CN118070575A

CN118070575A - Process data processing method of coating aluminum sheet for PCB

Info

Publication number: CN118070575A
Application number: CN202410495223.3A
Authority: CN
Inventors: 邹向东
Original assignee: Guangdong Zhongchen Electronic Technology Co ltd
Current assignee: Guangdong Zhongchen Electronic Technology Co ltd
Priority date: 2024-04-24
Filing date: 2024-04-24
Publication date: 2024-05-24
Anticipated expiration: 2044-04-24
Also published as: CN118070575B

Abstract

The invention relates to the technical field of printed circuit boards, in particular to a process data processing method of a coated aluminum sheet for a PCB. The method comprises the following steps: acquiring material composition data of a coated aluminum sheet, wherein the material composition data comprises matrix alloy data, reinforcing phase data and functional coating data; carrying out interaction analysis on components of the electronic structure layer of the coating aluminum sheet according to the material composition data, and predicting interface behaviors so as to obtain interface behavior prediction data; performing phase field simulation according to the interface behavior prediction data, and mapping interface states and performances under different conditions to obtain interface characteristic diagram data; and carrying out state simulation analysis on the growth behaviors of the coating according to the interface characteristic diagram data and different interface states, thereby obtaining growth behavior simulation data. The invention ensures that the coating aluminum sheet has good heat conduction performance in the drilling process through micro-channel cooling, and can rapidly take away the heat generated in the drilling process.

Description

Process data processing method of coating aluminum sheet for PCB

Technical Field

The invention relates to the technical field of printed circuit boards, in particular to a process data processing method of a coated aluminum sheet for a PCB.

Background

The coating control method of the coated aluminum sheet in the drilling process of the PCB (Printed Circuit Board ) is a method for coating the surface of the aluminum sheet by a pointer to protect the surface quality of the aluminum sheet and ensuring the drilling precision and quality. The coating may protect the surface of the aluminum sheet or other material from the external environment, such as oxidation, corrosion, contamination, etc., during the PCB manufacturing process. Particularly, in the drilling process of the PCB, the coating layer can protect the surface of the aluminum sheet from mechanical damage in the drilling process. The coating layer can increase the wear resistance of the surface of the PCB, reduce the surface wear and damage caused by external force effects such as friction, scraping and the like, and prolong the service life of the PCB. The thickness of the coating needs to be precisely controlled to meet specific requirements. Too thick or too thin coating may affect heat conduction performance, drilling accuracy or other performance indexes, but the process data processing method of the coated aluminum sheet for the PCB circuit board often has the problem of uncontrollable coating growth.

Disclosure of Invention

Based on the above, the present invention is needed to provide a process data processing method for a coated aluminum sheet for a PCB circuit board, so as to solve at least one of the above technical problems.

In order to achieve the above purpose, a process data processing method of a coated aluminum sheet for a PCB circuit board comprises the following steps:

Step S1: acquiring material composition data of a coated aluminum sheet, wherein the material composition data comprises matrix alloy data, reinforcing phase data and functional coating data; carrying out interaction analysis on components of the electronic structure layer of the coating aluminum sheet according to the material composition data, and predicting interface behaviors so as to obtain interface behavior prediction data; performing phase field simulation according to the interface behavior prediction data, and mapping interface states and performances under different conditions to obtain interface characteristic diagram data;

Step S2: performing state simulation analysis on the growth behaviors of the coating according to the interface characteristic diagram data and different interface states, so as to obtain growth behavior simulation data; establishing a coating growth dynamics model according to the corresponding technological parameters of different interface states and the growth behavior simulation data; the control parameters are adjusted in real time according to the preset coating thickness and a coating growth dynamics model, so that control parameter adjustment data are obtained; constructing a multi-stage feedback control system according to the control parameter adjustment data, thereby obtaining a thickness control model;

step S3: obtaining coating defect data under different process conditions; performing defect attribution analysis on the coating defect data, and extracting a mapping relation to obtain defect attribution data; performing defect prediction according to the defect attribution data and the thickness control model, and obtaining a minimized defect level through an integrated optimization technology, so as to obtain optimized process window data;

Step S4: acquiring micro-channel geometric data inside the coated aluminum sheet; constructing a micro-channel geometric model according to the micro-channel geometric data; simulating the heat flow behavior in the micro-channel according to the micro-channel geometric model by a finite volume method, and correcting the coupling effect so as to obtain heat flow path diagram data;

Step S5: and carrying out temperature parameter alignment on the heat flow path diagram data and the optimized process window data so as to obtain optimized coating scheme data.

The invention can comprehensively understand the material composition of the coated aluminum sheet by acquiring the material composition data of the coated aluminum sheet, including matrix alloy data, reinforcing phase data and functional coating data. This aids in a thorough understanding of the internal structure and material properties of the coated aluminum sheet. By performing interaction analysis on the material composition data, interactions and interface behavior between the different components can be studied. This helps predict the interfacial behavior of the coated aluminum sheet under different conditions, such as interfacial adhesion, interfacial diffusion, etc. These predictive data are important for optimizing coating design and improving coating performance. By analyzing and simulating the interface characteristic diagram data, the growth behavior of the coating in different interface states can be known. This helps to understand the effect of the growth mechanism, growth rate, and growth conditions of the coating on the coating properties. Guidance and reference can be provided for optimizing the coating growth process through growth behavior simulation analysis. And establishing a coating growth dynamics model based on different interface states and corresponding technological parameters. By adjusting the control parameters in real time, the accurate control of the thickness of the coating can be realized. This helps to improve the consistency and repeatability of the coating preparation and meets the application requirements of a particular thickness requirement. By analyzing and attributing coating defect data, the source and mechanism of generation of defects can be determined. This helps identify and solve problems during the coating preparation process and takes corresponding measures to reduce the likelihood of defects. And performing defect prediction and optimization based on the defect attribution data and the thickness control model. By integrating optimization techniques, an optimized process window can be found that minimizes the defect level. This helps to optimize the coating preparation process and improve the coating quality and performance. By acquiring the geometric data of the micro-channels inside the coated aluminum sheet, the shape, size and distribution of the micro-channels can be known. This helps design and optimize the microchannel structure to meet specific thermal management requirements. And simulating the thermal flow behavior in the micro-channel by a numerical simulation method such as a finite volume method. This may help understand the heat flow path, heat transfer efficiency, and characteristics of the thermal interface. Through simulation analysis, the design of the micro-channel can be optimized, and the heat conduction efficiency and the heat management performance are improved. The thermal flow path map data and the optimized process window data are aligned, i.e., the temperature parameters therebetween are matched and adjusted. This helps ensure that the thermal flow path map data and the optimized process window data are consistent in temperature parameters for further analysis and optimization. The data after alignment is analyzed and processed, so that optimized coating scheme data can be obtained. These data may provide information about the optimal performance and effect of the coating under specific temperature conditions. Based on these data, the coating design and manufacturing process can be further improved to achieve better thermal management effects and performance. In summary, the above steps include in-depth knowledge of the material composition, interactions and interfacial behavior of the coated aluminum sheet, optimization of coating growth process and thickness control, identification and reduction of coating defects, optimization of microchannel design and thermal management performance, and finally, the acquisition of optimized coating scheme data. These effects help to improve the performance, quality and reliability of the coating, meeting the needs of a particular application.

The coating aluminum sheet is suitable for a cover plate of a PCB Cheng Zuankong, has good heat conduction performance in the drilling process, and can rapidly take away heat generated in drilling. Simultaneously, prevent to warp in twinkling of an eye when the drill bit is drilled down, make drilling precision keep good, and prevent that the face from producing the burr. The coated aluminum sheet has the following characteristics: the composite aluminum sheet is composed of pure aluminum with smooth surface and proper hardness on the upper and lower surfaces, and a special fiber core layer, and has a total thickness of 0.20mm. The composite aluminum sheet has excellent performance and is particularly suitable for the cover plate for micropore processing of a multilayer plate. The composite aluminum sheet has proper hardness, can prevent burrs from being generated on the surface of the plate during high-speed drilling, and can not generate burrs due to high stiffness and good elasticity of the cover plate. And the drilling position accuracy is improved. The abrasion of the drill bit is reduced, the breakage of the drill bit is prevented, and the service life of the drill bit is prolonged.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

fig. 1 is a flow chart showing the steps of the process data processing method of the coated aluminum sheet for the PCB circuit board of the present invention;

FIG. 2 is a detailed step flow chart of step S1 in FIG. 1;

fig. 3 is a detailed step flow chart of step S2 in fig. 1.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, referring to fig. 1 to 3, the present invention provides a process data processing method of a coated aluminum sheet for a PCB circuit board, the method comprising the steps of:

In the embodiment of the present invention, as described with reference to fig. 1, the process data processing method of the coated aluminum sheet for a PCB circuit board of the present invention includes the following steps:

The embodiment of the invention collects the matrix alloy data, the reinforcing phase data and the functional coating data of the coated aluminum sheet. These data may be obtained through laboratory tests, literature studies, or material databases. The electronic structure of the coated aluminum sheet is simulated and analyzed using a quantum chemical calculation method (e.g., density functional theory) using the obtained material composition data. This may provide information about interactions between the components. Based on the results of the electronic structure simulation, the interface behavior of the coated aluminum sheet, such as interface stability, interdiffusion, interface reaction, etc., is predicted. This can be achieved by modeling the interface structure and using molecular dynamics modeling, etc.

The embodiment of the invention carries out state simulation analysis on the growth behaviors of the coating in different interface states based on the interface behavior prediction data obtained in the step S1. This may use a physical model or a computational model, such as a phase field model, to simulate the coating growth behavior. And establishing a coating growth dynamics model according to the technological parameters and the growth behavior simulation data in different interface states. This can be modeled using statistical methods, machine learning, or numerical optimization techniques. And obtaining control parameter adjustment data by adjusting the control parameters in real time by using a preset coating thickness target and a coating growth dynamics model. This can be achieved using feedback control algorithms and optimization methods. And constructing a multistage feedback control system according to the control parameter adjustment data by utilizing a closed-loop control theory, thereby obtaining a coating thickness control model. This may include components such as sensors, controllers, and actuators.

The embodiment of the invention collects coating defect data under different process conditions. This can be obtained by experimental observation, detection techniques or analysis of historical data. And carrying out defect attribution analysis on the coating defect data, and determining the source and the reason of the defects. This can be done using statistical analysis, image processing, and expert systems, among other methods. The extracted defect attribution data is correlated with a thickness control model to predict defect conditions of the coating at different interface states. This may be achieved using association rules between the defect data and the model. The optimized process window data is determined by minimizing the coating defect level through an optimization algorithm and a search method using an integrated optimization technique. This may be accomplished using genetic algorithms, simulated annealing, and other optimization algorithms.

The embodiment of the invention obtains the geometric data of the micro-channel inside the coated aluminum sheet through experimental measurement or imaging technology. This may use scanning electron microscopy, computed tomography, etc. techniques to obtain the topographical information of the micro-channels. And constructing a micro-channel geometric model by using the acquired micro-channel geometric data. This may be accomplished using computer aided design software or three-dimensional modeling software. The thermal flow behavior inside the micro-channel is simulated based on the micro-channel geometry model using a finite volume method or other numerical simulation method. This allows for factors such as fluid heat transfer, mass transfer and flow, and coupling corrections to be made to obtain more accurate thermal flow path diagram data.

The embodiment of the invention aligns the heat flow path diagram data obtained in the step S4 with the optimized process window data. This may be achieved by data processing and matching algorithms. And on the basis of temperature parameter alignment, obtaining optimized coating scheme data. This may include suggestions and results in terms of coating material selection, growth process parameter adjustment and optimization, and the like.

The coating aluminum sheet is suitable for a cover plate of a PCB Cheng Zuankong, has good heat conduction performance in the drilling process, and can rapidly take away heat generated in drilling. Simultaneously, prevent to warp in twinkling of an eye when the drill bit is drilled down, make drilling precision keep good, and prevent that the face from producing the burr. The coated aluminum sheet has the following characteristics:

The upper and lower surfaces of the composite aluminum sheet are made of pure aluminum with smooth surface and proper hardness, and the composite aluminum sheet is compounded with a special fiber core layer, and the total thickness is 0.20mm. The composite aluminum sheet has excellent performance and is particularly suitable for the cover plate for micropore processing of a multilayer plate. The cover plate has proper hardness, can prevent burrs from being generated on the surface of the plate during high-speed drilling, and has high stiffness and good elasticity, so that the burrs are not generated. And the drilling position accuracy is improved. The abrasion of the drill bit is reduced, the breakage of the drill bit is prevented, and the service life of the drill bit is prolonged.

Preferably, step S1 comprises the steps of:

Step S11: acquiring material composition data of a coated aluminum sheet, wherein the material composition data comprises matrix alloy data, reinforcing phase data and functional coating data;

Step S12: modeling the electronic structure of the coated aluminum sheet according to the material composition data by utilizing a density functional theory, so as to obtain electronic density distribution data;

Step S13: calculating interaction energy and bonding energy among different components of the coated aluminum sheet based on the electron density distribution data, so as to obtain interaction analysis result data;

step S14: according to the interaction analysis result data, predicting the interface energy and the diffusion coefficient, so as to obtain interface behavior prediction data;

step S15: and carrying out phase field simulation according to the interface behavior prediction data, and mapping the interface states and performances under different conditions to obtain interface characteristic diagram data.

As an embodiment of the present invention, referring to fig. 2, a detailed step flow diagram of step S1 in fig. 1 is shown, and in the embodiment of the present invention, step S1 includes the following steps:

The embodiment of the invention collects the composition information of the coating aluminum sheet matrix alloy, which comprises main alloy elements and the content thereof. Such data may be obtained through chemical composition reports, laboratory tests, or literature studies provided by the material suppliers. If a reinforcing phase such as particles, fibers or crystals is present in the coated aluminum sheet, information on its composition, shape and size needs to be collected. These data can be obtained by microscopic observation, composition analysis and material characterization techniques. If the surface of the coated aluminum sheet has a functional coating, such as an anti-corrosion coating, an anti-wear coating, etc., it is necessary to collect the composition, thickness, structure and performance data of the coating. Such data may be obtained through laboratory testing, specification sheets provided by the coating manufacturer, or related literature.

The embodiment of the invention adopts a DFT equivalent sub-chemical calculation method to model the electronic structure of the coated aluminum sheet. The DFT describes the behavior of electrons based on the density distribution of the wave function. An appropriate computing software package, such as VASP, CASTEP, quantum Espresso, etc., is selected for performing the DFT computation. These software provide tools and algorithms for calculating the electronic structure and density. In the modeling process, parameters such as atomic species, crystal structure, lattice constant and the like in the aluminum sheet of the coating are required to be considered. These parameters may be obtained by experimental measurements, literature studies or structural databases.

The embodiment of the invention uses a DFT calculation method to calculate the interaction energy between different components in the coated aluminum sheet. These calculations can be accomplished by calculating the potential energy of interactions between atoms within the system. By analyzing the electron density distribution, the bonding energy of the chemical bonds in the coated aluminum sheet can be calculated. This may provide information about the strength and stability of the chemical bond. The calculation of interaction energy and bonding energy typically relies on quantum chemistry software packages, such as VASP, CASTEP, gaussian and the like. These software packages provide tools and methods for calculating interaction energy and bonding energy.

The embodiment of the invention can predict the energy condition of the interface of the aluminum sheet coating based on the interaction analysis result data. The interfacial energy difference, i.e., the energy difference between the coating and the substrate, can be calculated to assess the stability and affinity of the interface. From the interaction analysis results, the diffusion coefficient of the interface of the coated aluminum sheet can be predicted. The diffusion coefficient describes the diffusion rate of a substance at the interface and can be calculated by diffusion modeling and statistical mechanical methods.

The embodiment of the invention uses a phase field method to simulate the morphological evolution process of the interface of the coated aluminum sheet. The phase field simulation is a simulation method based on energy functional and is used for describing the morphology and evolution of a material interface. In the phase field simulation process, simulation parameters such as interface energy, interface diffusion coefficient, temperature, pressure and the like need to be set. These parameters can be selected and adjusted based on experimental data, simulation results, and literature studies. According to the result of the phase field simulation, the morphology and structure information of the interface of the coated aluminum sheet under different conditions can be obtained. The information can be used for predicting mechanical property, thermal property, chemical property and the like of the interface, and drawing interface characteristic diagram data.

The present invention obtains matrix alloy data, reinforcement phase data and functional coating data of the coated aluminum sheet, and can provide detailed information about the composition and characteristics of the material. This aids in an in-depth understanding of the composition, structure and properties of the coated aluminum sheet, providing the basis data for subsequent analysis and simulation. And modeling the electronic structure of the coated aluminum sheet by using a density functional theory, and calculating the distribution condition and energy level distribution of electrons. This helps to understand the electronic behavior, density of electronic states, and electronic structural characteristics of the coated aluminum flakes. Based on the electron density distribution data, the interaction energy between the different components in the coated aluminum sheet can be calculated. The method is helpful for researching the energy change and interaction strength between different components, and provides basis for the prediction of the subsequent interface behaviors. Based on the interaction analysis result data, the energy state of the coating aluminum sheet interface can be predicted. This helps to understand the stability, interface energy variation and interaction behavior of the interface, providing a basis for the prediction of interface characteristics. By analyzing the result data of the interaction, the diffusion behavior between the different components in the coated aluminum sheet can be predicted. This helps understand the mass transport characteristics and diffusion rate at the coating interface, providing a basis for evaluation of the performance and stability of the coating. Based on the interface behavior prediction data, phase field simulation can be performed to simulate the distribution and interaction conditions of different components in the coated aluminum sheet. This helps understand the structural evolution and phase separation behavior of the coating interface, providing guidance for coating design and optimization. The state and performance information of the interface of the coated aluminum sheet under different conditions can be obtained through phase field simulation. The interface characteristic diagram data can provide information about interface structure, phase separation degree, interface diffusion and the like, and has important significance for predicting and optimizing coating performance. In summary, the above steps include obtaining material composition data, electronic structure information and interaction analysis result data of the coated aluminum sheet, predicting interface energy and diffusion coefficient, and obtaining coating interface characteristic map data through phase field simulation. These data and predictions are significant for understanding the composition, structure, interactions and interfacial behavior of the coated aluminum sheet, providing scientific basis for coating design, optimization and performance assessment.

Preferably, step S15 comprises the steps of:

step S151: simulating interface states of the coated aluminum sheet under different conditions according to preset phase field simulation parameters and interface behavior prediction data, so as to obtain interface behavior simulation data, wherein the phase field simulation parameters comprise a time range, spatial resolution and boundary conditions;

The embodiment of the invention determines the time span of the phase field simulation, namely the total time required for the simulation. The appropriate time frame is selected according to the specific problem and the purpose of the simulation. The degree of spatial discretization of the phase field simulation, i.e., the size of the division of the material interface into spatial grids, is determined. Smaller spatial resolution may provide finer interface structure information, but also require more computing resources. Boundary conditions of the simulation system are defined, including the external environment and interactions with surrounding materials. The boundary conditions may be a fixed concentration, a fixed temperature, a fixed interface energy, etc. The initial interface state of the coated aluminum sheet is converted into initial conditions for phase field simulation. Experimental data, previous simulation results, or empirical knowledge may be used to determine the initial interface state. According to the set time range and spatial resolution, using phase field equation to make time stepping analog calculation. The phase field equations describe the evolution of phase field variables (e.g., concentration or phase field function). The values of the phase field variables are updated on the spatial grid according to a simulated time step and numerical method (finite difference method or finite element method). And in each time step, calculating the evolution process of the interface of the coated aluminum sheet according to a phase field equation, boundary conditions and initial conditions.

Step S152: calculating concentration distribution of different components in the coating aluminum sheet within the same time step for the interface behavior simulation data, so as to obtain interface state change data;

In each time step in the phase field simulation, the embodiment of the invention calculates the concentration distribution of different components in the coated aluminum sheet according to the values of the phase field variables. The phase field variables are converted into concentration values of the components according to the relationship between the phase field variables and the concentrations defined in the phase field model. And mapping the discrete values of the phase field variables onto the space grid by using a numerical calculation method, such as a difference method or an interpolation method, so as to obtain concentration distribution data of different components.

Step S153: extracting key features of the interface state change data to obtain interface state feature data; calculating performance parameters of the interface state change data to obtain performance parameter data;

According to the embodiment of the invention, a proper feature extraction method is selected according to specific problems and research targets. This may include geometric features of the interface morphology (e.g., interface area, interface curvature, etc.) and dynamic features of the time evolution (e.g., interface growth rate, interface diffusion coefficient, etc.). Key features are extracted from the interface state change data using image processing techniques, mathematical statistics methods, or feature extraction algorithms. And calculating the performance parameters of the interface of the coated aluminum sheet based on the interface state change data. This may include mechanical properties (e.g., interfacial strength, interfacial stress, etc.), thermal properties (e.g., interfacial thermal conductivity, thermal resistance, etc.), or other specific performance metrics. And correspondingly calculating the interface state change data according to a specific performance parameter calculation method and model.

Step S154: and mapping the interface state characteristic data and the performance parameter data correspondingly, so as to obtain interface characteristic diagram data.

The embodiment of the invention corresponds the extracted interface characteristic data and performance parameter data to establish the association relationship between the characteristics and the performance. The feature data and the performance parameter data may be correlated and mapped using statistical analysis methods, machine learning algorithms, or domain expert knowledge. And combining the corresponding mapped characteristic data and performance parameter data to generate interface characteristic diagram data. The interface characteristic diagram may be a two-dimensional or three-dimensional visual representation, wherein the characteristic data may be used for presentation of interface morphology and the performance parameter data may be used for presentation of interface performance. The interface characteristic map data may be plotted and presented using a data visualization tool or programming language to better understand and analyze the interface state characteristics of the coated aluminum sheet under different conditions.

According to the invention, interface state change of the coated aluminum sheet under different conditions can be simulated according to the preset phase field simulation parameters and interface behavior prediction data. This helps to observe and understand the evolution process of the coating interface, predicting the morphology and behavior of the interface under different conditions. By analyzing the interface behavior simulation data, the concentration distribution of different components in the coated aluminum sheet in the same time step can be calculated. This helps to understand the distribution change of each component in the interface, the degree of phase separation, and the interface diffusion behavior. By analyzing the interface state change data, key characteristic information such as interface area, interface shape, phase separation degree and the like can be extracted. This helps to quantitatively describe and compare the characteristics of the coating interface under different conditions, providing a basis for the assessment of the interface characteristics. Based on the interface state change data, performance parameters of the coating interface, such as interface energy, interface diffusion coefficient, interface strength, etc., can be calculated. These performance parameters can evaluate the interfacial stability, interfacial reactivity, and interfacial mechanical properties of the coating. Interface characteristic map data can be generated by mapping the interface state characteristic data and the performance parameter data correspondingly. The graphical data can intuitively display the characteristics and performance changes of the coating interface under different conditions, and help analysts better understand and compare the interface behaviors of the coating. In summary, the above steps include obtaining interface behavior simulation data through phase field simulation, calculating concentration distribution of different components in the coating, extracting interface state characteristics and calculating performance parameters, and generating interface characteristic map data. These data and results can provide quantitative and graphical descriptions of the interfacial behavior of the coating, providing important information for coating design, optimization, and performance assessment.

Preferably, step S2 comprises the steps of:

step S21: performing diffusion process simulation on the growth behaviors of the coating in different interface states according to the interface characteristic diagram data so as to obtain diffusion process simulation data;

step S22: simulating the growth rule of the crystal in the coating growth process according to the interface characteristic diagram data, so as to obtain crystal growth simulation data;

Step S23: performing diffusion rate simulation on interface diffusion in the coating growth process according to the interaction analysis result data, so as to obtain interface diffusion simulation data;

step S24: performing diffusion process simulation on the coating growth behaviors according to the interface diffusion simulation data, the crystal growth simulation data and the growth behavior diffusion process simulation data to obtain diffusion process simulation data;

Step S25: establishing a coating growth dynamics model according to the corresponding technological parameters of different interface states and the growth behavior simulation data;

step S26: the control parameters are adjusted in real time according to the preset coating thickness and a coating growth dynamics model, so that control parameter adjustment data are obtained;

Step S27: and constructing a multi-stage feedback control system according to the control parameter adjustment data by utilizing a closed-loop control theory, thereby obtaining a thickness control model.

As an embodiment of the present invention, referring to fig. 3, a detailed step flow chart of step S2 in fig. 1 is shown, and in the embodiment of the present invention, step S2 includes the following steps:

according to the embodiment of the invention, the simulation calculation of the diffusion process is performed according to the interface characteristic diagram data. Diffusion processes in the growth of the coating are described using diffusion equations or other suitable numerical models and applied under different interface conditions. And carrying out discretization solving on the diffusion equation by using a corresponding numerical method such as a finite difference method or a finite element method. According to the simulation results, simulation data describing the diffusion process in the coating growth under different interface states are obtained.

According to the embodiment of the invention, the simulation calculation of crystal growth is performed according to the interface characteristic diagram data. An appropriate crystal growth model, such as a solidification model or a surface diffusion model, is selected to describe the formation and development of crystals during the growth of the coating. The crystal growth model is discretized using a numerical method, such as the finite element method or the lattice Boltzmann method. According to the simulation results, simulation data describing the growth of crystals in the growth of the coating in different interface states are obtained.

According to the embodiment of the invention, the simulation calculation of the interface diffusion is performed according to the interface characteristic diagram data. According to the physical and chemical characteristics of the coating growth, a proper interface diffusion model is established, such as Fick law or Stefan condition. And carrying out discretization solving on the interface diffusion model by using a numerical method, such as a finite difference method or a finite element method. And obtaining simulation data describing interface diffusion in the growth of the coating under different interface states according to the simulation result.

According to the embodiment of the invention, the simulation calculation of the diffusion process is performed according to the interface characteristic diagram data. The interface and bulk diffusion during diffusion are taken into consideration comprehensively in combination with the diffusion process simulation in step S21 and the interface diffusion simulation in step S23. And carrying out discretization solving on the diffusion process by using a corresponding numerical model and a corresponding method. According to the simulation results, simulation data describing the diffusion process in the coating growth under different interface states are obtained.

The embodiment of the invention combines diffusion process simulation data obtained in the step S24, crystal growth simulation data in the step S22 and interface diffusion simulation data in the step S23 to establish a dynamic model of coating growth. And describing thickness change and evolution rules in the coating growth process by a mathematical equation or other models by considering technological parameters and growth behaviors in different interface states. The kinetic model may be constructed using statistical methods, machine learning methods, or modeling methods based on physical principles.

The embodiment of the invention utilizes the control parameter adjustment data obtained in the step S26 to construct a multi-stage feedback control system. And designing a proper control strategy and algorithm, and carrying out feedback control on the control parameters and the measurement data of the actual coating growth process. Hierarchical control, fuzzy control, adaptive control and other methods can be adopted to realize multi-stage feedback control. And on the basis of a multistage feedback control system, a coating thickness control model is established. And combining the adjustment of the control parameters with thickness measurement data of the actual coating growth process to establish a thickness control model. By controlling the model, the thickness of the coating in the growing process can be accurately controlled.

According to the invention, the diffusion behavior of the coating in the growth process under different interface states can be simulated by analyzing and simulating the interface characteristic diagram data. This helps to understand the diffusion mechanism, diffusion rate, and interface diffusion effects of the different components in the coating on the growth of the coating. Based on the interface characteristic diagram data, the growth rule of the crystal in the coating growth process can be simulated. This helps to understand the morphological evolution, growth rate, and orientation and structural characteristics of the crystal at different interface states. The rate of interfacial diffusion during the growth of the coating can be simulated by analyzing the result data by interaction. This helps to understand the diffusion behavior at different interface states, the kinetic characteristics of the interface diffusion, and the effect of diffusion on the coating structure and performance. The diffusion process simulation can be performed on the coating growth behaviors under different interface states by integrating the interface diffusion simulation data, the crystal growth simulation data and the growth behavior diffusion process simulation data. This helps to study interactions between diffusion and crystal growth in coating growth, interfacial dynamics, and evolution of coating structure and properties. According to the corresponding technological parameters of different interface states and the growth behavior simulation data, a dynamic model of the coating growth can be established. This helps to understand the basic laws of coating growth, interfacial control factors, and optimization of coating structure and performance. The control parameters in the coating growth process can be adjusted in real time through a coating growth dynamics model and a preset coating thickness. This helps to optimize the coating growth conditions so that the thickness of the coating is satisfactory and to achieve precise control of the coating growth process. A multistage feedback control system can be constructed to realize the control of the coating thickness through a closed-loop control theory and control parameter adjustment data. This facilitates real-time monitoring and adjustment of the coating thickness during coating growth to achieve the desired precise control and stability. In summary, the above steps include obtaining diffusion process simulation data, obtaining crystal growth simulation data, obtaining interface diffusion simulation data, establishing a coating growth dynamics model, obtaining control parameter adjustment data, and constructing a thickness control model. These data and models can provide insight into the growth behavior of the coating, precise control of the coating thickness, and optimization of the coating structure and performance.

Preferably, step S26 includes the steps of:

Step S261: acquiring temperature, ion flow, pressure and gas flow rate information in the growth process in real time, so as to obtain parameter acquisition data;

In the coating growth process, the embodiment of the invention uses a sensor or monitoring equipment to collect the parameter data related to the growth process, such as temperature, ion flow, pressure, gas flow rate and the like in real time. The accuracy and stability of the acquisition equipment are ensured so as to acquire reliable parameter acquisition data.

Step S262: coating growth result prediction is carried out according to parameter acquisition data based on a coating growth dynamics model, so that coating growth prediction result data is obtained;

According to the embodiment of the invention, a coating growth dynamics model is established according to the existing knowledge or experimental data. The dynamics model can be a mathematical model based on physical principles, or an empirical model based on statistical or machine learning methods. The model should be selected taking into account the coating materials, growth conditions and desired predicted coating properties. And predicting a coating growth result according to the parameter data acquired in real time by using the established coating growth dynamics model. Inputting the parameter data acquired in real time into a model, simulating the coating growth process and obtaining the predicted result data.

Step S263: performing deviation feature extraction on the predicted result data of the coating growth according to the preset coating thickness, so as to obtain deviation feature data;

According to the embodiment of the invention, the required coating thickness is set as a target value according to specific requirements. And comparing the predicted coating growth result data with a set target coating thickness value, and extracting deviation characteristic data. The deviation characterization data may be the difference between the predicted outcome and the target value or other indicator representing the deviation.

Step S264: performing change constraint according to the reasonable range of each process parameter and the deviation characteristic data, so as to obtain constraint condition data;

According to the embodiment of the invention, the reasonable range of each technological parameter is set according to the technological requirement and the actual condition of the coating growth. These process parameters may include temperature, ion flux, pressure, gas flow rate, etc. And determining constraint condition data by combining the set process parameter range and deviation characteristic data. The constraint data describes a range of variation of the parameters to ensure that the coating growth process is performed within reasonable parameters and the deviation profile is within acceptable limits.

Step S265: and adjusting the control parameters in real time according to the constraint condition data, thereby obtaining control parameter adjustment data.

According to the embodiment of the invention, a proper control algorithm is designed to adjust the control parameters in the coating growth process according to constraint condition data. Common control algorithms include proportional-integral-derivative control (PID control), fuzzy control, and the like. And adjusting control parameters in the coating growth process in real time according to the constraint condition data and the control algorithm. The adjustment of the control parameters should be based on real-time collected parameter data to maintain the stability and accuracy of the coating growth process.

The invention can acquire the key parameter data related to the growth of the coating by acquiring the temperature, the ion flow, the pressure, the gas flow rate and other information in the growth process in real time. This helps to monitor and understand the physical and chemical changes during the growth of the coating, providing a basis for experimental data. Based on the coating growth dynamics model and the parameter acquisition data, the prediction of the coating growth result can be performed. The growth trend of the coating, the thickness and the characteristics of the coating can be known through the predicted result data, and the follow-up growth strategy and the optimized control parameters can be formulated. By comparing the predicted coating growth result with the preset coating thickness, deviation characteristic data in the coating growth process can be extracted. These characteristic data reflect the differences between the coating growth results and the target requirements, helping to assess the accuracy and stability of the coating growth. Based on reasonable ranges of the process parameters and deviation feature data, changing constraints during the coating growth process can be determined. These constraints are used to limit or direct the range of adjustment of the control parameters to ensure that the coating growth process is within a controllable range and close to the desired result. Based on the constraint condition data, the control parameters can be adjusted in real time to optimize the coating growth process and meet the expected requirements. Through real-time control parameter adjustment, the coating growth process can be accurately controlled and optimized, and the quality and performance of the coating are improved. In summary, the above steps include acquisition of parameter acquisition data, acquisition of coating growth prediction result data, extraction of deviation feature data, determination of constraint condition data, and real-time optimization of control parameter adjustment data. These data and optimization processes help to achieve precise control, quality optimization, and performance improvement of the coating growth process.

Preferably, step S3 comprises the steps of:

step S31: obtaining coating defect data under different process conditions;

The embodiments of the present invention use different process conditions for coating growth in a laboratory or production environment. Process conditions may include variations in parameters such as temperature, ion flux, pressure, gas flow rate, etc. Under each process condition, defect detection and analysis was performed on the coating samples. The coating is inspected for defects using suitable inspection methods and equipment, such as a microscope, scanning Electron Microscope (SEM), etc., and defect data recorded.

Step S32: performing defect attribution analysis on the coating defect data, and extracting a mapping relation to obtain defect attribution data;

According to the embodiment of the invention, attribution analysis is carried out on the acquired coating defect data, and the formation reason and possible influence factors of each defect are determined. Attribution analysis may be performed by experimental observation, statistical analysis, expert knowledge, experience, and the like. And extracting mapping relations between different defect types and process conditions by analyzing the defect attribution data. These mappings may be quantitative (e.g., a numerical model) or qualitative (e.g., rules or empirical knowledge).

Step S33: performing defect prediction according to the defect attribution data and the thickness control model, so as to obtain defect prediction data;

Embodiments of the present invention build a thickness control model that describes the relationship between coating thickness and process conditions. The model may be built based on physical principles, statistical methods, or machine learning algorithms. And performing defect prediction by using the defect attribution data and the thickness control model. The process conditions are input into a thickness control model, the thickness of the coating is predicted, and the type and extent of defects that may occur are predicted from the defect attribution data.

Step S34: and obtaining the defect prediction data by using an integrated optimization technology to minimize the defect level, thereby obtaining the optimized process window data.

The embodiment of the invention optimizes the defect prediction data by using an integrated optimization technology (such as genetic algorithm, particle swarm optimization and the like). The goal of the optimization is to find the optimal combination of process parameters to achieve the minimum defect level. And obtaining the optimal process parameter combination according to the result of the integrated optimization technology. These parameter combinations define a process window in which the thickness of the coating is controlled within a predetermined range and the defect level is minimized.

According to the invention, the defect database can be established by acquiring the coating defect data under different process conditions, and a large amount of actual production data can be obtained. These data can be used to analyze and evaluate the mechanism of defect formation during coating growth, helping to identify potential problems and points of improvement. By analyzing the defect attribution of the coating defect data, the influence degree of different factors on defect formation can be determined, and a main defect source can be found out. Meanwhile, the mapping relation is extracted to reveal the association rule between different parameters and defect types, so that the deep understanding of a defect forming mechanism is facilitated. Based on the defect attribution data and the thickness control model, defect prediction can be performed. By predicting defect levels of the coating under different process conditions, the impact of different parameter settings on defect formation can be evaluated and guidance and reference can be provided for formulating an optimized process. The defect prediction data may be analyzed and optimized by integrated optimization techniques to obtain a process parameter combination that minimizes the defect level. These optimized process window data can be used to guide parameter settings during production, help improve coating quality, reduce defect incidence, and improve production efficiency. In summary, the above steps include obtaining coating defect data, extracting defect attribution data and mapping relationships, performing defect prediction, and obtaining optimized process window data. These data and analysis processes help to understand the defect formation mechanism, optimize process parameters, thereby improving the quality and performance of the coating, reducing the incidence of defects, and improving production efficiency and reliability.

Preferably, step S32 comprises the steps of:

Step S321: performing defect type classification on the coating defect data to obtain defect classification data;

The coating defect data collected by the embodiment of the invention can be data obtained in an experiment or a production process. Coating defect data should include a variety of different types of defect samples. The collected defect data is classified into different defect types. Classification may be based on the morphology, features, or other relevant attributes of the defect.

Step S322: tracing the defect classification data, and counting the number of the defects under different process conditions to obtain defect source data;

The embodiment of the invention traces the source of each defect type sample and determines the generation reason or source thereof. The tracing can be performed by experimental observation, data analysis, expert knowledge, experience and other methods. The number of each defect type produced under different process conditions is counted. For each process condition, the number of samples for each defect type is calculated.

Step S323: corresponding the process conditions and the number of each defect type according to the defect source data, so as to obtain defect process parameter related data;

The embodiment of the invention corresponds the defect source data to the technological parameters of the coating. And determining the association relation between each defect type and the technological parameters according to the defect tracing result. And establishing a defect technological parameter association data table, and recording the association relation between each defect type and the corresponding technological parameter. The association data table may be a matrix in which rows represent defect types, columns represent process parameters, and elements in the table represent correlation strengths or other relevant indicators.

Step S324: analyzing the correlation strength between the technological parameters and the defect types according to the defect technological parameter correlation data by a Pelson correlation method, so as to obtain correlation strength data;

The embodiment of the invention uses the pearson correlation coefficient or other correlation analysis methods to evaluate the correlation strength between the technological parameters and the defect types. A correlation coefficient between each process parameter and each defect type is calculated. Based on the result of the correlation analysis, correlation strength data between the process parameters and the defect types are obtained. A numerical value or other index may be used to represent the degree of correlation.

Step S325: removing the process parameters with weak correlation according to the data with weak correlation, and screening to obtain key process parameter data with dominant influence of the defect type;

according to the embodiment of the invention, the technological parameters with weak correlation with the defect type are eliminated according to the correlation strength data. And the process parameters with strong correlation with the defect type are reserved as key process parameters. A critical process parameter dataset is obtained containing process parameters related to defect type dominant effects.

Step S326: and performing relation mapping on the defect classification data and the key process parameter data so as to obtain defect attribution data.

The embodiment of the invention carries out relation mapping on the defect classification data and the key process parameter data. The mapping analysis may be performed using data analysis methods, machine learning algorithms, statistical models, or the like. And obtaining defect attribution data based on the relation mapping result of the key process parameter data and the defect classification data. The defect attribution data describes the association between different defect types and key process parameters.

The invention can distinguish and mark different types of defects by classifying the coating defect data, thereby obtaining defect classification data. This facilitates the study and analysis of different types of defects, further understanding the nature and mechanism of formation of coating defects. By traceable analysis of the defect classification data, the source and the generation path of different defect types can be determined. Meanwhile, the influence degree of different process parameters on defect formation can be known by counting the generation quantity of each defect type under different process conditions, and a basis is provided for subsequent defect prediction and optimization. By analyzing the defect source data, the process conditions and the number of each defect type can be corresponding to obtain the defect process parameter related data. These data reflect the extent to which different process parameters affect different defect types, helping to determine the relationship between critical process parameters and defect types. By applying the pearson correlation method, the defect process parameter correlation data can be analyzed, and the degree of correlation between the process parameters and the defect types can be estimated. These data may help determine which process parameters are closely related to a particular defect type, further screening and optimizing key process parameters. Based on the correlation strength data, the process parameters with weak correlation with the defect type can be eliminated, and key process parameter data with dominant influence of the defect type can be obtained through screening. These key process parameters are the main contributors to the creation of a particular defect type, providing guidance for optimizing process conditions and reducing defects. Through relation mapping of the defect classification data and the key process parameter data, the association relation between different defect types and the key process parameters can be determined, and defect attribution data is further obtained. These data reveal causal relationships between defect types and process parameters, aid in understanding defect formation mechanisms in depth, and provide basis for developing targeted defect prevention and improvement measures. In summary, the above steps include obtaining defect classification data, defect source data, defect process parameter association data, correlation strength data, critical process parameter data, and defect attribution data. The data and analysis process are helpful for understanding the characteristics, formation mechanism and relation with process parameters of different defects, and provide guidance and basis for optimizing process conditions, reducing defect rate and improving product quality.

Preferably, step S4 comprises the steps of:

step S41: scanning and imaging a micro-channel structure inside the coating aluminum sheet through scanning electron microscope equipment, so as to obtain micro-channel geometric data;

The embodiment of the invention uses scanning electron microscope equipment to scan and image the micro-channel structure inside the coated aluminum sheet. Ensuring proper scan parameter settings to obtain high quality microscope images. The scanned microscope image is preprocessed, such as denoising, contrast enhancement and the like, so as to improve the image quality. And (3) dividing and detecting edges of the preprocessed microscope image by using an image processing technology so as to extract geometric data of the micro-channel. The shape information of the micro-channel can be extracted by using threshold segmentation, edge detection algorithm and the like.

Step S42: performing digital processing on the geometric data of the micro-channel, and extracting the shape boundary line of the micro-channel, thereby obtaining the boundary line data of the micro-channel;

The embodiment of the invention carries out digital processing on the extracted micro-channel geometric data and converts the extracted micro-channel geometric data into a data format which can be processed by a computer. The shape data of the micro-channels may be converted into digitized boundary line data using an image processing algorithm or a geometric data processing algorithm. The shape boundary line of the micro-channel is extracted from the digitized micro-channel data. The boundary line data of the micro-channel can be extracted using curve fitting, edge tracking, etc. algorithms.

Step S43: establishing a three-dimensional geometric model of the micro-channel by utilizing CAD according to the micro-channel boundary line data, thereby obtaining the micro-channel geometric model;

the embodiment of the invention utilizes Computer Aided Design (CAD) software to establish a three-dimensional geometric model of the micro-channel according to the micro-channel boundary line data. Drawing and modeling are performed using a drawing tool provided by CAD software to ensure that the geometric model matches the shape of the actual microchannel.

Step S44: gridding the micro-channel geometric model, and endowing physical properties to the cell units so as to obtain a micro-channel grid model;

According to the embodiment of the invention, the micro-channel geometric model is gridded and divided into discrete cell units. Gridding may use grid generation software or algorithms to transform the microchannel geometry model into a discrete grid model. Physical properties such as size, material properties, etc. are imparted to the cell units after meshing. The physical properties may be set according to actual conditions, including thermal conductivity, heat capacity, and the like.

Step S45: and simulating the heat flow behavior in the micro-channel according to the micro-channel grid model by a finite volume method, and correcting the coupling effect so as to obtain heat flow path diagram data.

The embodiment of the invention is based on a micro-channel grid model, and utilizes a finite volume method or other heat flow simulation methods to simulate the heat flow behavior in the micro-channel. And calculating heat flow parameters such as temperature distribution, flow velocity distribution and the like in the micro-channel according to the heat conduction equation and the hydrodynamic equation. Consider the coupling between different physical processes within a microchannel, such as heat transfer and convection of fluids, modification of boundary conditions, and the like. And (3) carrying out correction and adjustment of the coupling effect according to actual conditions and simulation requirements so as to obtain a more accurate thermal flow simulation result. Based on the results of the thermal flow simulation, parameters or features of interest, such as temperature gradients, flow velocity profiles, etc., are extracted to generate thermal flow path map data. The simulation results may be visualized as a thermal flow path graph using visualization software or data processing tools to intuitively demonstrate thermal flow behavior.

The invention uses scanning electron microscope equipment to scan the coated aluminum sheet with high resolution to obtain the geometric shape and structure information of the micro-channel. By such scanning imaging techniques, details and features within the micro-channels, such as channel size, shape, arrangement, etc., can be obtained. These microchannel geometry data are important for subsequent analysis and modeling. The microchannel geometry data obtained from the scanning electron microscope is digitized for subsequent processing and analysis. Shape boundary line data of the micro-channel, namely, contour lines inside the micro-channel can be extracted through proper image processing and edge detection algorithms. These boundary line data are used for subsequent modeling and simulation. A three-dimensional geometric model of the microchannel is constructed from boundary line data of the microchannel using Computer Aided Design (CAD) software. The actual geometric shape of the micro-channel can be accurately reconstructed through the modeling function of CAD software, and the actual geometric shape comprises parameters such as the length, the width, the height and the like of the channel. This three-dimensional geometric model is the basis for subsequent simulation and analysis. The three-dimensional geometric model of the microchannel is gridded to divide it into a number of small cell units. The meshing process can discretize the geometry of the micro-channels, making it suitable for numerical simulation and computation. Each cell unit may be given specific physical properties, such as temperature, flow rate, etc., to describe the flow characteristics inside the microchannel. The microchannel mesh model thus obtained can be used for subsequent thermal flow simulation. And simulating the heat flow behavior in the micro-channel based on the grid model of the micro-channel by using a numerical simulation method such as a finite volume method. By solving the hydrodynamic and thermal conduction equations, the related parameters such as temperature distribution, flow velocity distribution and the like in the micro-channel can be obtained. In addition, coupling correction may be performed to account for thermal conduction and thermal convection effects between the fluid and the solid. Finally, according to the simulation result, heat flow path diagram data can be generated, and the transmission path and the characteristics of the heat flow in the micro-channel are displayed. The geometric data and boundary line data of the micro-channel can be acquired through imaging and digital processing of a scanning electron microscope. This allows researchers to know in detail the features of the structure, dimensions and shape inside the microchannel, providing an accurate basis for subsequent modeling and simulation. The actual shape and size of the micro-channel can be accurately reconstructed by establishing a three-dimensional geometric model through CAD software according to the boundary line data of the micro-channel. This geometric model provides a basis for subsequent numerical modeling and analysis to better understand and predict the thermal flow behavior within the microchannel. The microchannel geometric model is subjected to gridding, is discretized into small cell units and is endowed with physical properties, and can be used for numerical simulation and calculation. The grid model can solve hydrodynamic and thermal conduction equations more conveniently, so that the thermal flow behavior inside the micro-channel is simulated. And carrying out thermal flow simulation on the grid model of the micro channel by using a numerical simulation method, such as a finite volume method. By solving the corresponding equation, parameters such as temperature distribution, flow velocity distribution and the like in the micro-channel can be known, and the heat transmission path and characteristics can be predicted. This is important to optimize the design and performance of the micro-channel. In the thermal flow simulation, the coupling effect correction can be performed by considering the heat conduction and the heat convection effect between the fluid and the solid. Therefore, the thermal flow behavior in the micro-channel can be described more accurately, and the accuracy of the simulation result is improved.

Preferably, step S45 comprises the steps of:

Step S451: defining initial and boundary conditions of heat flux and temperature at the cell unit boundary based on the microchannel mesh model, thereby obtaining a thermal conductance mesh model;

The embodiments of the present invention define initial conditions and boundary conditions for heat flux and temperature at the cell unit boundaries of the microchannel mesh model. The initial conditions represent the temperature distribution at the start of the simulation, and the boundary conditions represent the heat transfer conditions at the cell boundaries, such as fixed temperature, convective heat transfer, etc. And establishing a thermal conductance grid model according to the micro-channel grid model and the defined boundary conditions. In the thermal conductivity grid model, the nodes of each cell unit contain temperature information, and the temperature distribution can be calculated through thermal conductivity between the nodes.

Step S452: extracting a temperature change differential equation of the thermal conductivity grid model according to a preset time step, so as to obtain heat transfer rate equation data;

According to the embodiment of the invention, the temperature change in the thermal conductivity grid model is converted into a differential equation according to the preset time step. The differential equation describes the temperature change relationship in adjacent time steps and can be derived from the thermal conduction equation. And extracting data of the heat transfer rate equation according to the deduction result of the difference equation, wherein the data comprise the relation between related coefficients and nodes.

Step S453: differentiating the heat transfer rate equation data by a finite difference method, and performing iterative solution to obtain temperature initial value estimated data of the next time step;

The embodiment of the invention utilizes a finite difference method to differentiate the heat transfer rate equation data, and converts a differential equation into a discrete differential equation. The finite difference method approximates the derivative term in the differential equation with the differential term, thereby obtaining a discrete numerical calculation method. And carrying out iterative solution on the differential equation, and calculating the temperature initial value estimated data of the next time step by using a numerical calculation method. Common iterative solving algorithms include an explicit difference method, an implicit difference method, a catch-up method and the like, and suitable methods can be selected according to specific situations.

Step S454: extracting coupling influence of convection on mass transfer according to the temperature initial value estimated data, and correcting boundary conditions to obtain an optimized thermal conductivity grid model;

according to the embodiment of the invention, the coupling influence of the convection and mass transfer is extracted according to the temperature initial value pre-estimated data. The coupling effect can be calculated and corrected according to the fluid mechanics theory and the heat and mass transfer theory. And correcting and optimizing the boundary conditions according to the extraction result of the coupling influence so as to describe the heat transfer process more accurately. The correction includes adjusting the boundary temperature, changing the convective heat transfer coefficient, and the like.

Step S455: and drawing heat flow path diagram data according to the optimized heat conduction grid model by utilizing numerical drawing software.

The embodiment of the invention uses proper numerical drawing software to draw heat flow path diagram data according to the optimized heat conduction grid model. The graph drawn by the numerical drawing software can show the heat flow characteristics such as temperature distribution, heat flow direction, strength and the like in the micro-channel. These data can be used to analyze and evaluate the heat transfer performance of the microchannels.

The present invention defines initial and boundary conditions of heat flux and temperature at the boundaries of the cell units according to a microchannel mesh model. The heat flux represents the amount of thermal energy transferred through the cell surface, while the temperature initiation and boundary conditions determine the initial temperature and boundary temperature during the simulation. By defining these conditions, a thermal conductivity mesh model can be constructed to describe the thermal conductivity behavior inside the microchannel. And extracting a temperature change differential equation from the thermal conductivity grid model according to a preset time step. This differential equation describes the temperature change over time, which is based on the heat transfer equation and the discretized grid model. By solving the differential equation, heat transfer rate equation data, i.e., equations describing the heat transfer rate inside the microchannel, can be obtained. Discretizing the heat transfer rate equation data by using a finite difference method, and carrying out iterative solution. The finite difference method converts the differential equation into a discrete differential equation, and the temperature initial value estimated data of the next time step can be obtained through iterative calculation. These predicted data are used for coupling influence extraction and boundary condition correction in the next step. And extracting the coupling influence of the convection on mass transfer according to the temperature initial value pre-estimated data. Convection flow considers the effect of fluid flow in the microchannel on temperature conduction. And according to the result of the coupling influence extraction, the boundary condition can be corrected, and the thermal conductance grid model is further optimized. The modified model more accurately describes the thermal conduction and flow behavior inside the microchannel. And drawing heat flow path diagram data according to the optimized heat conduction grid model by utilizing numerical drawing software. The heat flow path diagram shows the transmission path and characteristics of heat flow in the micro-channel, and can intuitively show the heat flow behavior in the micro-channel. These graphical data help researchers better understand and analyze the heat conduction and flow characteristics inside the microchannels. By combining the microchannel mesh model with the thermal conductivity equation, a numerical model of the system can be built for predicting and analyzing the thermal conduction and flow behavior inside the microchannel. This helps engineers and researchers to gain insight into the thermal performance of the microchannel system, optimizing design and operating parameters. By defining and modifying the initial and boundary conditions of heat flux, temperature, the operating state of the actual system can be more accurately described. Accurate definition of these conditions can improve the predictive power of the model and improve the reliability of the simulation results. Extracting the heat transfer rate equation data allows for knowledge of the heat transfer rate distribution inside the microchannel. This is important to study the distribution and transfer paths of heat in the microchannels, helping to optimize the design and performance of the heat transfer device. Discretizing the differential equation by a finite difference method, and performing iterative solution to obtain the temperature initial value estimated data of the next time step. The numerical simulation method can efficiently solve the complex heat transfer problem and provides a basis for further analysis and optimization. The heat conduction and flow behavior inside the microchannel can be described more accurately by taking into account the coupling effect of convection on mass transfer and by making modifications to the boundary conditions. This helps to optimize the thermal conductivity mesh model, improving the accuracy and reliability of the simulation results. The heat flow characteristics inside the micro-channel can be intuitively displayed by drawing the heat flow path diagram data by numerical drawing software. Such visual analysis helps researchers better understand and interpret simulation results, discover potential thermal flow problems, and guide further research and improvement. In summary, these steps include increasing model predictive power, optimizing design and operating parameters, improving reliability of simulation results, and providing a basis for analysis and optimization of thermal conduction and flow behavior.

Preferably, step S5 comprises the steps of:

Step S51: extracting temperature change rules of different depth positions in the micro-channel from the heat flow path diagram data, thereby obtaining temperature distribution data;

The embodiment of the invention analyzes and processes the heat flow path diagram data to extract the temperature change rules of different depth positions in the micro-channel and obtain the temperature distribution data. Temperature data associated with different depth locations within the microchannel are extracted from the thermal flow path map data. This may be achieved by image processing techniques, pixel value extraction, or other related methods. And analyzing the extracted temperature data to know the change rule of the temperature in the micro-channel. Statistical methods, mathematical models, or other analytical techniques may be used to study temperature distribution, gradients, and trends. Based on the results of the data analysis, data describing the temperature distribution within the microchannel is generated. This may be a list of temperature values, a heat map or other form of data representation.

Step S52: extracting key process parameters affecting the coating quality and the variation range thereof from the optimized process window data, thereby obtaining key parameter range data;

The embodiment of the invention analyzes the optimized process window data and knows the influence of different process parameters on the coating quality. This may be achieved by statistical analysis, experimental design, or other related methods. And determining key process parameters affecting the coating quality according to the data analysis result. These parameters may relate to spray speed, spray distance, spray angle, spray pressure, etc. For each key process parameter, the range of variation thereof is determined. This may be determined experimentally, empirically, or by other means to ensure that the coating quality is within a reasonable range. Based on the extraction of the key parameters and the determination of the variation range, data describing the key parameter range is generated. This may be a list of a set of parameter values, a parameter range map, or other form of data representation.

Step S53: and carrying out temperature parameter alignment on the temperature distribution data and the key parameter range data, thereby obtaining optimized coating scheme data.

The embodiment of the invention aligns the temperature distribution data with the key parameter range data. This may involve matching, interpolating or mapping the temperature data with corresponding key parameter ranges according to depth position. Based on the results of the data alignment, optimized coating scheme data is generated. This may include suggested combinations of process parameters, temperature control strategies, or other relevant data representations. And evaluating and verifying the generated optimized coating scheme data. Numerical models, simulation methods, or experimental verification may be used to evaluate the performance and feasibility of the coating scheme.

The invention analyzes the heat flow path diagram data and extracts the temperature change rules of different depth positions in the micro-channel. By observing and analyzing the heat flow path diagram, the temperature distribution conditions at different positions inside the micro-channel can be known, including the change trend and gradient of the temperature. By extracting the temperature change rules, temperature distribution data in the micro-channel, namely temperature values at different positions, can be obtained. The optimized process window data is analyzed to extract key process parameters affecting the coating quality and their range of variation. Optimizing a process window refers to a combination of process parameters that can achieve the best coating quality under specific process conditions. By analyzing these data, key parameters affecting the coating quality can be determined and their range of variation can be known. These critical parameter range data are important for formulating an optimized coating scheme. The temperature profile data is aligned with the critical parameter range data to obtain optimized coating scheme data. By comparing and analyzing the temperature profile data with the critical parameter ranges, the optimal combination of process parameters under different temperature conditions can be determined. In this way, an optimized coating scheme can be formulated to achieve optimal coating quality over a given temperature range. These optimized coating scheme data may guide the selection and optimization of the actual coating process. By analyzing the thermal flow path diagram data, temperature distribution data at different positions in the micro-channel can be obtained. These data provide information about the temperature change law and gradients, helping to understand the thermal flow behavior and thermal conductivity characteristics. This is important for optimizing the coating process and improving the heat transfer properties. By analyzing the optimized process window data, the key process parameters affecting the coating quality and the variation range thereof are extracted. These critical parameter range data are instructive in determining process conditions, optimizing coating properties. They can assist engineers and researchers in determining the proper process parameter ranges to achieve the desired coating quality. By aligning the temperature profile data with the critical parameter range data, an optimal combination of process parameters under different temperature conditions can be determined. In this way, an optimized coating scheme can be formulated to achieve optimal coating quality over a given temperature range. This alignment helps to improve the efficiency and quality of the coating preparation and can reduce trial and error and adjustment time. In summary, the above steps include providing information about the temperature profile and gradients, determining the critical process parameter ranges that affect the coating quality, and developing optimized coating schemes for different temperature conditions. These effects help to guide the selection and optimization of the coating process, improve the coating quality and improve the production efficiency.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The process data processing method of the coated aluminum sheet for the PCB is characterized by comprising the following steps of:

2. The process data processing method of a coated aluminum sheet for a PCB wiring board according to claim 1, wherein the step S1 comprises the steps of:

3. The process data processing method of a coated aluminum sheet for a PCB wiring board according to claim 2, wherein step S15 comprises the steps of:

4. A process data processing method of a coated aluminum sheet for a PCB wiring board according to claim 3, wherein the step S2 comprises the steps of:

5. The process data processing method of a coated aluminum sheet for a PCB panel as claimed in claim 4, wherein the step S26 comprises the steps of:

6. The process data processing method of a coated aluminum sheet for a PCB panel according to claim 5, wherein the step S3 comprises the steps of:

step S31: obtaining coating defect data under different process conditions;

7. The process data processing method of a coated aluminum sheet for a PCB panel as set forth in claim 6, wherein the step S32 includes the steps of:

8. The process data processing method of a coated aluminum sheet for a PCB panel according to claim 7, wherein the step S4 comprises the steps of:

9. The process data processing method of a coated aluminum sheet for a PCB panel according to claim 8, wherein the step S45 comprises the steps of:

10. The process data processing method of a coated aluminum sheet for a PCB panel according to claim 9, wherein the step S5 comprises the steps of: