Printed board assembly three-proofing spray simulation and process parameter design optimization method

Abstract

This invention provides a method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies, comprising the following steps: determining the conformal coating material, the printed circuit board assembly object, and the coating requirements based on the corresponding process scenario; determining a calculation model for conformal coating based on the process scenario, the calculation model including a three-dimensional spatial model of the coating scenario, a spray flow density distribution model, a paint layer adhesion and flow diffusion model, and a spray scanning process model; optimizing the conformal coating and parameters of the printed circuit board assembly based on the process scenario and the calculation model to determine the optimal coating parameters; and implementing the coating based on the optimal coating parameters. This invention makes the planning and design process of conformal coating more evidence-based, and reduces the risk of rework due to design and planning defects when facing mass production of products.

Description

Technical Field

[0001] This invention relates to the field of conformal coating for printed circuit board assemblies, specifically to a method for simulation and optimization of process parameters for conformal coating of printed circuit board assemblies. Background Technology

[0002] Conformal coatings (films) can prevent printed circuit board (PCB) assemblies in electronic devices operating in complex environments from being affected by moisture, salt spray, and mold, thus preventing system failures. Currently, the demand for conformal coatings for PCB assemblies is gradually shifting from military and aerospace products to consumer electronics. Efficient planning, design, and parameter optimization for conformal coatings have become crucial requirements in electronics manufacturing. The diversity of shapes in consumer electronics, the variety of forms and structures of their densely distributed components, and the diverse needs for localized conformal coatings make efficient planning, parameter design, and optimization of conformal coatings a critical issue.

[0003] Current research on conformal coatings focuses more on structural components, specifically various curved surfaces, using reasonable models for segmentation and coating trajectory planning. However, there is a lack of suitable methods for addressing the structural diversity of complex, high-density components on printed circuit board assemblies. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies. This method fully considers the complex three-dimensional structure of the object to be coated and the adhesion, flow, and diffusion of the coating film in three-dimensional space. The simulation results are used to evaluate the effectiveness of the coating planning and design, and to guide optimization. This method makes conformal coating design for complex printed circuit board assemblies more evidence-based, and can promptly identify potential defects in the planning and parameter design during implementation, thereby improving the efficiency and economy of conformal coating implementation for mass production.

[0005] The method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies provided by the present invention includes the following steps:

[0006] Step S1: Determine the conformal coating materials, printed circuit board assembly objects, and coating requirements based on the corresponding process scenario of the product;

[0007] Step S2: Determine the calculation model for conformal spraying based on the process scenario. The calculation model includes a three-dimensional spatial model of the spraying scenario, a spraying flow density distribution model, a paint layer adhesion and flow diffusion model, and a spraying scanning process model.

[0008] Step S3: Based on the process scenario, perform the three-proof coating and parameter optimization of the printed circuit board assembly through the calculation model to determine the optimal coating parameters;

[0009] Step S4: Carry out the spraying according to the optimal spraying parameters.

[0010] Preferably, the three-dimensional spatial model of the spraying scene includes the three-dimensional space of the printed circuit board substrate, components, and the area above the printed circuit board;

[0011] The three-dimensional spatial model of the spraying scene is a spatial discrete voxel model. Each voxel in the calculation model describes the material changes within a preset spatial range of its coordinate position using corresponding voxel attributes.

[0012] The computational model for the three-proof spraying process simulates the three-proof spraying process by calculating the evolution of the properties of each voxel in the three-dimensional space of the spraying scene over time.

[0013] Preferably, the spray flow density distribution model is used to describe the distribution of the spray flow density of the three-proof material in the space above the printed circuit board;

[0014] The paint adhesion and flow diffusion model is used to describe the adhesion of paint spraying on printed circuit boards and their components and the flow diffusion of paint film on the surface, including the adhesion coefficient of paint spraying onto the printed circuit board assembly material and the flow diffusion model related to the dose gradient between paint films.

[0015] The spraying and scanning process model is used to describe the trajectory and mode of the spray head scanning and spraying, including the coordinate path function P of the center of the spray head projected onto the printed circuit board plane in the three-dimensional space model. s The dwell time t at each center coordinate point of the spraying scan d and the total number of scans n s .

[0016] Preferably, the voxel properties in the three-dimensional spatial model of the three-dimensional coating scenario include: material μ, adhesion time t. s And the voxel material occupancy rate Π;

[0017] The material μ includes at least the spraying space, coating material, and components and printed circuit board substrate material.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: Initialize the 3D spatial voxel model of the spraying scene and the spraying scanning process model;

[0020] Step S22: Iteratively calculate the position of the scanning spray with Δt as the time unit and refresh the Π and μ attributes of all voxels in the scanning space;

[0021] Step S23: When the iterative accumulation reaches... The final spraying effect is marked according to the voxel attributes μ of each space, where (i,j,k) represents the voxel position.

[0022] Preferably, step S3 includes the following steps:

[0023] S31: Determine the spraying parameters to be optimized;

[0024] S32: Initialize the calculation model, set the process scenario parameters, establish n sets of parameter models to be optimized, and randomly initialize the parameters to be optimized;

[0025] S33: Independently and in parallel substitute each set of parameter models to be optimized into the calculation model to perform spraying effect simulation calculation;

[0026] S34: Evaluate and calculate the spraying effect. Calculate the coating thickness at various points on the printed circuit board assembly using the voxel state in the simulation model. Evaluate the design effect of the spraying parameters using a comprehensive function of the root mean square of the thickness deviation at key locations and the average thickness of the prohibited spraying area.

[0027] S35: Repeat steps S33-S34 until the spraying parameters meet the design requirements or the maximum number of iterations is reached.

[0028] Preferably, the spray flow density distribution model adopts an empirical model obtained from the thickness distribution test of the spraying experiment or a multiple Gaussian distribution model truncated from the nozzle shape.

[0029] Preferably, the flow diffusion model is:

[0030] f d (i,j,k)=∑d f (t s(nb) (i,j,k))·(Π (nb) (i,j,k)-Π(i,j,k))

[0031] Among them Π (nb) (i,j,k) represents the attribute Π of the voxels neighboring voxels of voxel (i,j,k); d f The function represents the flow diffusion coefficient, which decreases with increasing adhesion time; t s(nb) (i,j,k) represents the paint film adhesion time of the voxels neighboring voxels (i,j,k).

[0032] Preferably, the process of the voxel properties evolving over time for:

[0033]

[0034]

[0035] in, This represents the material occupancy of the voxel at position (i,j,k) at time t. f is the paint adhesion coefficient based on the material properties of the neighboring locations of the voxel. s It is a spray flow density model, f d It is a flow-diffusion model, where Δt is a unit time of one iteration, and Π full It is the dose that fills the voxel space, and F is the paint spraying flow rate.

[0036] Preferably, the method for establishing the spraying scanning process model is as follows:

[0037] Based on the spacing d at the center position of the scan s Construct a table of scan point locations on the projection plane of the scan space;

[0038] Set the spraying dwell time t for different positions according to the spraying requirements. d ;

[0039] Set t for areas where spraying is unnecessary or prohibited d =0;

[0040] Based on the spraying scanning strategy, the scanning points are sorted, and a coordinate path function P is constructed. s。

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. This invention provides a rapid and efficient method for the planning and design of conformal coating for printed circuit board assemblies, making the planning and design process of conformal coating more based on evidence, and reducing the risk of rework due to design and planning defects when mass-producing products.

[0043] 2. In the planning and design of conformal coating for printed circuit board assemblies, this invention fully considers the different spatial shapes of printed circuit board assemblies and the impact of differences in the shape, structure, and distribution of components on the conformal coating effect. This is more in line with actual production scenarios and the calculation results are more accurate.

[0044] 3. In the planning and design of conformal coating for printed circuit board assemblies, this invention fully considers the adhesion, flow and diffusion of the coating film on the printed circuit board and components, as well as the curing over time, which is more in line with actual production and its simulation effect and evaluation conclusion are more reliable. Attached Figure Description

[0045] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0046] Figure 1This is a flowchart of the simulation and process parameter design optimization method for conformal coating of printed circuit board assembly samples in an embodiment of the present invention;

[0047] Figure 2 This is a sample model and scanning plan of a printed circuit board assembly in an embodiment of the present invention;

[0048] Figure 3 This is a model space voxel model of local components of a printed circuit board assembly sample in the embodiment of the present invention;

[0049] Figure 4 This is a simulation of the spraying effect on a portion of the printed circuit board assembly sample in an embodiment of the present invention. Detailed Implementation

[0050] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention. These all fall within the scope of protection of the present invention.

[0051] like Figure 1 As shown in the embodiments of the present invention, the present invention provides a method for simulation and process parameter design optimization of conformal coating for printed circuit board assembly samples. The present invention uses... Figure 2 Using a test printed circuit board assembly as the target, we conducted conformal coating simulation and process parameter design optimization. The coating was carried out using selective automatic coating equipment with a circular nozzle, and the conformal coating was polyurethane.

[0052] The conformal coating simulation and process parameter design optimization for this printed circuit board assembly include the following steps:

[0053] Step S1: Determine the conformal coating materials, printed circuit board assembly objects, and coating requirements based on the corresponding process scenario of the product;

[0054] Step S2: Determine the calculation model for conformal spraying based on the process scenario. The calculation model includes a three-dimensional spatial model of the spraying scenario, a spraying flow density distribution model, a paint layer adhesion and flow diffusion model, and a spraying scanning process model.

[0055] Step S3: Based on the process scenario, perform the three-proof coating and parameter optimization of the printed circuit board assembly through the calculation model to determine the optimal coating parameters;

[0056] Step S4: Carry out the spraying according to the optimal spraying parameters.

[0057] In this embodiment of the invention, a three-dimensional spatial model of the spraying scene is first established in step S2. This model needs to cover the three-dimensional space including the printed circuit board substrate, components, and the area above the printed circuit board. Figure 3 (a) is a schematic diagram of the cross-section of the spatial voxel model at a local BGA component in the printed circuit board assembly sample, and (b) is a schematic diagram of adjacent voxels. As shown in the figure, each voxel in the calculation model describes the material variation within a spatial range based on its coordinate position using its attributes. The specific attributes include: material μ, adhesion time t. s Voxel material occupancy Π. The spatial range can be set to within 1 mm. Voxel material properties μ include elements {AIR, COATING, COMPONENT, BOARD}, representing the spraying space (air), coating material, components, and substrate, respectively. The calculation method for the evolution of voxel properties over time is based on:

[0058]

[0059]

[0060] in, This represents the material occupancy of the voxel at position (i,j,k) at time t. f is the paint adhesion coefficient based on the material properties of the neighboring locations of the voxel. s It is a spray flow density model, f d It is a flow-diffusion model, where Δt is a unit time of one iteration, and Π full It is the dose that fills the voxel space, and F is the paint spraying flow rate.

[0061] In step S2, the spray flow rate density distribution model is determined. Because a circular nozzle is selected, and considering the thickness distribution of the spraying experiment, a triple Gaussian distribution model is adopted for the spray flow rate density distribution.

[0062] In step S2, the paint adhesion and flow diffusion model is determined. The calculation method for this model is as follows:

[0063] f d (i,j,k)=∑d f (t s(nb) (i,j,k))·(Π (nb) (i,j,k)-Π(i,j,k))

[0064] Among them Π (nb) (i,j,k) represents the Π attribute of the voxels neighboring voxels of voxel (i,j,k), d f The function represents the flow diffusion coefficient, which decreases with increasing adhesion time. The value of d is determined based on the selected polyurethane paint grade and the humidity characteristics of the implementation environment. f function.

[0065] In step S2, the spraying scanning process model is determined. The spacing d between the scanning center positions is determined based on the equipment parameters and the required spraying thickness. s Based on this spacing, a scanning point location table is constructed on the projection plane of the scanning space; the spraying dwell time t at different locations is set according to the spraying requirements. d In this example, the time is preset to be the same at all points; for the glass diode area of ​​the component that does not require or is prohibited from being sprayed, its t d =0; Based on the equipment's spraying and scanning strategy, the Serpentine mode was selected, and the scan points were sorted and connected to construct the coordinate path function P. s The trajectory is shown in the diagram. Figure 2 As shown.

[0066] In step S3, the optimal process parameter design is obtained through iterative calculations using the model established in step S2. Each simulation iteration is an iterative evolution calculated in units of time infinitesimal elements. The specific implementation steps include:

[0067] Step S21: Initialize the three-dimensional space voxel model of the three-dimensional conformal spraying scene and the spraying scanning process model;

[0068] Step S22: Iteratively calculate the position of the scanning spray with Δt as the time unit and refresh the Π and μ attributes of all voxels in the scanning space;

[0069] Step S23: When the iterative accumulation reaches... The final spraying effect is marked based on the μ attribute of each voxel in space.

[0070] In step S21, the three-dimensional spatial voxel model is initialized, that is, the material properties of each voxel element are set, wherein the voxel occupancy rate of AIR material is 0.

[0071] In step S22, time Δt is 1 / 10 of the ratio of the dose occupied by a single voxel to the dose density at the center of the scan.

[0072] In step 3, for the optimization of the model coefficients, this embodiment selected the particle swarm optimization algorithm, and its specific implementation steps include:

[0073] S31: Determine the spraying parameters to be optimized;

[0074] S32: Initialize the calculation model, set the process scenario parameters, establish n sets of parameter models to be optimized, and randomly initialize the parameters to be optimized;

[0075] S33: Independently and in parallel substitute each set of parameter models to be optimized into the calculation model to perform spraying effect simulation calculation;

[0076] S34: Evaluate and calculate the spraying effect. Calculate the coating thickness at various points on the printed circuit board assembly using the voxel state in the simulation model. Evaluate the design effect of the spraying parameters using a comprehensive function of the RMS thickness deviation at key locations and the average thickness of prohibited spraying areas.

[0077] S35: Repeat steps S33-S34 until the spraying parameters meet the design requirements or the maximum number of iterations is reached.

[0078] In step S31, the spraying dwell time t is selected. d The vector formed is defined as the particle X = {t} to be optimized. d1 , t d2 , t d3 ···t dn}

[0079] In step S32, the model and particle swarm are initialized. The particle size is 30, and the number of calculation iterations for each particle is set to 100,000. First, each particle is initialized by random perturbation over a preset time. The initial velocity of all particles is set to 0.

[0080] In step S33, each group of particles is substituted into the model for parallel simulation.

[0081] The parameter optimization process in step S34 is the particle update process, and the iterative function for the particle rate is:

[0082]

[0083] Where t is the iteration time, P i (t) This represents the optimal historical position of the particle. To find the optimal point for all particles and avoid the calculation iterations from converging to a local optimum, the inertia coefficient ω is set to 0.8, the learning coefficients c1 and c2 are set to 0.2, and the random factors r1 and r2 are set to random values ​​in the range [0,1].

[0084] The particle's position update is calculated as follows:

[0085]

[0086] The fitness function for each particle is:

[0087]

[0088] (x, y) are the voxel coordinates on the printed circuit board plane, n COATING (x,y) represents the number of voxels in the COATING state in the normal direction of the printed circuit board at the (x,y) coordinate position, which describes the coating thickness at that position.

[0089] This is the root mean square of the number of voxels filled with conformal coating at all points along the normal direction of the printed circuit board assembly surface. For each particle, while calculating the fitness value, the average thickness f of the restricted coating area also needs to be calculated. fib The particle update iteration calculation is as follows:

[0090]

[0091]

[0092] f(P i (t) () represents particle P at time t i fitness value, f fib This represents the average thickness of the prohibited areas.

[0093] This iterative calculation process is repeated to select the optimal parameters. The final optimal parameters are then applied to the actual spraying process. The simulation results of spraying on local components of the printed circuit board assembly sample are shown below. Figure 4 As shown in (a), (b), and (c).

[0094] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A printed board assembly tri-proof spray simulation and process parameter design optimization method, characterized by, Includes the following steps: Step S1: Determine the conformal coating materials, printed circuit board assembly objects, and coating requirements based on the corresponding process scenario of the product; Step S2: Determine the calculation model for conformal spraying based on the process scenario. The calculation model includes a three-dimensional spatial model of the spraying scenario, a spraying flow density distribution model, a paint layer adhesion and flow diffusion model, and a spraying scanning process model. Step S3: Based on the process scenario, perform the three-proof coating and parameter optimization of the printed circuit board assembly through the calculation model to determine the optimal coating parameters; Step S4: Carry out the spraying according to the optimal spraying parameters; the three-dimensional space model of the spraying scene includes the three-dimensional space of the printed circuit board substrate, components, and the area above the printed circuit board; The three-dimensional spatial model of the spraying scene is a spatial discrete voxel model. Each voxel in the calculation model describes the material changes within a preset spatial range of its coordinate position using corresponding voxel attributes. The calculation model for the three-proof spraying process simulates the three-proof spraying process by calculating the evolution of the properties of each voxel in the three-dimensional space of the spraying scene over time. The spraying flow density distribution model is used to describe the distribution of the three-proof material spraying flow density in the space above the printed circuit board. The paint adhesion and flow diffusion model is used to describe the adhesion of paint spraying on printed circuit boards and their components and the flow diffusion of paint film on the surface, including the adhesion coefficient of paint spraying onto the printed circuit board assembly material and the flow diffusion model related to the dose gradient between paint films. The spraying and scanning process model is used to describe the trajectory and mode of the spray head scanning and spraying, including the coordinate path function of the projection of the center of the spray head onto the printed circuit board plane in the three-dimensional space model. P s The dwell time of each center coordinate point of the spraying scan t d and total number of scans n s .

2. The method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies according to claim 1, characterized in that, The voxel properties in the three-dimensional spatial model of the three-dimensional coating scenario include: material μ Adhesion time t s and voxel material occupancy Π ; The material μ It includes at least the spraying space, coating materials, and components and printed circuit board substrate materials.

3. The method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies according to claim 2, characterized in that, Step S2 includes the following steps: Step S21: Initialize the 3D spatial voxel model of the spraying scene and the spraying scanning process model; Step S22: with Δt The position of the scan spray is iteratively calculated using time units, and all voxels in the scan space are refreshed. Π and μ property; Step S23: When the cumulative iteration reaches Based on the properties of each voxel in space μ Mark the final spraying effect, among which, (i,j,k) Indicates the position of a voxel.

4. The method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies according to claim 1, characterized in that, Step S3 includes the following steps: S31: Determine the spraying parameters to be optimized; S32: Initialize the calculation model, set the process scenario parameters, and establish... n Set up a model of parameters to be optimized and randomly initialize the parameters to be optimized. S33: Independently and in parallel substitute each set of parameter models to be optimized into the calculation model to perform spraying effect simulation calculation; S34: Evaluate and calculate the spraying effect. Calculate the coating thickness at various points on the printed circuit board assembly using the voxel state in the simulation model. Evaluate the design effect of the spraying parameters using a comprehensive function of the root mean square of the thickness deviation at key locations and the average thickness of the prohibited spraying area. S35: Repeat steps S33-S34 until the spraying parameters meet the design requirements or the maximum number of iterations is reached.

5. The method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies according to claim 1, characterized in that, The spray flow density distribution model adopts an empirical model obtained from the thickness distribution test of the spraying experiment or a multiple Gaussian distribution model truncated from the nozzle shape.

6. The method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies according to claim 1, characterized in that, The flow-diffusion model is as follows: in Π (nb) ( i,j,k ) is a voxel ( i,j,k Neighbor voxel attributes Π ; d f The function represents the flow diffusion coefficient, which decreases with increasing adhesion time. t s(nb) ( i , j , k ) is a voxel ( i,j,k Adhesion time of the paint film of neighboring voxels.

7. The method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies according to claim 2, characterized in that, The process of the evolution of the voxel properties over time for: in, yes t time( i , j , k The occupancy rate of voxel materials at different locations. The paint adhesion coefficient is based on the material properties of the neighboring locations of the voxel. f s It is a spray flow density model. f d It is a flow-diffusion model. Δt It is a unit of time for an iteration. Π full It is the dose that fills the voxel space. F This refers to the paint spraying flow rate.

8. The method for simulation and process parameter design optimization of conformal coating for printed circuit board assemblies according to claim 1, characterized in that, The method for establishing the model of the spraying and scanning process is as follows: According to the spacing of the scan center position d s Construct a table of scan point locations on the projection plane of the scan space; Set the spraying dwell time for different locations according to the spraying requirements. t d ; For areas where spraying is unnecessary or prohibited, set up t d =0; Based on the spraying scanning strategy, the scanning points are sorted, and a coordinate path function is constructed. P s。

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