A screwing process planning method for PCB assembly

Abstract

This invention relates to a tightening process planning method for PCB board assembly, belonging to the field of printed circuit board assembly technology. The method performs the following steps: 1) acquiring point cloud data of the PCB board; 2) constructing a high-quality surface using the point cloud data; 3) setting the boundary parameters of the surface; 4) optimizing the tightening process based on the surface using a genetic algorithm. Specifically, the tightening sequence of a set of screws and the different preload of each screw are considered as individuals, the screw number and the preload applied to each screw are considered as design variables, and the flatness of the surface is considered as the optimization objective, thereby optimizing the tightening process. This invention has high versatility, is not limited by PCB board specifications and assembly processes, and can ensure that the flatness of the PCB board assembled using this method is within the required range; thus improving the final assembly accuracy of the PCB board, increasing the success rate of first-pass assembly, and enhancing the overall assembly quality.

Description

Technical Field

[0001] This invention relates to a tightening process planning method for PCB board assembly, belonging to the field of printed circuit board assembly technology. Background Technology

[0002] Printed circuit boards (PCBs) are essential electronic components used to mount circuitry and are widely used in electronic devices. PCBs are composed of materials such as copper foil, resin, and glass cloth, each with different physical and chemical properties. Therefore, during PCB manufacturing, because the PCB is a composite laminate structure with different coefficients of thermal expansion for each layer, residual stress accumulates after temperature changes, leading to warping. In PCB assembly, relying on traditional experience-based blind insertion, assembly, and adjustment methods results in an unpredictable overall assembly state, unreliable assembly performance, and an uncontrollable assembly process. The mechanical precision of the PCB assembly is also unknown, leading to uncertainties in the form and position of the power connectors. Blind screwing and assembly based on experience can easily cause excessive local stress, damaging solder joints. Taking array antennas as an example, the flatness of the PCB significantly affects the assembly success rate of the antenna's port panel.

[0003] Therefore, existing technologies reduce or eliminate PCB board deformation through engineering design, process control, and shipping calibration. Engineering design also avoids structural asymmetry, material asymmetry, and graphic asymmetry to minimize deformation. However, this cannot completely prevent deformation during subsequent installation, still affecting the flatness of the PCB board. There are two main reasons for this: firstly, the shape deviations caused by PCB board processing and forming, as mentioned earlier, still exist; secondly, during the assembly and tightening of multiple screws in a specific order, a phenomenon occurs where later-tightened screws often affect earlier-tightened screws, resulting in a decrease in the preload of earlier-tightened bolts. This phenomenon is called elastic interaction. Since array antennas extensively use screw connections in interlayer assembly, this elastic interaction between multiple screws ultimately leads to uneven preload distribution, further reducing the flatness of the weakly rigid antenna functional board and consequently affecting the overall assembly accuracy. It is worth noting that although there has been some research on the elastic interaction problem of multi-bolt connections, most of them focus on simple examples of multi-bolt arrangements such as flanges. However, they cannot adequately address more complex situations and more irregularly arranged multi-bolt connection structures. Summary of the Invention

[0004] The technical problem to be solved by this invention is: how to provide a tightening process planning method for PCB board assembly with low deformation after assembly, and the method should have high versatility.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is: a tightening process planning method for PCB board assembly, comprising the following steps:

[0006] 1) Obtain the point cloud data of the PCB board;

[0007] The PCB board is scanned using structured light method to obtain the point cloud data;

[0008] 2) Construct high-quality curved surfaces using the point cloud data;

[0009] 3) Set the boundary parameters of the surface based on the actual working conditions;

[0010] The boundary parameters that are set include defining material properties, meshing, defining contact relationships, applying boundary conditions, and loads;

[0011] 4) Based on the surface, the tightening process is optimized using a genetic algorithm; wherein, the tightening sequence of a set of screws and the different preload of each screw are taken as individuals, the number of each screw and the preload applied to each screw are taken as design variables, and the flatness of the surface is taken as the optimization target, so as to optimize the tightening process.

[0012] The improvement to the above technical solution is: Step 1) Use a structured light scanner to scan the PCB board.

[0013] The improvement of the above technical solution is as follows: In step 4), the optimization process uses the point displacement value and local stress as constraints, and the restrictions on the local stress and point displacement value are selected based on the process specifications of the actual working conditions.

[0014] The beneficial effects of this invention are: it has high versatility, is not limited by PCB board specifications and assembly processes, and can ensure that the flatness of the PCB board assembled by this method is within the required range; thereby improving the assembly accuracy of the PCB board, increasing the success rate of first-time assembly, and improving the overall assembly quality. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart illustrating a tightening process planning method for PCB board assembly according to an embodiment of the present invention.

[0017] Figure 2This is a schematic diagram of a PCB board assembly with an array antenna according to an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of the PCB board assembly of the hidden array antenna according to an embodiment of the present invention.

[0019] Figure 4 This is an optimized flowchart of a tightening process planning method for PCB board assembly according to an embodiment of the present invention.

[0020] Reference numerals: 1. Antenna assembly; 2. Antenna port panel; 3. Circuit components; 4. Electrical connector; 5. PCB board; 6. Screw; 7. Heat sink base. Detailed Implementation

[0021] Example

[0022] like Figure 2 and Figure 3 As shown, in this embodiment, the selected PCB board size is 156.5×156.5×3mm, the PCB substrate material is epoxy fiberglass board, and the heat resistance grade is FR-4. M3 stainless steel screws of grade SS304 are used. The heat sink base is machined using a one-piece milling method and is made of aluminum alloy material of grade L6063.

[0023] This embodiment provides a tightening process planning method for PCB board assembly, such as... Figure 1 As shown, perform the following steps:

[0024] 1) Obtain the point cloud data of the PCB board;

[0025] The PCB board is scanned using structured light to obtain point cloud data; the PCB board is scanned using a structured light scanner; and the point cloud data is preprocessed using the data processing software built into the device and converted into an STL (Standard Triangle Language) format file for output.

[0026] 2) Construct high-quality surfaces using the point cloud data; select the Digital Shape Editor (DSE) and Quick Surface Reconstruction (QSR) modules in CATIA as reverse modeling platforms. The DSE module is used to filter and repair the STL format point cloud data, while the QSR module is used to finally fit a high-quality surface.

[0027] 3) Set the boundary parameters of the surface;

[0028] ABAQUS was chosen as the finite element method (FEM) software. The reverse-engineered model was imported into ABAQUS, and material properties, meshing, contact relationships, boundary conditions, and loads were defined based on real-world conditions. The PCB board's material properties were defined using engineering constants and solid element layups, while the screws and heatsink base were modeled using linear elasticity based on elastic modulus and Poisson's ratio. The mesh type used was C3D8R. Solid bolt elements were replaced with MPC (Multi-point constraint) multi-node coupling constraints combined with beam elements, and the load type was set to BoltLoad, applied to the beam elements. A sliding friction contact relationship was defined between the heatsink base and the PCB board, with six degrees of freedom constraints applied to the lower surface of the heatsink base to simulate real-world conditions. Output displacement and stress field variables were defined in the step module.

[0029] 4) Based on the aforementioned surface, a genetic algorithm is used to optimize the tightening process. Specifically, the tightening sequence of a set of screws and the different preload of each screw are considered as individuals. The 36 screws are numbered sequentially, and the screw number and the preload applied to each screw are used as design variables. The flatness of the surface is used as the optimization objective to optimize the tightening process. The optimization process uses point displacement values ​​and local stress as constraints, and the limitations on local stress and point displacement values ​​are selected based on the process specifications of actual working conditions.

[0030] The flatness calculation program was pre-written and packaged in MATLAB, then compiled into a component that can be called by Python. The corresponding module can be directly imported into a Python script for use as a function. Since the flatness calculation requires the displacement values ​​of points on the target plane, Python is needed to read the displacement values ​​of points on the target PCB plane from the ABAQUS finite element calculation results. The flatness calculation is implemented programmatically, specifically using the least squares plane method code for flatness measurement from the academic paper "Design of a Flatness Error Evaluation Program Based on MATLAB" (Authors: Ma Shuhong, Wu Huling, Xue Shuai, Microcomputer Applications, Volume.37, No. 10, 2021, pages 77-80).

[0031] The computational flow of the genetic algorithm in this embodiment is as follows: Figure 4 As shown,

[0032] 1) Initialize parameters such as the number of iterations, genetics, and mutation rate, and randomly generate a parent population with a certain number of individuals. Alternatively, based on production experience or theoretical research, one or more superior tightening strategies can be selected as given initial individuals to be introduced into the first generation population.

[0033] 2) Check the individuals in the generated parent group, using local stress and displacement value of the sampling point as constraints. If the local stress and displacement value of the sampling point are within the maximum allowable range, then discard the individual.

[0034] 3) Determine the objective function value based on the flatness calculation formula.

[0035] When calculating the objective function value, a series of nodes are selected as a monitoring node set in the finite element model in advance. The normal displacement obtained from the finite element simulation at the monitoring node is extracted and substituted into the program for calculation. Then, different flatness values ​​are calculated for each tightening strategy (including tightening sequence and tightening force applied to each screw).

[0036] 4) Determine the excellence level of individuals based on the objective function, sort the individuals, retain a small number of excellent individuals, and then obtain new individuals through genetic operators such as mutation and heredity, keeping the number of offspring populations the same as the number of parent populations.

[0037] 5) Repeat steps 2) through 4) until the set number of iterations is reached, then stop the optimization. Output the best individuals, and write each generation of individuals and its objective function value to a file for review.

[0038] From the retained tightening strategies, select the best option and assemble the PCB board according to that strategy, including the tightening sequence and the preload of each screw. Use a 3D-DIC non-contact optical measurement device to measure the global stress and displacement fields of the assembled PCB board, confirming that the flatness of the final assembled PCB board is within the required range.

Claims

1. A tightening process planning method for PCB board assembly, characterized in that, Perform the following steps: 1) Obtain the point cloud data of the PCB board; The PCB board is scanned using structured light method to obtain the point cloud data; 2) Construct a surface using the point cloud data; 3) Set the boundary parameters of the surface; Define material properties, generate meshes, define contact relationships, apply boundary conditions and loads based on real-world working conditions; 4) Based on the surface, the tightening process is optimized using a genetic algorithm. The tightening sequence of a set of screws and the different preload of each screw are taken as individuals. The number of each screw and the preload applied to each screw are taken as design variables. The flatness of the surface is taken as the optimization target. The point displacement value and local stress are taken as constraints. The restrictions on the local stress and point displacement value are selected based on the process specifications of the actual working conditions to optimize the tightening process.

2. The tightening process planning method for PCB board assembly of claim 1, wherein: Step 1) Scan the PCB board using a structured light scanner.

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