Metal manufacturing optimization method and system based on additive manufacturing process

Abstract

The invention discloses a metal manufacturing optimization method and system based on an additive manufacturing process. The metal manufacturing optimization method based on the additive manufacturing process comprises the following steps that the metal manufacturing optimization method is obtained; manufacturing optimization parameters corresponding to the metal manufacturing optimization method are obtained; obtaining an optimization method reliability degree evaluation coefficient; a reliable optimization method is obtained; obtaining a reliable optimization parameter corresponding to the reliable optimization method; obtaining a reliable optimization coefficient; obtaining a good degree evaluation index of the optimization method according to the reliable optimization coefficient; and the optimal metal manufacturing optimization method is obtained through analysis according to the optimization method good degree evaluation index. The optimal metal manufacturing optimization method is obtained through analysis according to the optimization method good degree evaluation index, the effect of accurately and efficiently finding out the metal manufacturing optimization method with the best optimization effect is achieved, and the problem that in the prior art, it is difficult to accurately and efficiently find out the metal manufacturing optimization method with the best optimization effect is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of metal manufacturing, and in particular to a metal manufacturing optimization method and system based on an additive manufacturing process. Background Art

[0002] Metal manufacturing is a process technology that processes metal materials into objects, parts, and components. Traditional metal manufacturing processes, such as casting, forging, and cutting, usually involve multiple steps and require multiple mechanical operations, with problems such as long manufacturing cycles, high costs, and high complexity. The additive manufacturing process is a digital process that starts from an almost model-free state, directly melts and deposits metal materials onto a substrate, and builds the desired shape layer by layer. With the development of additive manufacturing technology, especially the maturity of processes such as laser melting and electron beam melting, new approaches have been provided for metal manufacturing. The additive manufacturing process can directly manufacture metal parts from digital models, with the advantages of short manufacturing cycles and high structural complexity. However, the existing additive manufacturing process still has certain limitations in metal manufacturing, such as manufacturing quality and production efficiency, so metal manufacturing needs to be optimized.

[0003] The existing metal manufacturing optimization system integrates technologies such as the Internet of Things, big data analysis, and cloud computing to achieve intelligent monitoring of the production process, introduce automated equipment and robotics technology, reduce manual operations, improve processing accuracy and stability, and reduce labor costs to achieve optimization of the metal manufacturing process.

[0004] For example, a patent application with publication number: CN116227084A discloses a support structure optimization design method for controlling thermal stress in metal additive manufacturing, including: defining design domain and non-design domain; initializing design variables; converting design variables into physical variables through density filtering and projection; establishing an interpolation model of physical variables and overall structural stiffness; performing finite element simulation on the additive manufacturing layer-by-layer processing process of the overall structure to obtain the thermal deformation of the overall structure; calculating the maximum thermal stress of the structure, the volume fraction of the support structure, and the suspension angle constraint response; calculating the sensitivity; updating the design variables to solve the optimization model; judging whether it converges; and post-processing the optimization results.

[0005] For example, the invention patent with announcement number: CN115906339B announces a method for manufacturing metal nodes of building structures based on additive manufacturing technology, including: 1. Establishing a computational analysis model of metal nodes; 2. Using finite element numerical simulation calculation method to perform non-differentiated computational analysis on the computational analysis model and the metal node entity to obtain the analysis result of the metal node model; 3. According to the analysis result of the metal node model, setting the optimization target of the metal node, and performing intelligent optimization to obtain the optimal stress cloud map; 4. Adjusting the thickness and shape of each part of the metal node model according to the optimal stress cloud map to obtain an optimized metal node model; 5. Transmitting the optimized metal node model to the additive manufacturing equipment in the form of a command stream, and the additive manufacturing equipment prepares the required metal nodes according to the command stream.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the prior art, there are many types of metal manufacturing methods based on additive manufacturing technology, and the metal manufacturing methods can be optimized in many aspects, but the optimization effect needs to be analyzed from multiple aspects. The analysis process is complicated and time-consuming, and there is a problem that it is difficult to accurately and efficiently find the metal manufacturing optimization method with the best optimization effect. Summary of the invention

[0007] The embodiments of the present application solve the problem in the prior art that it is difficult to accurately and efficiently find the metal manufacturing optimization method with the best optimization effect by providing a metal manufacturing optimization method and system based on additive manufacturing technology, thereby achieving accurate and efficient finding of the metal manufacturing optimization method with the best optimization effect.

[0008] An embodiment of the present application provides a metal manufacturing optimization method based on an additive manufacturing process, comprising the following steps: obtaining a current metal manufacturing method, optimizing the current metal manufacturing method to obtain a metal manufacturing optimization method; obtaining manufacturing optimization parameters corresponding to the metal manufacturing optimization method; obtaining a reliability evaluation coefficient of the optimization method based on the manufacturing optimization parameters; analyzing the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method; obtaining reliable optimization parameters corresponding to the reliable optimization method; obtaining a reliable optimization coefficient based on the reliable optimization parameters; obtaining an optimization method goodness evaluation index based on the reliable optimization coefficient; and obtaining the best metal manufacturing optimization method based on the optimization method goodness evaluation index.

[0009] Furthermore, the specific analysis process of obtaining the metal manufacturing optimization method is: obtaining the current metal manufacturing method; optimizing the current metal manufacturing method, and the optimization of the current metal manufacturing method specifically includes: improving process parameters, optimizing interlayer bonding process, using special metal materials matching the additive manufacturing process, and improving heat treatment process; collecting all methods obtained after optimizing the current metal manufacturing method, and using all methods as metal manufacturing optimization methods.

[0010] Furthermore, the specific method for obtaining the reliability evaluation coefficient of the optimization method is: obtaining all metal manufacturing optimization methods and numbering the metal manufacturing optimization methods; obtaining manufacturing optimization parameters, the manufacturing optimization parameters including: energy consumption rate, metal manufacturing accuracy and metal manufacturing efficiency; constructing a calculation formula for the reliability evaluation coefficient of the optimization method; the specific calculation formula for the reliability evaluation coefficient of the optimization method is:

[0011] In the formula, Expressed as The reliability evaluation coefficient of the optimization method for metal manufacturing optimization method, , Expressed as the total number of metal manufacturing optimization methods, Expressed as Energy consumption rate of a metal manufacturing optimization method, Expressed as Metal manufacturing accuracy of a metal manufacturing optimization method, Expressed as Metal manufacturing efficiency of a metal manufacturing optimization method, It is expressed as the weight ratio of energy consumption rate in the reliability evaluation coefficient of the optimization method. It is expressed as the weight ratio of metal manufacturing accuracy in the reliability evaluation coefficient of the optimization method. It is expressed as the weight ratio of metal manufacturing efficiency in the reliability evaluation coefficient of the optimization method. Expressed as a natural constant.

[0012] Furthermore, the specific analysis process of analyzing the reliability assessment coefficient of the optimization method to obtain a reliable optimization method is: obtaining the reliability assessment coefficient of the optimization method, arranging the reliability assessment coefficients of the optimization method in order from large to small, and taking the metal manufacturing optimization method corresponding to the reliability assessment coefficient of the top N optimization methods as a reliable optimization method, where N is represented by a positive integer.

[0013] Furthermore, the specific analysis process of obtaining the reliable optimization parameters corresponding to the reliable optimization method is: obtaining a reliable optimization method; using the reliable optimization method to manufacture metal, real-time monitoring of the process of using the reliable optimization method to manufacture metal, and acquiring data of the manufactured metal products, and performing data processing after the data is acquired to obtain reliable optimization parameters; the reliable optimization parameters include: dimensional accuracy parameters, mechanical property parameters and processing influencing parameters.

[0014] Furthermore, the specific process of obtaining the optimization method goodness evaluation index based on the reliable optimization coefficient is: obtaining reliable optimization parameters; analyzing the reliable optimization parameters to obtain the reliable optimization coefficient, specifically including: obtaining the metal product dimensional accuracy index based on the dimensional accuracy parameter analysis, obtaining the metal product mechanical performance index based on the mechanical performance parameter analysis, and obtaining the product processing influence index based on the processing influence parameter analysis; the reliable optimization coefficient includes: the metal product dimensional accuracy index, the metal product mechanical performance index and the product processing influence index; analyzing the reliable optimization coefficient to obtain the optimization method goodness evaluation index.

[0015] Furthermore, the specific analysis process of the metal product dimensional accuracy index is: extracting the metal product size, metal product weight and product part size from the dimensional accuracy parameters, comparing the metal product size with the metal product reference size, comparing the metal product weight with the metal product reference weight, and comparing the product part size with the product part reference size, and performing analysis and processing to obtain the metal product dimensional accuracy index.

[0016] Furthermore, the specific analysis process of the mechanical performance indicators of the metal products is: extracting the hardness of the metal products, the tensile strength of the metal products and the toughness of the metal products from the mechanical performance parameters, and analyzing the hardness of the metal products, the tensile strength of the metal products, the toughness of the metal products, the standard hardness of the metal products, the standard tensile strength of the metal products and the standard toughness of the metal products to obtain the mechanical performance indicators of the metal products.

[0017] Furthermore, the specific method for obtaining the goodness evaluation index of the optimization method is as follows: obtaining the number of reliable optimization methods and numbering the reliable optimization methods; obtaining the number of metal products produced according to the reliable optimization methods and numbering the metal products; constructing a calculation formula for the goodness evaluation index of the optimization method; the specific calculation formula for the goodness evaluation index of the optimization method is:

[0018] In the formula, Expressed as The optimization method goodness evaluation index of the reliable optimization method, , Expressed as the total number of reliable optimization methods, Expressed as A reliable optimization method to manufacture Metal product dimensional accuracy indicators for metal products, , Expressed as the total number of metal products, Expressed as A reliable optimization method to manufacture The mechanical performance indicators of metal products of each metal product, Expressed as A reliable optimization method to manufacture Product processing impact indicators of metal products, It is expressed as the weight ratio of the metal product dimensional accuracy index in the optimization method goodness evaluation index. It is expressed as the weight ratio of the metal product mechanical performance index in the optimization method goodness evaluation index. It is expressed as the weight ratio of product processing impact indicators in the optimization method goodness evaluation index.

[0019] Furthermore, the specific analysis process of obtaining the best metal manufacturing optimization method based on the optimization method goodness evaluation index is: obtaining the optimization method goodness evaluation index, arranging the optimization method goodness evaluation index in descending order, extracting the optimization method goodness evaluation index that ranks first, and using the reliable optimization method corresponding to the optimization method goodness evaluation index as the best metal manufacturing optimization method.

[0020] An embodiment of the present application provides a metal manufacturing optimization system based on an additive manufacturing process, including: a method optimization module, a parameter acquisition module, a coefficient acquisition module, a coefficient analysis module, a reliable parameter acquisition module, an optimization coefficient acquisition module, an optimization coefficient processing module and an optimization analysis module: the method optimization module is used to acquire the current metal manufacturing method and optimize the current metal manufacturing method to obtain the metal manufacturing optimization method; the parameter acquisition module is used to acquire the manufacturing optimization parameters corresponding to the metal manufacturing optimization method; the coefficient acquisition module is used to acquire the reliability evaluation coefficient of the optimization method according to the manufacturing optimization parameters; the coefficient analysis module is used to analyze the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method; the reliable parameter acquisition module is used to acquire the reliable optimization parameters corresponding to the reliable optimization method; the optimization coefficient acquisition module is used to acquire the reliable optimization coefficient according to the reliable optimization parameters; the optimization coefficient processing module is used to acquire the goodness evaluation index of the optimization method according to the reliable optimization coefficient; the optimization analysis module is used to acquire the best metal manufacturing optimization method according to the goodness evaluation index of the optimization method.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By obtaining the reliable optimization parameters corresponding to the reliable optimization method, the reliable optimization coefficient is obtained according to the reliable optimization parameters, and the optimization method goodness evaluation index is obtained according to the reliable optimization coefficient, so as to obtain the best metal manufacturing optimization method according to the optimization method goodness evaluation index analysis, thereby realizing the rapid identification of the most suitable metal manufacturing optimization method in a relatively short time, and effectively solving the problem in the prior art that it is difficult to accurately and efficiently find the metal manufacturing optimization method with the best optimization effect.

[0022] 2. By acquiring the current metal manufacturing method, optimizing the current metal manufacturing method to obtain a metal manufacturing optimization method, obtaining the manufacturing optimization parameters corresponding to the metal manufacturing optimization method, and obtaining the reliability evaluation coefficient of the optimization method according to the manufacturing optimization parameters, the reliability evaluation coefficient of the optimization method is analyzed to obtain a reliable optimization method, thereby realizing the rapid elimination of unsuitable optimization directions and avoiding spending too much time and energy on unsuitable optimization directions.

[0023] 3. By using the best metal manufacturing optimization method to optimize and adjust the machinery for metal manufacturing, the production efficiency and accuracy of the machinery are improved, thereby reducing the economic losses caused by low production efficiency and low accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a metal manufacturing optimization method based on an additive manufacturing process provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a metal manufacturing optimization system based on additive manufacturing process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The embodiments of the present application solve the problem in the prior art that it is difficult to accurately and efficiently find the metal manufacturing optimization method with the best optimization effect by providing a metal manufacturing optimization method and system based on additive manufacturing technology. By obtaining reliable optimization parameters corresponding to the reliable optimization method, a reliable optimization coefficient is obtained according to the reliable optimization parameters, and an optimization method goodness evaluation index is obtained according to the reliable optimization coefficient, then the best metal manufacturing optimization method is obtained according to the optimization method goodness evaluation index analysis, thereby achieving accurate and efficient finding of the metal manufacturing optimization method with the best optimization effect.

[0026] The technical solution in the embodiments of the present application is to solve the above-mentioned problem that it is difficult to accurately and efficiently find the metal manufacturing optimization method with the best optimization effect. The overall idea is as follows: obtain the current metal manufacturing method, optimize the current metal manufacturing method to obtain the metal manufacturing optimization method; obtain the manufacturing optimization parameters corresponding to the metal manufacturing optimization method; obtain the reliability evaluation coefficient of the optimization method according to the manufacturing optimization parameters; analyze the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method; obtain the reliable optimization parameters corresponding to the reliable optimization method, and the reliable optimization parameters include dimensional accuracy parameters, mechanical performance parameters and processing influencing parameters; obtain the reliable optimization coefficient according to the reliable optimization parameters, and the reliable optimization coefficient includes: metal product dimensional accuracy index, metal product mechanical performance index and product processing influencing index; obtain the optimization method goodness evaluation index according to the reliable optimization coefficient; obtain the best metal manufacturing optimization method according to the optimization method goodness evaluation index, so as to achieve the effect of accurately and efficiently finding the metal manufacturing optimization method with the best optimization effect.

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1 As shown, it is a flow chart of a metal manufacturing optimization method based on additive manufacturing process provided in an embodiment of the present application. The method is applied to a metal manufacturing optimization system based on additive manufacturing process. The method includes the following steps: obtaining the current metal manufacturing method, optimizing the current metal manufacturing method to obtain the metal manufacturing optimization method; obtaining manufacturing optimization parameters corresponding to the metal manufacturing optimization method; obtaining a reliability evaluation coefficient of the optimization method according to the manufacturing optimization parameters; analyzing the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method; obtaining reliable optimization parameters corresponding to the reliable optimization method; obtaining a reliable optimization coefficient according to the reliable optimization parameters; obtaining an optimization method goodness evaluation index according to the reliable optimization coefficient; and obtaining the best metal manufacturing optimization method according to the optimization method goodness evaluation index.

[0029] In this embodiment, the metal manufacturing optimization method based on additive manufacturing process is applied to metal manufacturing using additive manufacturing technology. The additive manufacturing process is used to achieve efficient, precise and intelligent manufacturing of metal parts, improve production efficiency, and is suitable for mass production of metal parts, with broad application prospects.

[0030] Furthermore, the specific analysis process of obtaining the metal manufacturing optimization method is: obtaining the current metal manufacturing method; optimizing the current metal manufacturing method, and optimizing the current metal manufacturing method specifically includes: improving process parameters, optimizing interlayer bonding process, using special metal materials that match the additive manufacturing process, and improving heat treatment process; collecting all methods obtained after optimizing the current metal manufacturing method, and using all methods as metal manufacturing optimization methods.

[0031] In this embodiment, the current metal manufacturing method can be optimized through a variety of methods, for example, by improving the uniformity of the microstructure by improving process parameters and optimizing the interlayer bonding process; developing special metal materials that match the additive manufacturing process to improve the mechanical properties of the parts; using a more precise machine control system to reduce errors and improve printing accuracy and repeatability; integrating online monitoring technology to monitor key parameters in the printing process in real time and automatically adjust the parameters to optimize the performance of the final part; developing more efficient and lower-cost additive manufacturing equipment, and reducing unit costs through mass production.

[0032] Furthermore, the reliability evaluation coefficient of the optimization method can be obtained through the following process, including but not limited to: measuring the cost-effectiveness of the manufacturing process through the cost function, which may include material cost, energy consumption, machine loss, etc.; measuring product quality and consistency through the quality function, such as dimensional tolerance, surface finish, internal defects, etc.; measuring the time efficiency from order receipt to final delivery through the delivery time function; combining the objective functions such as cost function, quality function and delivery time function to obtain the reliability evaluation coefficient of the optimization method. The reliability evaluation coefficient of the optimization method can also be obtained by calculation. The specific method for obtaining the reliability evaluation coefficient of the optimization method is: obtaining all metal manufacturing optimization methods and numbering the metal manufacturing optimization methods; obtaining manufacturing optimization parameters, which include: energy consumption rate, metal manufacturing accuracy and metal manufacturing efficiency; constructing a calculation formula for the reliability evaluation coefficient of the optimization method; the specific calculation formula for the reliability evaluation coefficient of the optimization method is:

[0033] In the formula, Expressed as The reliability evaluation coefficient of the optimization method for metal manufacturing optimization method, , Expressed as the total number of metal manufacturing optimization methods, Expressed as Energy consumption rate of a metal manufacturing optimization method, Expressed as Metal manufacturing accuracy of a metal manufacturing optimization method, Expressed as Metal manufacturing efficiency of a metal manufacturing optimization method, It is expressed as the weight ratio of energy consumption rate in the reliability evaluation coefficient of the optimization method. It is expressed as the weight ratio of metal manufacturing accuracy in the reliability evaluation coefficient of the optimization method. It is expressed as the weight ratio of metal manufacturing efficiency in the reliability evaluation coefficient of the optimization method. Expressed as a natural constant.

[0034] In this embodiment, to determine whether a method is good or not, it is usually necessary to conduct a comprehensive evaluation based on multiple aspects such as the degree of achievement of the purpose, efficiency, energy consumption rate, cost-effectiveness, and the impact on other factors. Here, the metal manufacturing optimization method is evaluated from three aspects: energy consumption rate, metal manufacturing accuracy, and metal manufacturing efficiency. By evaluating the metal manufacturing optimization method, the feasibility of the metal manufacturing optimization method in practical application is clarified.

[0035] After obtaining the manufacturing optimization parameters, data processing such as normalization, data cleaning and preprocessing is performed to reduce the error rate of the manufacturing optimization parameters, which is conducive to obtaining a more accurate reliability evaluation coefficient of the optimization method based on the manufacturing optimization parameters.

[0036] Connect a power meter to the power supply or key components of the equipment that performs metal manufacturing to measure the power consumed and obtain the energy consumption rate based on power analysis. Use optical meters and laser measuring instruments to obtain metal manufacturing accuracy. Count the duration of metal manufacturing and the number of completed items, and compare the two to obtain metal manufacturing efficiency.

[0037] Furthermore, the reliability evaluation coefficient of the optimization method is analyzed to obtain the specific analysis process of the reliable optimization method: obtain the reliability evaluation coefficient of the optimization method, arrange the reliability evaluation coefficients of the optimization method in order from large to small, and take the metal manufacturing optimization method corresponding to the reliability evaluation coefficient of the top N optimization methods as the reliable optimization method, where N is represented by a positive integer.

[0038] In this embodiment, the larger the reliability evaluation coefficient of the optimization method, the higher the reliability of the metal manufacturing optimization method; the smaller the reliability evaluation coefficient of the optimization method, the lower the reliability of the metal manufacturing optimization method. The method with high reliability is regarded as a reliable optimization method, and the optimization degree of the reliable optimization method is analyzed. The method with low reliability is not analyzed subsequently, so as to reduce the time wasted in analyzing the method with low reliability. The value range of N is: N is less than the total number of reliable optimization methods.

[0039] Furthermore, the specific analysis process of obtaining reliable optimization parameters corresponding to the reliable optimization method is: obtaining a reliable optimization method; using the reliable optimization method to manufacture metals, real-time monitoring of the process of using the reliable optimization method to manufacture metals, and acquiring data of the manufactured metal products, and performing data processing after data acquisition to obtain reliable optimization parameters; reliable optimization parameters include: dimensional accuracy parameters, mechanical property parameters and processing influencing parameters.

[0040] In this embodiment, data processing is performed after data acquisition to obtain reliable optimization parameters. The data processing process includes data cleaning, data conversion, data storage and preprocessing, etc., so that the accuracy and reliability of the reliable optimization parameters are higher, which is conducive to more accurate calculation processes involving subsequent reliable optimization parameters.

[0041] Furthermore, the specific process of obtaining the goodness evaluation index of the optimization method according to the reliable optimization coefficient is: obtaining reliable optimization parameters; analyzing the reliable optimization parameters to obtain the reliable optimization coefficient, specifically including: obtaining the dimensional accuracy index of the metal product according to the dimensional accuracy parameter analysis, obtaining the mechanical performance index of the metal product according to the mechanical performance parameter analysis, and obtaining the product processing influence index according to the processing influence parameter analysis; the reliable optimization coefficient includes: the dimensional accuracy index of the metal product, the mechanical performance index of the metal product and the product processing influence index; analyzing the reliable optimization coefficient to obtain the goodness evaluation index of the optimization method.

[0042] In this embodiment, the product processing influence index can be obtained by the following method, including: using a multi-objective optimization algorithm, considering multiple indicators (such as the number of defects, production time, cost, etc.) at the same time, to evaluate the quality of the product production method; using regression analysis to determine the influence of factors such as production time on the quality of the production method. The product processing influence index can also be calculated, and the specific product processing influence index calculation formula is:

[0043] In the formula, Expressed as A reliable optimization method to manufacture Product processing impact indicators of metal products, , Expressed as the total number of reliable optimization methods, , Expressed as the total number of metal products, Expressed as A reliable optimization method to manufacture Metal products in The total number of defects in metal products at the time of information collection, , It is expressed as the total number of information collection times. Expressed as A reliable optimization method to manufacture Metal products in The manufacturing time of the metal product at the time of information collection, Expressed as Standard manufacturing time for metal products per metal product, Expressed as A reliable optimization method to manufacture Metal products in The secondary processing time of product parts during the first information collection, Expressed as Standard time for secondary processing of metal product parts, Expressed as A reliable optimization method to manufacture Metal products in The number of secondary processing steps of product parts at the time of information collection, Expressed as The number of standard steps for secondary processing of product parts of metal products, Expressed as a correction factor corresponding to the total number of defects in metal products, It is expressed as the correction factor corresponding to the manufacturing time of metal products, It is expressed as the correction factor corresponding to the secondary processing time of product parts, Expressed as a correction factor corresponding to the number of secondary processing steps for product parts.

[0044] Data conversion, data integration, data storage and preprocessing are performed on the processing influencing parameters, so that the fault tolerance of the processing influencing parameters is reduced, which is conducive to obtaining more accurate product processing influencing indicators based on processing influencing parameters with higher accuracy.

[0045] Conduct a preliminary inspection of metal products by visual observation or using magnifying glasses, microscopes and other tools to identify visible defects such as cracks, pores, inclusions, shrinkage cavities, etc. Use various non-destructive testing methods such as ultrasonic testing, radiographic testing (X-rays, gamma rays), magnetic particle testing, penetration testing and eddy current testing to find defects inside metal products. Count all defects to get the total number of defects, which is used as the total number of defects in metal products. Use cameras, mobile phones, video recorders, timers and clocks to record the manufacturing process and secondary processing of metal products and obtain the manufacturing time of metal products and the secondary processing time of product parts.

[0046] Furthermore, the specific analysis process of the metal product dimensional accuracy index is: extracting the metal product size, metal product weight and product part size from the dimensional accuracy parameters, comparing the metal product size with the metal product reference size, comparing the metal product weight with the metal product reference weight, and comparing the product part size with the product part reference size, and performing analysis and processing to obtain the metal product dimensional accuracy index.

[0047] In this embodiment, the dimensional accuracy index of metal products can be obtained by a variety of methods, such as: using the least squares method for curve fitting and data regression analysis to determine the dimensional change trend during the manufacturing process; using descriptive statistics, hypothesis testing (such as t-test, ANOVA), and regression analysis to analyze the measured data to determine whether the size and shape of the product are within the tolerance range. The specific method for obtaining the dimensional accuracy index of metal products is: obtaining the number of reliable optimization methods and numbering the reliable optimization methods; obtaining the number of metal products made according to the reliable optimization method and numbering the metal products; performing multiple information collection on metal products, obtaining the number of information collection times, and numbering the number of information collection times; obtaining the number of parts contained in the metal product and numbering the parts; constructing a calculation formula for the dimensional accuracy index of metal products: The specific calculation formula for the dimensional accuracy index of metal products is:

[0048] In the formula, Expressed as A reliable optimization method to manufacture Metal product dimensional accuracy indicators for metal products, , Expressed as the total number of reliable optimization methods, , Expressed as the total number of metal products, Expressed as A reliable optimization method to manufacture Metal products in The size of the metal product at the time of the first information collection, , It is expressed as the total number of information collection times. Expressed as Metal product reference dimensions for each metal product, Expressed as The permissible dimensional error of a metal product is Expressed as A reliable optimization method to manufacture Metal products in The weight of metal products at the time of information collection, Expressed as Metal product reference weight of each metal product, Expressed as The permissible weight error of a metal product is Expressed as A reliable optimization method to manufacture Metal products Parts in The product part size at the time of information collection, , Expressed as the total number of parts, Expressed as Metal products Product part reference dimensions for each part, It is expressed as the weight ratio of metal product size in metal product size accuracy index. It is expressed as the weight ratio of the metal product weight to the metal product dimensional accuracy index. It is expressed as the weight ratio of product part size in the metal product size accuracy index. Expressed as a natural constant.

[0049] By integrating, selecting, transforming and cleaning the dimensional accuracy parameters, the error rate of dimensional accuracy parameters can be reduced, which is conducive to obtaining more accurate dimensional accuracy indicators for metal products.

[0050] Use calipers, micrometers, depth gauges, measuring rods, micrometers, projectors, coordinate measuring machines and optical measuring instruments to inspect metal products and parts of metal products to obtain the dimensions of metal products and product parts. Use electronic balances, platform scales, floor scales, crane scales, etc. to inspect the weight of metal products, and the weight data obtained is used as the weight of metal products.

[0051] Furthermore, the specific analysis process of the mechanical performance indicators of metal products is as follows: The hardness, tensile strength and toughness of metal products are extracted from the mechanical performance parameters, and the hardness, tensile strength, toughness, standard hardness, standard tensile strength and standard toughness of metal products are analyzed to obtain the mechanical performance indicators of metal products.

[0052] In this embodiment, the mechanical performance index of the metal product can be obtained by the following methods, including but not limited to: comparing the obtained mechanical performance parameters (such as tensile strength, yield strength, hardness, etc.) with the required values ​​in the product standard or design specification; using the least squares method to fit the data and determine the best fitting line or curve to evaluate the change trend of the mechanical performance. The mechanical performance index of the metal product can also be obtained by calculation. The specific calculation formula of the mechanical performance index of the metal product is:

[0053] In the formula, Expressed as A reliable optimization method to manufacture The mechanical performance indicators of metal products of each metal product, Expressed as A reliable optimization method to manufacture Metal products in The hardness of the metal product at the time of information collection, Expressed as Standard hardness of metal products for each metal product, Expressed as A reliable optimization method to manufacture Metal products in The tensile strength of the metal product at the time of information collection, Expressed as Standard tensile strength of metal products for each metal product, Expressed as A reliable optimization method to manufacture Metal products in The toughness of metal products at the time of information collection, Expressed as Standard toughness of metal products for each metal product, It is expressed as the influence factor corresponding to the hardness of the set metal product, It is expressed as the influence factor corresponding to the set tensile strength of the metal product, Expressed as the influence factor corresponding to the set toughness of the metal product.

[0054] By organizing, calculating, storing and cleaning the mechanical performance parameters, more accurate mechanical performance parameters are obtained, which is conducive to obtaining more accurate mechanical performance indicators of metal products through mechanical performance parameter calculation.

[0055] The hardness of metal products is measured by using a hardness tester (such as Brinell hardness tester, Rockwell hardness tester, etc.). The tensile test is performed using a tensile testing machine to measure the stress and strain of metal products during the stretching process, thereby determining the tensile strength, which is used as the tensile strength of the metal product. The impact test is performed using an impact testing machine to measure the absorbed energy and fracture toughness of metal products under impact loads, and the toughness of metal products is obtained.

[0056] The reliability evaluation coefficient of the optimization method is used to describe the reliability of the metal manufacturing optimization method; the metal product dimensional accuracy index is used to express the dimensional accuracy of the metal products manufactured by the reliable optimization method; the metal product mechanical property index is used to reflect the performance of the metal products manufactured by the reliable optimization method; the product processing influence index is used to refer to the processing difficulty of the metal products manufactured by the reliable optimization method; the optimization method goodness evaluation index is used to reflect the optimization degree of the reliable optimization method.

[0057] The smaller the metal product dimensional accuracy index is, the smaller the dimensional error of the metal product is, and the better the reliable optimization method is; the larger the metal product dimensional accuracy index is, the larger the dimensional error of the metal product is, and the worse the reliable optimization method is.

[0058] The smaller the mechanical property index of the metal product, the better the mechanical property of the metal product and the better the reliable optimization method; the larger the mechanical property index of the metal product, the worse the mechanical property of the metal product and the worse the reliable optimization method.

[0059] The smaller the product processing impact index, the fewer defects the product has, the more convenient the secondary processing is, and the better the reliable optimization method is; the larger the product processing impact index, the more defects the product has, the more troublesome the secondary processing is, and the worse the reliable optimization method is.

[0060] Furthermore, the goodness evaluation index of the optimization method can be obtained by the following methods, including: evaluating the effect of the optimization method by constructing an objective function for minimizing cost, maximizing efficiency, and minimizing energy consumption; in the optimization process, it is necessary to consider various constraints in the manufacturing process, such as machine capacity, raw material supply, process restrictions, etc., and use mathematical programming methods (such as linear programming, nonlinear programming, integer programming, etc.) to find the optimal solution that meets all constraints. The goodness evaluation index of the optimization method can also be calculated. The specific method for obtaining the goodness evaluation index of the optimization method is: obtain the number of reliable optimization methods and number the reliable optimization methods; obtain the number of metal products produced according to the reliable optimization method and number the metal products; construct a calculation formula for the goodness evaluation index of the optimization method; the specific calculation formula for the goodness evaluation index of the optimization method is:

[0061] In the formula, Expressed as The optimization method goodness evaluation index of the reliable optimization method, , Expressed as the total number of reliable optimization methods, Expressed as A reliable optimization method to manufacture Metal product dimensional accuracy indicators for metal products, , Expressed as the total number of metal products, Expressed as A reliable optimization method to manufacture The mechanical performance indicators of metal products of each metal product, Expressed as A reliable optimization method to manufacture Product processing impact indicators of metal products, It is expressed as the weight ratio of the metal product dimensional accuracy index in the optimization method goodness evaluation index. It is expressed as the weight ratio of the metal product mechanical performance index in the optimization method goodness evaluation index. It is expressed as the weight ratio of product processing impact indicators in the optimization method goodness evaluation index.

[0062] In this embodiment, the optimization method goodness evaluation index is analyzed from three aspects: metal product dimensional accuracy index, metal product mechanical property index and product processing influence index, so that the optimization method goodness evaluation index is more comprehensive. The optimization degree of a reliable optimization method can be more accurately analyzed through the optimization method goodness evaluation index.

[0063] Furthermore, the specific analysis process of obtaining the best metal manufacturing optimization method based on the optimization method goodness evaluation index analysis is: obtain the optimization method goodness evaluation index, arrange the optimization method goodness evaluation indexes in descending order, extract the optimization method goodness evaluation index that ranks first, and use the reliable optimization method corresponding to the optimization method goodness evaluation index as the best metal manufacturing optimization method.

[0064] In this embodiment, the larger the optimization method goodness evaluation index is, the higher the optimization degree of the reliable optimization method is; the smaller the optimization method goodness evaluation index is, the lower the optimization degree of the reliable optimization method is. The optimization method goodness evaluation index ranked first is extracted, and the reliable optimization method corresponding to the optimization method goodness evaluation index is the highest, and it is taken as the best metal manufacturing optimization method.

[0065] like Figure 2As shown, it is a structural schematic diagram of a metal manufacturing optimization system based on an additive manufacturing process provided in an embodiment of the present application. The metal manufacturing optimization system based on an additive manufacturing process provided in an embodiment of the present application includes: a method optimization module, a parameter acquisition module, a coefficient acquisition module, a coefficient analysis module, a reliable parameter acquisition module, an optimization coefficient acquisition module, an optimization coefficient processing module and an optimization analysis module: a method optimization module: used to acquire the current metal manufacturing method, and optimize the current metal manufacturing method to obtain a metal manufacturing optimization method; a parameter acquisition module: used to acquire manufacturing optimization parameters corresponding to the metal manufacturing optimization method; a coefficient acquisition module: used to obtain a reliability evaluation coefficient of the optimization method according to the manufacturing optimization parameters; a coefficient analysis module: used to analyze the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method; a reliable parameter acquisition module: used to acquire reliable optimization parameters corresponding to the reliable optimization method; an optimization coefficient acquisition module: used to obtain a reliable optimization coefficient according to the reliable optimization parameters; an optimization coefficient processing module: used to obtain an optimization method goodness evaluation index according to the reliable optimization coefficient; an optimization analysis module: used to obtain the best metal manufacturing optimization method according to the optimization method goodness evaluation index analysis.

[0066] The technical scheme in the above-mentioned embodiment of the present application has at least the following technical effects or advantages: relative to a support structure optimization design method for controlling thermal stress of metal additive manufacturing disclosed in a patent application with publication number: CN116227084A, the embodiment of the present application obtains the current metal manufacturing method, optimizes the current metal manufacturing method to obtain a metal manufacturing optimization method, obtains the manufacturing optimization parameters corresponding to the metal manufacturing optimization method, obtains the reliability evaluation coefficient of the optimization method according to the manufacturing optimization parameters, and then analyzes the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method, thereby realizing the rapid elimination of unsuitable optimization directions and avoiding spending too much time and energy on unsuitable optimization directions; relative to a method for manufacturing metal nodes of building structures based on additive manufacturing technology announced in an invention patent with publication number: CN115906339B, the embodiment of the present application optimizes and adjusts the machinery for metal manufacturing by using the best metal manufacturing optimization method, thereby improving the production efficiency and accuracy of the machinery, thereby reducing the economic losses caused by low production efficiency and low accuracy.

[0067] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0071] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0072] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A metal manufacturing optimization method based on additive manufacturing process, characterized in that: The following steps are involved: Obtaining a current metal manufacturing method, and optimizing the current metal manufacturing method to obtain a metal manufacturing optimization method; Obtaining manufacturing optimization parameters corresponding to the metal manufacturing optimization method; Obtaining the reliability evaluation coefficient of the optimization method according to the manufacturing optimization parameters; Analyze the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method; Obtain reliable optimization parameters corresponding to the reliable optimization method; Obtaining a reliable optimization coefficient according to the reliable optimization parameter; According to the reliable optimization coefficient, the goodness evaluation index of the optimization method is obtained; The best metal manufacturing optimization method is obtained based on the optimization method goodness evaluation index analysis.

2. The metal manufacturing optimization method based on additive manufacturing process as claimed in claim 1, characterized in that: The specific analysis process of obtaining the metal manufacturing optimization method is as follows: Obtain current metal fabrication methods; Optimizing the current metal manufacturing method, wherein the optimization of the current metal manufacturing method specifically includes: improving process parameters, optimizing the interlayer bonding process, using special metal materials that match the additive manufacturing process, and improving the heat treatment process; Collect all methods obtained after optimizing the current metal manufacturing method, and use all methods as metal manufacturing optimization methods.

3. The metal manufacturing optimization method based on additive manufacturing process as claimed in claim 1, characterized in that: The specific method for obtaining the reliability evaluation coefficient of the optimization method is: Obtain all metal manufacturing optimization methods and number the metal manufacturing optimization methods; Acquiring manufacturing optimization parameters, wherein the manufacturing optimization parameters include: energy consumption rate, metal manufacturing accuracy and metal manufacturing efficiency; Construct a calculation formula for the reliability evaluation coefficient of the optimization method; The specific optimization method reliability evaluation coefficient calculation formula is: In the formula, Expressed as The reliability evaluation coefficient of the optimization method for metal manufacturing optimization method, , Expressed as the total number of metal manufacturing optimization methods, Expressed as Energy consumption rate of a metal manufacturing optimization method, Expressed as Metal manufacturing accuracy of a metal manufacturing optimization method, Expressed as Metal manufacturing efficiency of a metal manufacturing optimization method, It is expressed as the weight ratio of energy consumption rate in the reliability evaluation coefficient of the optimization method. It is expressed as the weight ratio of metal manufacturing accuracy in the reliability evaluation coefficient of the optimization method. It is expressed as the weight ratio of metal manufacturing efficiency in the reliability evaluation coefficient of the optimization method. Expressed as a natural constant.

4. The metal manufacturing optimization method based on additive manufacturing process as claimed in claim 1, characterized in that: The specific analysis process of analyzing the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method is as follows: Obtain the reliability evaluation coefficient of the optimization method, arrange the reliability evaluation coefficients of the optimization method in descending order, and take the metal manufacturing optimization method corresponding to the reliability evaluation coefficient of the top N optimization methods as a reliable optimization method, where N is represented by a positive integer.

5. The metal manufacturing optimization method based on additive manufacturing process as claimed in claim 1, characterized in that: The specific process of obtaining the goodness evaluation index of the optimization method according to the reliable optimization coefficient is as follows: Obtain reliable optimization methods; Using a reliable optimization method to manufacture metals, real-time monitoring of the process of using the reliable optimization method to manufacture metals, acquiring data of manufactured metal products, and processing the data to obtain reliable optimization parameters after the data is acquired; The reliable optimization parameters include: dimensional accuracy parameters, mechanical performance parameters and processing influence parameters; Analyze the reliable optimization parameters to obtain the reliable optimization coefficient, specifically including: obtain the dimensional accuracy index of the metal product according to the dimensional accuracy parameter analysis, obtain the mechanical performance index of the metal product according to the mechanical performance parameter analysis, and obtain the product processing influence index according to the processing influence parameter analysis; The reliable optimization coefficients include: metal product dimensional accuracy index, metal product mechanical performance index and product processing impact index; The reliable optimization coefficient is analyzed to obtain the goodness evaluation index of the optimization method.

6. The metal manufacturing optimization method based on additive manufacturing process as claimed in claim 5, characterized in that: The specific analysis process of the metal product dimensional accuracy index is as follows: The metal product size, metal product weight and product part size are extracted from the dimensional accuracy parameters, the metal product size is compared with the metal product reference size, the metal product weight is compared with the metal product reference weight, and the product part size is compared with the product part reference size, and the metal product dimensional accuracy index is obtained by analyzing and processing.

7. The metal manufacturing optimization method based on additive manufacturing process as claimed in claim 5, characterized in that: The specific analysis process of the mechanical performance indicators of the metal products is as follows: The hardness, tensile strength and toughness of metal products are extracted from the mechanical performance parameters, and the hardness, tensile strength, toughness, standard hardness, standard tensile strength and standard toughness of metal products are analyzed to obtain the mechanical performance indicators of metal products.

8. The metal manufacturing optimization method based on additive manufacturing process as claimed in claim 1, characterized in that: The specific method for obtaining the goodness evaluation index of the optimization method is: Obtain the number of reliable optimization methods and number the reliable optimization methods; Obtain the number of metal products produced according to the reliable optimization method and number the metal products; Construct a calculation formula for the goodness evaluation index of the optimization method; The specific calculation formula for the goodness evaluation index of the optimization method is: In the formula, Expressed as The optimization method goodness evaluation index of the reliable optimization method, , Expressed as the total number of reliable optimization methods, Expressed as A reliable optimization method for manufacturing Metal product dimensional accuracy indicators for metal products, , Expressed as the total number of metal products, Expressed as A reliable optimization method to manufacture The mechanical performance indicators of metal products of each metal product, Expressed as A reliable optimization method for manufacturing Product processing impact indicators of metal products, It is expressed as the weight ratio of the metal product dimensional accuracy index in the optimization method goodness evaluation index. It is expressed as the weight ratio of the metal product mechanical performance index in the optimization method goodness evaluation index. It is expressed as the weight ratio of product processing impact indicators in the optimization method goodness evaluation index.

9. The metal manufacturing optimization method based on additive manufacturing process as claimed in claim 1, characterized in that: The specific analysis process of obtaining the best metal manufacturing optimization method according to the optimization method goodness evaluation index analysis is as follows: The goodness evaluation index of the optimization method is obtained, and the goodness evaluation index of the optimization method is arranged in descending order. The goodness evaluation index of the optimization method that ranks first is extracted, and the reliable optimization method corresponding to the goodness evaluation index of the optimization method is used as the best metal manufacturing optimization method.

10. A metal manufacturing optimization system based on additive manufacturing process, characterized in that: include: Method optimization module, parameter acquisition module, coefficient acquisition module, coefficient analysis module, reliable parameter acquisition module, optimization coefficient acquisition module, optimization coefficient processing module and optimization analysis module: The method optimization module is used to obtain the current metal manufacturing method and optimize the current metal manufacturing method to obtain the metal manufacturing optimization method; The parameter acquisition module is used to acquire the manufacturing optimization parameters corresponding to the metal manufacturing optimization method; The coefficient acquisition module is used to obtain the reliability evaluation coefficient of the optimization method according to the manufacturing optimization parameters; The coefficient analysis module is used to analyze the reliability evaluation coefficient of the optimization method to obtain a reliable optimization method; The reliable parameter acquisition module is used to obtain reliable optimization parameters corresponding to the reliable optimization method; The optimization coefficient acquisition module is used to obtain a reliable optimization coefficient according to a reliable optimization parameter; The optimization coefficient processing module is used to obtain an optimization method goodness evaluation index according to the reliable optimization coefficient; The optimization analysis module is used to obtain the best metal manufacturing optimization method based on the optimization method goodness evaluation index analysis.

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