0056] PCBA: Printed Circuit Board Assembly, refers to the finished assembly formed by the assembly and soldering of components on a printed circuit board (PCB). Attached Figure Description

[0057] Figure 1 This is a flowchart of an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the three-dimensional model of the PCBA after processing in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the finite element model for extracting the PCB stiffness matrix in an embodiment of the present invention;

[0060] Figure 4 This is a contour plot of the EZZ strain components of the PCB in an embodiment of the present invention;

[0061] Figure 5 This is a flowchart of the Monte Carlo method in an embodiment of the present invention;

[0062] Figure 6 This is a simulation cloud diagram of the ultimate strain in an embodiment of the present invention;

[0063] Figure 7 This is a diagram showing the distribution of components for the ultimate strain simulation in an embodiment of the present invention.

[0064] Figure 8 This is a diagram showing the distribution of extreme strain components on the PCB in an embodiment of the present invention.

[0065] Figure 9 This is a schematic diagram of component failure risk assessment in an embodiment of the present invention;

[0066] Figure 10 This represents the component failure probability results in the embodiments of the present invention;

[0067] Figure 11 This is a flowchart of the present invention; Figure 12 This is a flowchart of the present invention; Figure 13 This is a cloud map of the EXY strain components of the PCB in an embodiment of the present invention. Detailed Implementation

[0068] like Figures 1 to 13 As shown, an assembly stress assessment method for automotive motor controller PCBA includes the following steps:

[0069] 1) Based on the design drawings of the automotive motor controller PCBA (including parameters such as PCB substrate size, component packaging, and bolt mounting hole positions), establish a complete 3D model of the PCBA. This model includes all components such as the PCB substrate, resistors, capacitors, and chips, as well as the bolt mounting hole structure.

[0070] To eliminate the interference of redundant structures on subsequent mechanical analysis, the established 3D model of the PCBA (PCBA component) is simplified:

[0071] Remove unnecessary components such as resistors, capacitors, and chips, and retain only the PCB substrate and the core structure with bolt mounting holes;

[0072] Simplify non-critical features of the PCB substrate (such as small-diameter non-functional holes, edge chamfers, surface scratches, and other detailed structures that do not affect the overall rigidity).

[0073] The PCB substrate is divided into regions according to hardware layout rules to ensure that subsequent finite element modeling focuses on stress-sensitive areas. In this embodiment, the hardware layout rules are specifically: no components are placed within a 6mm radius of the bolt hole center point and within a 5mm radius of the pad outer edge. The simplified bare PCB model is as follows: Figure 2 As shown.

[0074] 2) Perform finite element modeling on the bare PCB model, assign corresponding material properties to the bare PCB model, and obtain the stiffness matrix; wherein, the stiffness matrix is ​​obtained by means of:

[0075] 2-1) The PCB bare board model was discretized using finite element meshing software HyperMesh, with first-order quadrilateral elements and a global mesh size set. In this embodiment, the global mesh size was 3mm.

[0076] 2-2) Assign anisotropic material properties to the bare PCB board FR-4, including density, Young's modulus (elastic modulus) and Poisson's ratio. All of the above material properties are determined based on material test data. At the same time, define shell unit properties, adopt the Psolid unit type, and set the shell unit thickness T=2mm (consistent with the actual PCB substrate thickness) to accurately simulate the bending and shear mechanical properties of the bare PCB board.

[0077] 2-3) Establish RBE2 elements (rigid connection elements) at each mounting point of the PCBA to simulate the fixing constraint of bolts on the bare PCB board; sequentially apply a 1mm displacement load in the Z direction to the center node of the RBE2 element at each mounting point (i.e., the center node of the RBE2 element moves 1mm along the Z-axis), and use full constraints (constraining all translational and rotational degrees of freedom) at the remaining bolt mounting points to simulate the PCB deformation caused by the bolt height difference during assembly. In this embodiment, the X / Y direction is the PCB board plane direction by default, and the Z direction is the axial direction of the PCB bolt mounting point.

[0078] Static stiffness analysis was performed using the OptiStruct solver, extracting the strain stiffness matrix of each mounting point in the ZZ, YZ, and XZ directions along either Z1 or Z2. (Z1 and Z2 correspond to the lower or upper surface of the shell element, respectively). The number of stiffness matrices (K) is the same as the number of bolt mounting points (M), and is directly related to the PCB structure geometry and material properties.

[0079] The stiffness matrices of each installation point are summarized and saved to an Excel worksheet. The stiffness matrices of different installation points are stored in separate sheets named S1, S2, ..., SM. Each sheet contains strain stiffness matrices in the ZZ, YZ, and XZ directions. The finite element model extracted from the stiffness matrix is ​​as follows. Figure 3 As shown.

[0080] In this embodiment, the strain data in each direction are extracted using HyperView post-processing software, as detailed below:

[0081] ① XZ strain: the normal strain of the PCB board in the X direction. (Linear strain), representing the rate of change of length along the X direction (positive for tensile deformation, negative for compressive deformation), is calculated as follows: , where u is the displacement component in the X direction.

[0082] ② YZ strain: the normal strain of the PCB board in the Y direction. (Linear strain), representing the rate of change of length along the Y direction, is calculated as follows: ,in, for Displacement components in the direction.

[0083] ③ ZZ strain: that is, the shear strain of the PCB board in the XY plane (Tensor shear strain), i.e., angular distortion in the XY plane. Among them, engineering shear strain ( ) is tensor shear strain ( 2 times, that is HyperView outputs tensor shear strain by default, and the calculation formula is:

[0084]

[0085] In this embodiment, the strain component cloud map is as follows: Figure 4 , 12 As shown in Figure 13.

[0086] 3) Determine the limit tolerance range of each mounting point of the PCBA (i.e., the corresponding bolt holes on the PCB substrate), and combine it with the stiffness matrix obtained in step 2) to analyze the PCB strain distribution under random disturbance using the Monte Carlo method, calculate the Proba value, and identify extreme conditions. Specific steps include:

[0087] 3-1) Calculate the limit tolerance range MaxTol = ±Hmm for each mounting point of the PCBA using the actual dimensional chain of each component. The actual tolerance of each mounting point of the PCBA (e.g., bolt displacement tolerance, i.e., the height tolerance of the fixing point) needs to be determined experimentally or through statistical sample data. Furthermore, the bolt displacement tolerance is assumed to follow a normal distribution, conforming to the principle of "…". "Principle; In this embodiment, the limit tolerance range of each mounting point of the PCBA is determined by the dimensional chain: MaxTol=±0.1mm.

[0088] 3-2) Set the number of Monte Carlo simulations N=1000, use the Randn() function to generate standard normally distributed random numbers, and construct a random perturbation vector to simulate random deviations caused by manufacturing tolerances. The random perturbation vector follows a normal distribution: Where y is a random perturbation vector, a is a scaling factor, and , To determine the variance of the disturbance, ensure that 99.7% of the disturbances fall within the range [-MaxTol, MaxTol], conforming to a normal distribution. Coverage characteristics.

[0089] The expression for the random perturbation vector is: This indicates the generation of a column vector of length 1000*N. In this implementation, mm, perturbation variance The length of the random vector y is .

[0090] 3-3) Set a filtering threshold sel, where, In this embodiment ,

[0091] like Then, the random perturbation vector is selected and stored as a sample set d1, i.e. ;

[0092] 3-4) Based on the total amount of the sample set d1 and the random perturbation vector y, calculate the proportion of the sample set d1 in the random perturbation vector y, and obtain the Proba value, which represents the probability that the random perturbation falls within the sensitive interval. The calculation formula is as follows:

[0093]

[0094] In the formula, Let be the probability that a random disturbance falls within the sensitive interval (|y|>sel). for , As the filtering threshold, This represents the limit tolerance range for each mounting point on the PCBA.

[0095] Since y follows a normal distribution ,and Therefore, the standard deviation can be determined. Standardize the above inequality:

[0096]

[0097] Where Z is a standard normal random variable.

[0098] Theoretical calculations are performed using the standard normal distribution table:

[0099] That is, obtained from theoretical calculations ,in, It is the standard normal cumulative distribution function.

[0100] In this embodiment, 1000*N random perturbation samples are generated, and the proportion of sample set d1 in the total sample is calculated. The result is consistent with the theoretical... The values ​​are consistent.

[0101] 3-5) Set the number of Monte Carlo simulations, and use the randsample() function to randomly select N samples from the sample set d1 to generate M random vectors (R1, R2, ..., RM). The number of random vectors is the same as the number of PCBA bolt mounting points, forming an N×M dimension RT matrix (physically meaning: a combination of random disturbances of M mounting points in N simulations).

[0102] Let the Monte Carlo loop variable i range from 1 to N. Assign the M elements of the i-th row of the RT matrix to the random perturbation weights A1, A2, ..., AM, respectively. Then, use the following formula to perform linear superposition of the stiffness tensors to obtain the equivalent stiffness tensor simulating the influence of tolerances:

[0103]

[0104] In the formula, To simulate the equivalent stiffness tensor affected by tolerances, The number of PCBA bolt mounting points. The random perturbation weights are drawn from the sample set. Let K be the stiffness matrix of the k-th mounting point on the PCB.

[0105] 3-6) Extract from the above equivalent stiffness tensor , , Three strain data sets were used to calculate the extreme values ​​of the principal strain in each simulation using the two-dimensional principal strain formula:

[0106]

[0107] The maximum principal strain of each simulation is stored in the EEMAX matrix (dimension TN, T is the number of PCB unit nodes, physical meaning: the set of maximum principal strains of T nodes in N simulations), and the minimum principal strain is stored in the EEMIN matrix (dimension TN, physical meaning: the set of minimum principal strains of T nodes in N simulations). The extreme values ​​of all units on the PCB in all simulations are calculated, including the maximum value (MaxMaxE) and minimum value (MinMaxE) of the maximum principal strain, and the maximum value (MaxMinE) and minimum value (MinMinE) of the minimum principal strain. Then, the node positions of the global maximum principal strain (Tmax1) and the global minimum principal strain (Tmin1) are located, and the corresponding tolerance combinations are extracted from the RT matrix through the position index, which are respectively used as the most dangerous working condition (the tolerance combination RoutmaxE corresponding to Tmax1) and the safest working condition (the tolerance combination RoutminE corresponding to Tmin1).

[0108] In this embodiment, the structure data of the correlation matrix is ​​shown in Table 1 below:

[0109] Table 1

[0110]

[0111] 4) Based on the tolerance combination corresponding to the most dangerous working condition identified in step 3), determine the high-strain region of the PCB and the distribution of components through finite element simulation. Specific steps include:

[0112] 4-1) The tolerance combination RoutmaxE corresponding to the most dangerous working condition identified is loaded onto the center node of the RBE2 element in the bare PCB model to simulate the worst bolt height difference in actual assembly. Ultimate strain simulation is performed using the OptiStruct solver to obtain the extreme strain distribution cloud map of the PCB, as shown below. Figure 6 As shown;

[0113] 4-2) Set a permissible strain threshold for the material to determine high strain regions. In this embodiment, the strain threshold = 500 microstrains ( This strain threshold is determined through the material mechanical property test report of the PCB material (e.g., FR-4). If the strain value in a certain area of ​​the PCB exceeds the strain threshold (i.e., >500), the strain threshold is determined. If the region is high strain, then the region is a high strain region.

[0114] The extreme strain values ​​of all unit nodes on the output PCB are plotted to obtain an extreme strain cloud map, which further confirms the high strain regions and component distribution. Figure 8 As shown; import the PCBA layout device diagram into HyperView software and overlay it with the strain distribution cloud map (e.g. Figure 7 As shown), the distribution of components within the high-strain region can be intuitively identified, specifically including:

[0115] Component location: all components are in the high strain region, or some components are in the high strain region; relative direction of components to strain components: perpendicular to strain components, or parallel to strain components.

[0116] 5) Based on the Proba value obtained in step 3) and the component distribution in the high-strain region determined in step 4), calculate the failure probability of each component and complete the assembly stress reliability assessment of the PCBA under design tolerances to guide PCB layout optimization. Specific steps include:

[0117] 5-1) Based on the high strain region range, strain extreme values ​​of each node in the high strain region are selected from the strain extreme values ​​of all unit nodes of the PCB to form a high strain region strain dataset.

[0118] 5-2) Based on the Proba value and the component distribution in the high-strain region, the strain exceedance probability of each component is obtained, and the failure probability of each component in the high-strain region is calculated. The calculation formula is as follows:

[0119]

[0120] In the formula, This represents the probability of component failure. This represents the total number of nodes in the region where the component is located. The strain in this region exceeds the strain threshold (500). The number of nodes, This represents the probability that a random disturbance falls within the sensitive region.

[0121] In this embodiment, the risk of components is assessed using the risk assessment rules in Table 2. This clarifies the risk level of components under different distribution states, enabling rapid preliminary risk assessment of components with high strain distribution areas. The strain exceedance probability of each component is obtained, as illustrated in the assessment diagram below. Figure 9 As shown.

[0122] Table 2

[0123]

[0124] 5-3) Set a reliability threshold to comprehensively assess the failure risk of components. This reliability threshold is determined based on automotive electronics industry standards and product design requirements. Summarize the failure probabilities and high-strain region distribution of all components to complete the assembly stress reliability assessment of the PCBA under design tolerances, obtaining the assessment results:

[0125] If the failure probability of all components is lower than the preset reliability threshold, then the assembly stress reliability of the PCBA meets the design requirements, and the design of the PCBA is a finalized design that can be directly put into production.

[0126] If the failure probability of a component is higher than the preset reliability threshold, the assembly stress reliability of the PCBA does not meet the design requirements and layout optimization is required.

[0127] Layout optimization design must be based on the distribution of high-strain regions and an optimization plan must be formulated. For example, avoid placing precision components such as chips and sensors in high-strain regions; adjust the mounting direction of components to be perpendicular to the strain components; increase the thickness of the PCB substrate in high-strain regions or add reinforcing rib structures; optimize the position of bolt mounting holes to reduce assembly stress concentration;

[0128] After completing the layout optimization, repeat steps 1) to 5) until the final PCBA assembly stress reliability meets the design requirements, and output the final result.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications made to the present invention by those skilled in the art without departing from the spirit of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for assembly stress evaluation of an automotive motor controller PCBA, characterized in that, The method comprises the following steps: 1) a three-dimensional model of the PCBA is established, and the PCBA assembly is simplified to obtain a PCB bare board model; 2) finite element modeling is performed on the PCB bare board model, the PCB bare board model is assigned with corresponding material properties, and a stiffness matrix is obtained; 3) the limit tolerance range of each mounting point of the PCBA is determined, and the stiffness matrix obtained in step 2) is combined to analyze the strain distribution of the PCB under random disturbance by using the Monte Carlo method, calculate the Proba value, identify the most dangerous working condition corresponding to the maximum principal strain and the safest working condition corresponding to the minimum principal strain, and calculate the failure probability of each component according to the Proba value obtained in step 3) and the component distribution state of the high strain region determined in step 4), and complete the assembly stress reliability evaluation of the PCBA under the design tolerance. In step 2), the stiffness matrix is obtained in the following manner: 2-1) the PCB bare board model is discretized by finite element mesh, a first-order four-node element is used, and a global size mesh is set; 2. The assembly stress evaluation method according to claim 1, characterized by, 2-2) the anisotropic material properties of the PCB bare board are assigned, and the shell element properties are defined, and the shell element thickness is set; the material properties include density, Young's modulus and Poisson's ratio; 2-3) RBE2 elements are established at each mounting point of the PCBA, displacement is loaded on the center node of each RBE2 element in turn, and the stiffness matrix corresponding to each displacement loading point is extracted. In step 3), the limit tolerance range of each mounting point of the PCBA is determined, and the stiffness matrix is combined to analyze the strain distribution of the PCB under random disturbance by using the Monte Carlo method, calculate the Proba value, identify the most dangerous working condition corresponding to the maximum principal strain and the safest working condition corresponding to the minimum principal strain, and the specific steps include: 3-1) the limit tolerance range of each mounting point of the PCBA is calculated through the actual size chain of each component of the product; 3. The assembly stress evaluation method according to claim 1, characterized by, 3-4) according to the total amount of the sample set and the random disturbance vector, the proportion of the sample set in the random disturbance vector is calculated, the probability Proba value of the random disturbance falling in the sensitive interval is obtained, and the calculation formula is: 3-5) the simulation times of Monte Carlo are set as N, N samples are randomly selected from the sample set, M random vectors are generated, an RT matrix with NXM dimensions is formed, and the equivalent stiffness tensor is obtained by linear superposition of the stiffness tensor by using the following formula: 3-2) generate a standard normal distribution random number by using a Randn() function, construct a random disturbance vector, and the random disturbance vector satisfies the formula ; wherein, y is a random disturbance vector, a is a scaling coefficient, and , is a disturbance variance; 3-3) Set a screening threshold, if the random disturbance vector is stored as a sample set; 3-6) according to the equivalent stiffness tensor, the principal strain extreme value of each simulation is calculated by using the two-dimensional principal strain formula; the most dangerous working condition corresponding to the maximum principal strain and the safest working condition corresponding to the minimum principal strain are finally identified, and the tolerance combination corresponding to the most dangerous working condition is recorded. ； ； In the formula, is the probability of random disturbance falling in the sensitive interval, is , is the screening threshold, is the limit tolerance range of each mounting point of the PCBA; In step 4), according to the tolerance combination corresponding to the most dangerous working condition, the limit strain simulation of the PCB board is performed, the strain distribution cloud diagram of the PCB bare board is obtained, and the high strain region and the component distribution state in the high strain region are further determined, and the specific steps include: ； wherein is the equivalent stiffness tensor to simulate the tolerance effect, is the number of PCBA bolt mounting points, is the random perturbation weight sampled from the sample set, is the stiffness matrix of the kth mounting point of the PCB. ​ 4. The assembly stress evaluation method according to claim 1, 2 or 3, characterized by, ​ 4-1) Load the identified tolerance combination corresponding to the most dangerous working condition to the center node of the RBE2 unit of the PCB bare board model, and use OptiStruct to perform limit strain simulation to obtain a PCB extreme strain distribution cloud map; 4-2) Overlap the PCBA Layout device map and the strain distribution cloud map to obtain the position range of the high strain area, and determine the distribution state of the components in the high strain area.

5. The assembly stress evaluation method according to claim 4, characterized by, In step 4-2), the high strain area is obtained as follows: set a material allowable strain threshold, if the strain value of a certain area of the PCB board exceeds the strain threshold, then the area is a high strain area.

6. The assembly stress evaluation method according to claim 4, characterized by, The distribution state of the components in the high strain area includes that all components are in the high strain area, some components are in the high strain area, and the relative direction of the components and the strain component.

7. The assembly stress evaluation method according to claim 1, characterized by, In step 5), according to the Proba value and the distribution state of the components in the high strain area, the strain overrun probability of each component in the high strain area is obtained, and the failure probability of each component is calculated, and the assembly stress reliability evaluation of the PCBA under the design tolerance is completed to guide the Layout optimization design of the PCB board, and the specific steps include: 5-1) Extract the strain extreme value of all nodes of the PCB, combine the high strain area range, and select the strain extreme value of each node in the high strain area; 5-2) According to the Proba value and the distribution state of the components in the high strain area, the strain overrun probability and the failure probability of each component are calculated, and the calculation formula of the failure probability is as follows: ； wherein, is the component failure probability, is the total number of nodes in the region where the component is located, is the number of nodes in the region where the strain exceeds the strain threshold, is the probability that the random perturbation falls in the sensitive interval; 5-3) Set a reliability threshold, and collect the failure probability of all components and the distribution state of the high strain area to complete the assembly stress reliability evaluation of the PCBA under the design tolerance: If the failure probability of all components is lower than the preset reliability threshold, the assembly stress reliability of the PCBA meets the design requirements; If there is a component whose failure probability is higher than the preset reliability threshold, the assembly stress reliability of the PCBA does not meet the design requirements, and the PCB board Layout design needs to be optimized based on the distribution state of the high strain area.

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