Electroplating process parameter optimization control simulation system based on intelligent algorithm

Abstract

The invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and belongs to the technical field of electroplating process simulation, the system comprises a relation module, a model module and a system module, a plurality of optimization targets are identified, quantitative indexes are set, and the relation between the targets and electroplating process parameters is established; according to the method, an intelligent algorithm is utilized to optimize process parameters and generate an optimization result on the basis of considering limiting conditions, a simulation model is constructed, a user-friendly front-end interface and a user-friendly rear-end interface are developed, the simulation model is integrated, user operation and result checking are facilitated, efficient and stable electroplating process optimization is achieved, and the overall performance of the electroplating process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electroplating process simulation, and in particular to an electroplating process parameter optimization control simulation system based on intelligent algorithm. Background Art

[0002] Electroplating processes are widely used in electronics, automobiles, aviation and other industries, involving important functions such as surface treatment and corrosion protection. With the development of industrial technology, the complexity and requirements of electroplating processes have increased, but traditional methods often only focus on a single goal and rely on manual experience, resulting in low optimization efficiency and unstable results. At the same time, the lack of an intuitive user interface makes it difficult for users to operate and view results conveniently.

[0003] Therefore, the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm. Summary of the invention

[0004] The present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm. The system identifies multiple optimization targets and sets quantitative indicators, establishes the relationship between the targets and the electroplating process parameters, utilizes the intelligent algorithm to optimize the process parameters on the basis of considering restriction conditions, generates optimization results, and constructs a simulation model. It develops user-friendly front-end and back-end interfaces, integrates the simulation model, facilitates user operation and result viewing, realizes efficient and stable electroplating process optimization, and improves the overall performance of the electroplating process.

[0005] The present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, comprising:

[0006] Relationship module: determine multiple main targets to be optimized, set quantitative indicators for each main target, and determine the correlation between the main target and the electroplating process parameters based on the quantitative indicators;

[0007] Model module: Select an intelligent algorithm based on the correlation, optimize the electroplating process parameters according to the intelligent algorithm and the main objectives, obtain the optimization results, and determine the constraints of each objective, and then build a simulation model according to the optimization results and the constraints;

[0008] System module: Develop front-end and back-end interfaces based on the simulation model, add functions to the user filing information according to the front-end interface, integrate the simulation model into the back-end interface to form a simulation system.

[0009] The present invention provides an electroplating process parameter optimization control simulation system based on intelligent algorithm, and a relationship module, comprising:

[0010] Quantification unit: determine the main objectives of the electroplating process, and quantify the main objectives according to user needs to obtain quantitative indicators;

[0011] Relationship analysis unit: collects historical data related to the electroplating process, performs correlation analysis on the quantitative indicators and the electroplating process parameters based on the historical data, and determines the correlation between each main target and the electroplating process parameters according to the correlation analysis results.

[0012] The present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, a quantization unit, comprising:

[0013] Extract features of user needs and obtain a demand feature set based on the extracted features;

[0014] Based on the demand feature set and in combination with a preset feature-target-indicator comparison table, a quantitative indicator that is compatible with the main objective is selected from the indicator database.

[0015] The present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, a relationship analysis unit, comprising:

[0016] Rank correlation coefficient calculation subunit: calculate the rank correlation coefficient between the quantitative index and the electroplating process parameters:

[0017] Among them, r s,w represents the weighted rank correlation coefficient between the quantitative index and the electroplating process parameters; w i represents the weight of the i-th electroplating process parameter; n represents the number of electroplating process parameters, where the number of electroplating process parameters corresponds to the quantitative index one by one; represents the rank of the i-th electroplating process parameter; represents the rank of the quantitative index corresponding to the i-th electroplating process parameter; n(n 2 -1) represents the standardization factor of the weighted rank correlation coefficient;

[0018] Inspection subunit: set the inspection range, perform rank inspection on the electroplating process parameters and quantitative indicators corresponding to the weighted rank correlation coefficients within the inspection range, obtain the correlation analysis results based on the rank test results, and then determine the correlation between each main target and the electroplating process parameters.

[0019] The present invention provides an electroplating process parameter optimization control simulation system based on intelligent algorithm, and the inspection subunit includes:

[0020] First range block: obtaining changes in the external environment and setting a first dynamic range according to the changes;

[0021] Second range block: obtaining an application field corresponding to the electroplating process, and setting a second dynamic range according to the application field;

[0022] Comprehensive range block: sets the inspection range based on the first dynamic range and the second dynamic range.

[0023] The present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, a model module, comprising:

[0024] Parameter unit: performing characteristic analysis on the correlation, selecting an intelligent algorithm from a characteristic-algorithm table according to the characteristic analysis result, and determining the electroplating process parameters to be optimized according to the characteristic analysis result;

[0025] Combination determination unit: Generates first-level individuals of electroplating process parameters based on intelligent algorithms, calculates the fitness value of each first-level individual, sorts the first-level individuals according to the fitness value, and groups the first-level individuals with fitness values ​​higher than the first preset fitness values ​​into groups of six as candidate parameter combinations;

[0026] Optimization unit: using a roulette wheel to select a secondary individual from the candidate parameter combination, cross-linking the secondary individuals to generate new secondary individuals, mutating the new secondary individuals, and iterating. If there is a fitness value corresponding to a secondary individual higher than a second preset fitness value, the iteration is terminated, and the corresponding secondary individual is set as an optimized individual. According to the optimized individual, the corresponding electroplating process parameters are optimized to obtain an optimization result;

[0027] Model building unit: setting constraints for each main objective based on the correlation, and building a simulation model in combination with the constraints and optimization results.

[0028] The present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and a model building unit, comprising:

[0029] Constraint condition subunit: obtain industry standards, user requirements and production capacity, formulate initial conditions for each major target according to industry standards, user requirements and production capacity, and adjust the initial conditions based on the historical data to obtain constraint conditions;

[0030] Construct subunits: Prioritize the optimization results corresponding to each main goal according to user requirements and production capacity, and combine the priority division results with the constraints to construct the simulation model.

[0031] The present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, the system module comprising:

[0032] User unit: obtain the user record information and corresponding usage scenarios of the main users of the system, and determine the operation process and interaction method of the main users in the system according to the user record information and usage scenarios of the main users;

[0033] Division unit: Determine the functions to be implemented by the system based on the main objectives, and divide the system into functions according to the functions to be implemented;

[0034] Front-end unit: determining a first basic interface based on the operation process and the interaction mode, and determining a front-end interface according to the functional division result and the first basic interface;

[0035] Back-end unit: determine the second basic interface based on the simulation model, and determine the back-end interface according to the functional division result and the second basic interface;

[0036] System construction unit: constructs a simulation system based on the front-end interface and the back-end interface, and outputs a specific report on the optimization control of electroplating process parameters according to the simulation system.

[0037] The present invention provides an electroplating process parameter optimization control simulation system based on intelligent algorithm, the user unit comprises:

[0038] Scenario determination subunit: obtaining user record information of main users of the system, classifying the main users into types according to the user record information, and defining usage scenarios based on the type classification results;

[0039] Operation subunit: Draw a user operation flow chart using a column diagram tool based on the usage scenario, and define the operation process and corresponding interaction method for each type of classification result.

[0040] The present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, wherein user filing information includes: user basic information, user usage scenarios, user needs and expectations, user technical background, user roles and permissions.

[0041] Compared with the prior art, the beneficial effects of the present application are as follows: by identifying multiple optimization targets and setting quantitative indicators, establishing the relationship between the targets and the electroplating process parameters, using intelligent algorithms to optimize the process parameters based on considerations of constraints, generating optimization results, and building a simulation model, developing user-friendly front-end and back-end interfaces, integrating simulation models, facilitating user operation and result viewing, achieving efficient and stable electroplating process optimization, and improving the overall performance of the electroplating process.

[0042] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a structural schematic diagram of an electroplating process parameter optimization control simulation system based on an intelligent algorithm provided in an embodiment of the present invention;

[0046] Figure 2 It is the process optimization principle diagram of the electroplating process parameter optimization control simulation system. DETAILED DESCRIPTION

[0047] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0048] Embodiment 1:

[0049] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on intelligent algorithm, such as Figure 1 As shown, including:

[0050] Relationship module: determine multiple main targets to be optimized, set quantitative indicators for each main target, and determine the correlation between the main target and the electroplating process parameters based on the quantitative indicators;

[0051] Model module: Select an intelligent algorithm based on the correlation, optimize the electroplating process parameters according to the intelligent algorithm and the main objectives, obtain the optimization results, and determine the constraints of each objective, and then build a simulation model according to the optimization results and the constraints;

[0052] System module: Develop front-end and back-end interfaces based on the simulation model, add functions to the user filing information according to the front-end interface, integrate the simulation model into the back-end interface to form a simulation system.

[0053] In this embodiment, the main goals are the key performance indicators that are desired to be achieved in the electroplating process, which typically include coating thickness, ensuring that the coating reaches a predetermined thickness, adhesion, improving the bonding strength between the coating and the substrate, uniformity, ensuring the thickness consistency of the coating over the entire surface, and corrosion resistance, improving the coating's ability to resist corrosion. For example, the system sets a target coating thickness of 20 μm, and monitors and adjusts parameters in real time to achieve this goal.

[0054] In this embodiment, the quantitative indicators are specific and measurable standards for the main objectives, usually including: coating thickness, measured in microns (μm), adhesion, measured in Newtons (N), measured by pull-off test, uniformity, quantified by standard deviation or coefficient of variation, corrosion resistance: quantified by the duration of salt spray test or corrosion rate (such as mm / year), for example, real-time monitoring and recording of coating thickness to ensure that the set 20μm is reached.

[0055] In this embodiment, the weighted rank correlation coefficient between the quantitative indicators and the electroplating process parameters is calculated to evaluate their correlation, the weight and rank of each process parameter are calculated, and then the weighted rank correlation coefficient is obtained using the normalization factor, the test range is set, and the correlation coefficient is rank tested to determine the correlation between the main objectives and the process parameters.

[0056] In this embodiment, the correlation refers to the relationship between the quantitative index and the electroplating process parameters, which generally include positive correlation, when one variable increases, the other variable also increases (such as current density and coating thickness), negative correlation, when one variable increases, the other variable decreases (such as temperature and adhesion), and no correlation, there is no obvious relationship between the two variables. For example, it is found that the current density is positively correlated with the coating thickness. As the current density increases, the coating thickness also increases.

[0057] In this embodiment, the electroplating process parameters refer to the key factors controlled and adjusted during the electroplating process. These parameters directly affect the quality, performance and characteristics of the coating. Common electroplating process parameters include current density, electroplating time, temperature, pH value, etc.

[0058] In this embodiment, the intelligent algorithm is an algorithm that simulates natural selection or group behavior and is used to solve complex optimization problems, such as genetic algorithm, particle swarm optimization, ant colony algorithm, etc.

[0059] In this embodiment, the constraints are set during the optimization process to ensure that the optimization results are within a reasonable range. For example, the plating solution concentration must be between 1g / L and 10g / L, and the current density cannot exceed 3A / dm 2 .

[0060] In this embodiment, the optimization result is the optimal combination of electroplating process parameters determined by the optimization individuals, for example, current density, the optimal value determined after optimization, electroplating time, the recommended time after optimization, and temperature, the optimal temperature setting after optimization.

[0061] In this embodiment, the simulation model is a mathematical model constructed based on the optimization results and constraints, and is used to simulate the electroplating process and predict the results. For example, a model is constructed to predict the thickness and uniformity of the coating at different current densities to help verify the optimization effect.

[0062] In this embodiment, the user registration information is the user's basic information, including name, position, usage permissions, operation history, etc. For example, the registration information of a process engineer includes the electroplating line he is responsible for, operation experience and previous operation records.

[0063] In this embodiment, the front-end interface is the interface seen by the end user, and is designed in combination with the functional division and the first basic interface, for example, a complete operation interface including parameter input, simulation start button and result display area.

[0064] In this embodiment, the backend interface is a background management and data processing interface that supports the implementation of frontend functions, such as a background interface for processing simulation logic, data storage, and result calculation.

[0065] In this embodiment, function addition refers to the functions of the front-end interface such as user input interface, target selection, indicator setting, optimization parameter adjustment, and result display.

[0066] In this embodiment, Figure 1 The Q in it represents an electroplating process parameter optimization control simulation system based on intelligent algorithm.

[0067] In this embodiment, if Figure 2 As shown in the figure, T represents the electroplating process parameter optimization control simulation system. The construction process of the simulation system is to collect user needs and industry standards, clarify the functional goals and performance requirements of the system, determine the system architecture, including the design of the front-end and back-end, select the appropriate technology stack, and divide the system into different modules according to the functions to be implemented, such as user management, simulation operation, result analysis, etc. Develop a user interface to ensure friendly interaction, support parameter input, simulation startup and result display, implement simulation models and algorithms, process user input data, perform simulation calculations and generate results, ensure smooth data interaction between the front and back ends, design API interfaces to support data transmission, and conduct comprehensive tests on the system, including functional testing, performance testing and user experience testing, to ensure system stability and accuracy.

[0068] The working principle and beneficial effects of the above technical solution are: by identifying multiple optimization targets and setting quantitative indicators, establishing the relationship between the targets and the electroplating process parameters, using intelligent algorithms to optimize the process parameters based on constraints, generating optimization results, and building a simulation model, developing user-friendly front-end and back-end interfaces, integrating simulation models, facilitating user operation and result viewing, achieving efficient and stable electroplating process optimization, and improving the overall performance of the electroplating process.

[0069] Embodiment 2:

[0070] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and a relationship module, including:

[0071] Quantification unit: determine the main objectives of the electroplating process, and quantify the main objectives according to user needs to obtain quantitative indicators;

[0072] Relationship analysis unit: collects historical data related to the electroplating process, performs correlation analysis on the quantitative indicators and the electroplating process parameters based on the historical data, and determines the correlation between each main target and the electroplating process parameters according to the correlation analysis results.

[0073] In this embodiment, historical data refers to relevant data collected in the previous electroplating process, including process parameters: such as current density, temperature, stirring rate, plating solution composition, etc., product performance: such as coating thickness, adhesion, uniformity, corrosion resistance test results, environmental conditions: such as humidity, air pressure, etc., which affect the electroplating process. For example, the system collects the current density (such as 5-15A / dm 2 ), temperature (such as 20-30°C), stirring rate (such as 100-200rpm) and other data.

[0074] The working principle and beneficial effects of the above technical solution are: by clarifying the main objectives of the electroplating process, formulating quantitative indicators according to user needs, collecting historical data, conducting correlation analysis on the quantitative indicators and electroplating process parameters, and determining the relationship between the two, data support and theoretical basis are provided for subsequent optimization, ensuring the scientificity and effectiveness of the optimization solution.

[0075] Embodiment 3:

[0076] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and a quantization unit, comprising:

[0077] Extract features of user needs and obtain a demand feature set based on the extracted features;

[0078] Based on the demand feature set and in combination with a preset feature-target-indicator comparison table, a quantitative indicator that is compatible with the main objective is selected from the indicator database.

[0079] In this embodiment, feature extraction refers to identifying and refining key attributes and elements from user needs, including performance requirements, such as coating thickness, corrosion resistance, adhesion, etc., operational requirements, such as ease of operation, real-time monitoring, data analysis capabilities, etc., environmental factors, such as the impact of temperature, humidity, chemical composition, etc. on the electroplating process, and economic indicators: such as cost control, efficiency improvement, etc.

[0080] In this embodiment, the required feature set is a set formed by sorting and summarizing the extracted features, including a list of all key features. This set provides a basis for subsequent analysis and decision-making, and usually includes various performance indicators, user-expected functional characteristics, related operating environments and constraints.

[0081] In this embodiment, the preset feature-goal-indicator comparison table is a systematic framework for associating user demand features with specific business goals and quantitative indicators. The table usually contains features, key features extracted from user needs, goals, business goals related to the features, such as improving corrosion resistance, reducing production costs, etc., and indicators, specific indicators for quantifying the goals, such as test results, production efficiency, etc.

[0082] In this embodiment, the indicator database is a systematic information repository specifically used to store, manage and retrieve data related to electroplating process parameters and their performance indicators.

[0083] The working principle and beneficial effects of the above technical solution are: feature extraction of user needs to form a demand feature set, and combined with a preset feature-target-indicator comparison table, quantitative indicators matching the main goals are screened out from the indicator database, ensuring the consistency between needs and goals and optimizing the parameter setting of the electroplating process.

[0084] Embodiment 4:

[0085] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and a relationship analysis unit, comprising:

[0086] Rank correlation coefficient calculation subunit: calculate the rank correlation coefficient between the quantitative index and the electroplating process parameters:

[0087] Among them, r s,w represents the weighted rank correlation coefficient between the quantitative index and the electroplating process parameters; w i represents the weight of the i-th electroplating process parameter; n represents the number of electroplating process parameters, where the number of electroplating process parameters corresponds to the quantitative index one by one; represents the rank of the i-th electroplating process parameter; represents the rank of the quantitative index corresponding to the i-th electroplating process parameter; n(n 2 -1) represents the standardization factor of the weighted rank correlation coefficient;

[0088] Inspection subunit: set the inspection range, perform rank inspection on the electroplating process parameters and quantitative indicators corresponding to the weighted rank correlation coefficients within the inspection range, obtain the correlation analysis results based on the rank test results, and then determine the correlation between each main target and the electroplating process parameters.

[0089] In this embodiment, the test range refers to the threshold range of the correlation coefficient selected when performing the rank correlation coefficient test. Usually, this range is set based on the following factors: significance level. The commonly used significance level is 0.05 or 0.01, which means that at a confidence level of 95% or 99%, the correlation is judged to be significant. The limit of the correlation coefficient is set according to actual needs. For example, a correlation coefficient greater than 0.7 can be regarded as a strong correlation, 0.3 to 0.7 is a medium correlation, and less than 0.3 is a weak correlation. Industry standards: refer to the standards or literature within the industry to determine the appropriate test range.

[0090] In this embodiment, rank refers to the position or serial number corresponding to each data point in a set of data after sorting them in a certain order (usually from small to large or from large to small). Suppose there is a set of measured values ​​of electroplating process parameters: [3,1,4,3,5]. Sorting: sorting from small to large gives [1,3,3,4,5], and assigning ranks: 1 has a rank of 1, 3 has a rank of 2 and 3, 2.5 is taken, 4 has a rank of 4, and 5 has a rank of 5, and the final rank is [2.5,1,4,2.5,5].

[0091] In this embodiment, the rank test refers to comparing the ranks between the quantitative indicators and the electroplating process parameters to determine their correlation. Commonly used rank test methods include the Wilcoxon rank sum test: used to compare the ranks of two groups of independent samples, the Kruskal-Wallis test: used to compare the ranks of three or more independent samples, and the Spearman rank correlation coefficient test: used to evaluate the monotonic relationship between two variables and calculate the correlation between the ranks.

[0092] In this embodiment, the rank test results generally include the following: p-value, which indicates the significance level of the test. If the p-value is less than the set significance level (such as 0.05), the correlation can be considered significant. The rank correlation coefficient, which is the correlation coefficient obtained by rank calculation, reflects the strength of the relationship between the quantitative index and the electroplating process parameters. Conclusion, based on the p-value and the rank correlation coefficient, the correlation between each main target and the electroplating process parameters is judged (such as significant correlation, no significant correlation, etc.).

[0093] The working principle and beneficial effects of the above technical solution are: by calculating the weighted rank correlation coefficient between the quantitative indicators and the electroplating process parameters, evaluating their correlation, calculating the weight and rank of each process parameter, and then using the normalization factor to obtain the weighted rank correlation coefficient, setting the test range, and performing a rank test on the correlation coefficient to determine the correlation between the main objectives and the process parameters, a more scientific basis for parameter optimization is provided, which significantly improves the optimization efficiency and the stability of the results.

[0094] Embodiment 5:

[0095] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, the inspection subunit comprising:

[0096] First range block: obtaining changes in the external environment and setting a first dynamic range according to the changes;

[0097] Second range block: obtaining an application field corresponding to the electroplating process, and setting a second dynamic range according to the application field;

[0098] Comprehensive range block: sets the inspection range based on the first dynamic range and the second dynamic range.

[0099] In this embodiment, changes in the external environment include factors such as temperature, humidity, pressure, power supply fluctuations, and gas composition. These factors may affect the stability of the electroplating process and the performance of the coating. For example, temperature changes may affect the viscosity and current density of the plating solution, thereby affecting the uniformity and adhesion of the coating.

[0100] In this embodiment, the first dynamic range is a parameter range set according to changes in the external environment, aiming to ensure that the electroplating process can remain stable under different environmental conditions, for example, the set temperature range is 20°C to 30°C, and the humidity range is 40% to 60%.

[0101] In this embodiment, the second dynamic range is a parameter range set according to the application field corresponding to the electroplating process, reflecting the specific requirements of different industries for coating performance. For example, the electronics industry may require a thinner coating thickness (1-5 microns), while the automotive industry may require a thicker coating (10-20 microns).

[0102] In this embodiment, the application fields include industries served by the electroplating process, such as electronics, automobiles, aviation, medical care, etc. These fields have different requirements on the performance of the coating, such as corrosion resistance, conductivity, appearance, etc.

[0103] In this embodiment, the process of setting the inspection range includes: collecting external environmental change data and application field requirements, analyzing these data, determining a first dynamic range and a second dynamic range, combining these two ranges, and setting a final inspection range to ensure the adaptability and stability of the electroplating process. For example, in the first dynamic range, the electroplating temperature is set to 25°C ± 2°C to adapt to environmental fluctuations. In the second dynamic range, for electronic products, the coating thickness is set to between 3 microns and 5 microns. Inspection range: taking into account the external environment and application fields, the coating thickness is finally set to 3 microns to 5 microns, and the temperature is set to 23°C to 27°C to ensure process stability under different conditions.

[0104] The working principle and beneficial effects of the above technical solution are: by obtaining external environmental changes and the application fields of the electroplating process, the dynamic range is set respectively to ensure that the inspection range of the electroplating process parameters is optimized under changing environments and industry requirements, thereby improving the accuracy and adaptability of the optimization and ensuring the stability and effectiveness of the electroplating process.

[0105] Embodiment 6:

[0106] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and the model module includes:

[0107] Parameter unit: performing characteristic analysis on the correlation, selecting an intelligent algorithm from a characteristic-algorithm table according to the characteristic analysis result, and determining the electroplating process parameters to be optimized according to the characteristic analysis result;

[0108] Combination determination unit: Generates first-level individuals of electroplating process parameters based on intelligent algorithms, calculates the fitness value of each first-level individual, sorts the first-level individuals according to the fitness value, and groups the first-level individuals with fitness values ​​higher than the first preset fitness values ​​into groups of six as candidate parameter combinations;

[0109] Optimization unit: using a roulette wheel to select a secondary individual from the candidate parameter combination, cross-linking the secondary individuals to generate new secondary individuals, mutating the new secondary individuals, and iterating. If there is a fitness value corresponding to a secondary individual higher than a second preset fitness value, the iteration is terminated, and the corresponding secondary individual is set as an optimized individual. According to the optimized individual, the corresponding electroplating process parameters are optimized to obtain an optimization result;

[0110] Model building unit: setting constraints for each main objective based on the correlation, and building a simulation model in combination with the constraints and optimization results.

[0111] In this embodiment, the characteristic analysis is to analyze the relationship between the electroplating process parameters and the performance to determine which parameters have the greatest impact on the final result, for example, analyzing the impact of the plating solution concentration, temperature and current density on the coating thickness and adhesion.

[0112] In this embodiment, the characteristic-algorithm table is a table that lists suitable intelligent algorithms (such as genetic algorithm, particle swarm optimization) corresponding to different characteristics (such as parameter type, optimization target). For example, the table may show that "thickness optimization" corresponds to "genetic algorithm", and "uniformity optimization" corresponds to "particle swarm optimization".

[0113] In this embodiment, the characteristic analysis result is the output of the characteristic analysis, indicating which electroplating process parameters are most important and the relationship between them. For example, the characteristic analysis result shows that temperature has a significant effect on coating uniformity, while current density has a smaller effect on adhesion.

[0114] In this embodiment, the fitness value is a numerical value used to evaluate the performance of an individual in optimization, usually the value of an objective function. For example, if the goal is to maximize the coating thickness, the fitness value may be the actual thickness of the coating.

[0115] In this embodiment, the candidate parameter combination is a parameter combination that meets certain fitness standards after preliminary screening and is ready for further optimization. For example, the combination includes a plating solution concentration of 5g / L, a temperature of 25°C, and a current density of 2A / dm 2 Parameter group.

[0116] In this embodiment, the first preset fitness is a fitness threshold set during the initial individual screening process, and individuals below this value will be excluded. For example, the first preset fitness is set to 50, and only individuals with fitness values ​​exceeding 50 can enter the candidate parameter combination.

[0117] In this embodiment, the second preset fitness is a higher fitness threshold set during the optimization process, which is used to determine whether to end the iteration. For example, the second preset fitness is set to 80, and when a secondary individual with a fitness value higher than 80 appears, the iteration is stopped.

[0118] In this embodiment, the secondary individuals are individuals selected from the candidate parameter combinations during the optimization process for further crossover and variation. For example, the plating solution concentration of 5 g / L and the current density of 2 A / dm 2 as the secondary individuals.

[0119] In this embodiment, the first-level individual is a random combination of electroplating process parameters generated based on an intelligent algorithm as the starting point for optimization. For example, the first-level individual may be a plating solution concentration of 3g / L, a temperature of 30°C, and a current density of 1.5A / dm 2 .

[0120] In this embodiment, the optimized individual is the individual whose fitness value is higher than the second preset fitness value after iterative optimization. The optimal parameter combination is finally determined. For example, the optimized individual may be a plating solution concentration of 6g / L, a temperature of 28°C, and a current density of 2.5A / dm 2 .

[0121] The working principle and beneficial effects of the above technical solution are: select a suitable intelligent algorithm through characteristic analysis, determine the electroplating process parameters to be optimized, generate a first-level individual and calculate the fitness value, screen out parameter combinations with high fitness, and use roulette wheel selection, crossover and mutation operations to perform iterative optimization until an individual with a fitness value higher than a preset threshold is found. Finally, a simulation model is constructed to achieve comprehensive optimization of the electroplating process, comprehensively consider multiple performance requirements, meet the needs of different industries, improve optimization efficiency, and shorten the development cycle.

[0122] Embodiment 7:

[0123] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and a model building unit, including:

[0124] Constraint condition subunit: obtain industry standards, user requirements and production capacity, formulate initial conditions for each major target according to industry standards, user requirements and production capacity, and adjust the initial conditions based on the historical data to obtain constraint conditions;

[0125] Construct subunits: Prioritize the optimization results corresponding to each main goal according to user requirements and production capacity, and combine the priority division results with the constraints to construct the simulation model.

[0126] In this embodiment, the industry standard is the technical specifications, quality standards and safety requirements generally recognized in the electroplating industry, such as the ISO 9001 quality management system standard or the electroplating thickness standard for a specific industry (for example, the electronics industry requires the coating thickness to be between 10-20 microns).

[0127] In this embodiment, user requirements are specific demands of customers for the performance of electroplated products, including the thickness, adhesion, uniformity and corrosion resistance of the coating. For example, customers require that the coating must have at least 90% adhesion and excellent corrosion resistance to meet the use requirements in a specific environment.

[0128] In this embodiment, production capacity refers to the production level that production equipment and processes can achieve within a certain period of time, including output, efficiency and available resources. For example, the electroplating line of a factory can process 500 workpieces per hour, and the coating thickness ranges from 5 to 25 microns.

[0129] In this embodiment, the initial conditions are the initial values ​​of the electroplating process parameters set during the optimization process, such as current density, temperature and plating solution concentration. For example, the initial conditions are set to a current density of 2A / dm 2 , temperature 25°C and plating solution concentration 5g / L.

[0130] In this embodiment, the process of adjusting the initial conditions to obtain the limiting conditions is to analyze the impact of the initial conditions on product performance based on historical data and actual production conditions, determine the reasonable range of parameters, and form limiting conditions. For example, through historical data analysis, it is found that the current density exceeds 3A / dm 2 This will result in a decrease in the uniformity of the coating, so the current density is limited to 1-3A / dm 2 .

[0131] In this embodiment, the priority division process is to evaluate the main goals according to customer requirements and production capacity, determine which goals are most important, and assign different priorities. For example, if the customer's requirement for corrosion resistance is higher than that of coating thickness, corrosion resistance is given a higher priority (such as priority 1), while coating thickness is given priority 2.

[0132] In this embodiment, the process of constructing a simulation model is to combine the constraints with the priority division results, and construct a simulation model of the electroplating process based on a mathematical model or computer simulation software to predict the performance under different parameter combinations. For example, a model is constructed using MATLAB or Simulink, and parameters such as current density and temperature are input to simulate the thickness and adhesion of the coating under different conditions to verify the optimization effect.

[0133] The working principle and beneficial effects of the above technical solution are: by obtaining industry standards, customer needs and production capacity, formulating initial conditions and adjusting them to constraints based on historical data, then combining customer needs with production capacity, prioritizing the optimization results of the main objectives, and finally building a simulation model to achieve multi-objective optimization and effective electroplating process control.

[0134] Embodiment 8:

[0135] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and the system module includes:

[0136] User unit: obtain the user record information and corresponding usage scenarios of the main users of the system, and determine the operation process and interaction method of the main users in the system according to the user record information and usage scenarios of the main users;

[0137] Division unit: Determine the functions to be implemented by the system based on the main objectives, and divide the system into functions according to the functions to be implemented;

[0138] Front-end unit: determining a first basic interface based on the operation process and the interaction mode, and determining a front-end interface according to the functional division result and the first basic interface;

[0139] Back-end unit: determine the second basic interface based on the simulation model, and determine the back-end interface according to the functional division result and the second basic interface;

[0140] System construction unit: constructs a simulation system based on the front-end interface and the back-end interface, and outputs a specific report on the optimization control of electroplating process parameters according to the simulation system.

[0141] In this embodiment, the primary user is the main user of the system, usually an operator, engineer or manager of the electroplating process, such as a process engineer or quality control personnel of an electroplating plant.

[0142] In this embodiment, the usage scenario is a specific situation in which a user uses the system in a specific environment, including the purpose and needs of the operation. For example, the user needs to monitor the electroplating parameters in real time and adjust them during the production process to ensure the quality of the coating.

[0143] In this embodiment, the operation process is the steps and sequence for the user to perform tasks in the system, for example, the user logs in to the system → selects the electroplating process → inputs the initial parameters → runs the simulation → views the optimization results → generates a report.

[0144] In this embodiment, the interaction mode is the form of interaction between the user and the system, including input, output and feedback mechanisms. For example, the user inputs parameters through a graphical interface, and the system displays the results through charts and reports.

[0145] In this embodiment, the functions to be implemented are functional modules that the system needs to implement to meet user needs, such as parameter input, simulation operation, result display, report generation and other functions.

[0146] In this embodiment, the function division process is to classify and layer the system function modules according to the functions to be implemented to facilitate development and management. For example, the functions are divided into a user management module, a simulation module, a result analysis module, etc.

[0147] In this embodiment, the first basic interface is a preliminary interface design of the system, including basic operating elements and layout, such as a user login interface including user name and password input boxes.

[0148] In this embodiment, the second basic interface is a preliminary interface design of the backend system, which is usually used for data processing and management, such as a database management interface that displays data input and processing status.

[0149] In this embodiment, the specific report includes simulation parameters, recording the electroplating process parameters input by the user, such as current density, temperature, time, etc., simulation results, displaying indicators such as coating thickness, adhesion, uniformity and corrosion resistance calculated by simulation, optimization suggestions, based on the simulation results, providing optimization suggestions for process parameters, helping users adjust production conditions, comparative analysis, comparing the simulation results with industry standards or historical data, and evaluating the improvement effect of the process.

[0150] The working principle and beneficial effects of the above technical solution are: by obtaining user registration information and usage scenarios, the user's operation process and interaction method in the system are determined. Based on the main goals, the system functions are divided and the front-end and back-end interfaces are designed. Finally, a simulation system is built. The system outputs a specific report on the optimization control of electroplating process parameters to achieve efficient and intuitive operation and result display.

[0151] Embodiment 9:

[0152] The embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, the user unit comprising:

[0153] Scenario determination subunit: obtaining user record information of main users of the system, classifying the main users into types according to the user record information, and defining usage scenarios based on the type classification results;

[0154] Operation subunit: Draw a user operation flow chart using a column diagram tool based on the usage scenario, and define the operation process and corresponding interaction method for each type of classification result.

[0155] In this embodiment, the type classification includes role classification, usage requirement classification, and permission setting classification. For example, different types of users may require different functions and data displays in the system. Process engineers may require real-time parameter adjustment functions, while quality control personnel require quality monitoring and report generation.

[0156] In this embodiment, the user operation flowchart is a visual tool used to depict the steps and decision points for a user to complete a specific task in the system.

[0157] The working principle and beneficial effects of the above technical solution are: by collecting the filing information of the main users, dividing them into types, clarifying the needs and operation scenarios of different users, and based on these scenarios, using flowchart tools to draw user operation processes and define corresponding interaction methods, thereby achieving clear display of system functions and efficient interaction, ensuring that users can complete tasks quickly and accurately when using the simulation system, and improving operational efficiency and user experience.

[0158] Embodiment 10:

[0159] An embodiment of the present invention provides an electroplating process parameter optimization control simulation system based on an intelligent algorithm, and user registration information includes: user basic information, user usage scenarios, user needs and expectations, user technical background, user roles and permissions.

[0160] In this embodiment, the user's basic information includes user name, contact information (telephone, email, etc.), and organizational structure (such as company name, department, etc.); usage scenarios include the user's application field (such as electronics, automobiles, aviation, etc.), specific usage environment (such as laboratories, production lines, R&D centers, etc.), user operation frequency and usage time; user needs and expectations include specific requirements for the electroplating process (such as coating thickness, corrosion resistance, etc.), expectations for system functions (such as data analysis, report generation, etc.), user pain points and improvement suggestions; technical background includes the user's technical capabilities and experience level (such as professional background, operational proficiency), existing electroplating processes and equipment conditions; user roles and permissions include the user's role in the system (such as administrator, operator, auditor), and the operating permissions and access rights of different roles.

[0161] The working principle and beneficial effects of the above technical solution are: by collecting user registration information, extracting key features and analyzing needs, matching suitable quantitative indicators from the indicator database, using intelligent algorithms for multi-objective optimization, generating optimal electroplating process parameters, and verifying the optimization effect through simulation, providing visual feedback, and supporting continuous improvement.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An electroplating process parameter optimization control simulation system based on intelligent algorithm, characterized in that: include: Relationship module: determine multiple main targets to be optimized, set quantitative indicators for each main target, and determine the correlation between the main target and the electroplating process parameters based on the quantitative indicators; Model module: Select an intelligent algorithm based on the correlation, optimize the electroplating process parameters according to the intelligent algorithm and the main objectives, obtain the optimization results, and determine the constraints of each main objective, and then build a simulation model according to the optimization results and the constraints; System module: Develop front-end and back-end interfaces based on the simulation model, add functions to the user filing information according to the front-end interface, integrate the simulation model into the back-end interface to form a simulation system.

2. According to the electroplating process parameter optimization control simulation system based on intelligent algorithm in claim 1, it is characterized in that: Relationship modules, including: Quantification unit: determine the main objectives of the electroplating process, and quantify the main objectives according to user needs to obtain quantitative indicators; Relationship analysis unit: collects historical data related to the electroplating process, performs correlation analysis on the quantitative indicators and the electroplating process parameters based on the historical data, and determines the correlation between each main target and the electroplating process parameters according to the correlation analysis results.

3. The electroplating process parameter optimization control simulation system based on intelligent algorithm according to claim 2 is characterized in that: Quantitative unit, including: Extract features of user needs and obtain a demand feature set based on the extracted features; Based on the demand feature set and in combination with a preset feature-target-indicator comparison table, a quantitative indicator that is compatible with the main objective is selected from the indicator database.

4. The electroplating process parameter optimization control simulation system based on intelligent algorithm according to claim 2 is characterized in that: Relationship Analysis Unit, including: Rank correlation coefficient calculation subunit: calculate the rank correlation coefficient between the quantitative index and the electroplating process parameters: Among them, r s,w represents the weighted rank correlation coefficient between the quantitative index and the electroplating process parameters; w i represents the weight of the i-th electroplating process parameter; n represents the number of electroplating process parameters, where the number of electroplating process parameters corresponds to the quantitative index one by one; represents the rank of the i-th electroplating process parameter; represents the rank of the quantitative index corresponding to the i-th electroplating process parameter; n(n 2 -1) represents the standardization factor of the weighted rank correlation coefficient; Inspection subunit: set the inspection range, perform rank inspection on the electroplating process parameters and quantitative indicators corresponding to the weighted rank correlation coefficients within the inspection range, obtain the correlation analysis results based on the rank test results, and then determine the correlation between each main target and the electroplating process parameters.

5. The electroplating process parameter optimization control simulation system based on intelligent algorithm according to claim 4 is characterized in that: Inspection subunit, including: First range block: obtaining changes in the external environment and setting a first dynamic range according to the changes; Second range block: obtaining an application field corresponding to the electroplating process, and setting a second dynamic range according to the application field; Comprehensive range block: sets the inspection range based on the first dynamic range and the second dynamic range.

6. The electroplating process parameter optimization control simulation system based on intelligent algorithm according to claim 2 is characterized in that: Model modules, including: Parameter unit: performing characteristic analysis on the correlation, selecting an intelligent algorithm from a characteristic-algorithm table according to the characteristic analysis result, and determining the electroplating process parameters to be optimized according to the characteristic analysis result; Combination determination unit: Generates first-level individuals of electroplating process parameters based on intelligent algorithms, calculates the fitness value of each first-level individual, sorts the first-level individuals according to the fitness value, and groups the first-level individuals with fitness values ​​higher than the first preset fitness values ​​into groups of six as candidate parameter combinations; Optimization unit: using a roulette wheel to select a secondary individual from the candidate parameter combination, cross-generating a new secondary individual through crossover of the secondary individuals, mutating the new secondary individuals, and iterating; if there is a secondary individual corresponding to a fitness value higher than a second preset fitness value, the iteration is terminated, and the corresponding secondary individual is set as an optimized individual; the corresponding electroplating process parameters are optimized according to the optimized individual, and an optimization result is obtained; Model building unit: setting constraints for each main objective based on the correlation, and building a simulation model in combination with the constraints and optimization results.

7. The electroplating process parameter optimization control simulation system based on intelligent algorithm according to claim 6 is characterized in that: Model building unit, including: Constraint condition subunit: obtain industry standards, user requirements and production capacity, formulate initial conditions for each major target according to industry standards, user requirements and production capacity, and adjust the initial conditions based on the historical data to obtain constraint conditions; Construct subunits: Prioritize the optimization results corresponding to each main goal according to user requirements and production capacity, and combine the priority division results with the constraints to construct the simulation model.

8. The electroplating process parameter optimization control simulation system based on intelligent algorithm according to claim 1 is characterized in that: System modules, including: User unit: obtain the user record information and corresponding usage scenarios of the main users of the system, and determine the operation process and interaction method of the main users in the system according to the user record information and usage scenarios of the main users; Division unit: Determine the functions to be implemented by the system based on the main objectives, and divide the system into functions according to the functions to be implemented; Front-end unit: determining a first basic interface based on the operation process and the interaction mode, and determining a front-end interface according to the functional division result and the first basic interface; Back-end unit: determine the second basic interface based on the simulation model, and determine the back-end interface according to the functional division result and the second basic interface; System construction unit: constructs a simulation system based on the front-end interface and the back-end interface, and outputs a specific report on the optimization control of electroplating process parameters according to the simulation system.

9. The electroplating process parameter optimization control simulation system based on intelligent algorithm according to claim 8, characterized in that: User unit, including: Scenario determination subunit: obtaining user record information of main users of the system, classifying the main users into types according to the user record information, and defining usage scenarios based on the type classification results; Operation subunit: Draw a user operation flow chart using a column diagram tool based on the usage scenario, and define the operation process and corresponding interaction method for each type of classification result.

10. The electroplating process parameter optimization control simulation system based on intelligent algorithm according to claim 8, characterized in that: User filing information includes: user basic information, user usage scenarios, user needs and expectations, user technical background, user roles and permissions.

Images