Heat treatment process optimization method and system for hardware workpiece

Abstract

The invention provides a heat treatment process optimization method and system for a hardware workpiece, and relates to the technical field of data processing.The method comprises the steps that heat treatment request information of the hardware workpiece is obtained, initial heat treatment control parameters are obtained, then quality evaluation is conducted, a first control quality coefficient is obtained, and when a quality coefficient threshold value is not met, optimization is conducted; and performing heat treatment control on the heat treatment equipment according to the optimization result. The problems that in the prior art, heat treatment parameters cannot be adjusted according to components and needed performance of the hardware workpiece, treatment parameters not suitable for the current hardware workpiece cannot be recognized, and the heat treatment parameters cannot be adjusted in time can be solved, accurate control over heat treatment equipment is achieved, and therefore the quality and performance of the hardware workpiece are improved; the product quality is improved, the rejection rate is reduced, and the production efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method and system for optimizing the heat treatment process of metal hardware workpieces. Background Art

[0002] First of all, heat treatment is an important link in the manufacturing process of metal hardware workpieces. Through heat treatment, the internal organizational structure of the material can be changed, thereby improving its mechanical properties, physical properties, and chemical properties to meet specific usage requirements. However, traditional heat treatment processes often have problems such as high energy consumption and serious environmental pollution, which not only increase the production costs of enterprises but also bring a significant burden to the environment. With the continuous development of the manufacturing industry, the performance requirements for metal hardware workpieces are also constantly increasing. To meet market demands, enterprises need to continuously seek technological innovation and process improvement to improve product quality and production efficiency. The optimization of the heat treatment process is one of the key technological innovations.

[0003] In summary, the prior art cannot adjust the heat treatment parameters according to the composition and required performance of metal hardware workpieces, cannot identify the processing parameters that are not suitable for the current metal hardware workpieces, and cannot timely adjust the heat treatment parameters. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for optimizing the heat treatment process of metal hardware workpieces to solve the problems that the prior art cannot adjust the heat treatment parameters according to the composition and required performance of metal hardware workpieces, cannot identify the processing parameters that are not suitable for the current metal hardware workpieces, and cannot timely adjust the heat treatment parameters.

[0005] In view of the above problems, this application provides a method and system for optimizing the heat treatment process of metal hardware workpieces.

[0006] In a first aspect, the present application provides a method for optimizing the heat treatment process of a hardware workpiece. The method is implemented through a heat treatment process optimization system for a hardware workpiece. Wherein, the method includes: obtaining heat treatment request information of the hardware workpiece, where the heat treatment request information of the hardware workpiece includes the composition ratio of the hardware workpiece and the desired properties of the hardware workpiece; communicating with the heat treatment equipment to obtain initial heat treatment control parameters, where the initial heat treatment control parameters include initial temperature rise timing information, initial peak temperature information, initial heat preservation duration information, and initial temperature drop timing information; according to the composition ratio of the hardware workpiece and the desired properties of the hardware workpiece, performing a control quality assessment on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain a first control quality coefficient; when the first control quality coefficient does not meet the control quality coefficient threshold, optimizing the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain a heat treatment process optimization result; performing heat treatment control on the heat treatment equipment according to the heat treatment process optimization result.

[0007] In a second aspect, the present application further provides a heat treatment process optimization system for a hardware workpiece, which is used to execute a method for optimizing the heat treatment process of a hardware workpiece as described in the first aspect. Wherein, the system includes: a request information acquisition module, which is used to obtain heat treatment request information of the hardware workpiece, where the heat treatment request information of the hardware workpiece includes the composition ratio of the hardware workpiece and the desired properties of the hardware workpiece; a control parameter acquisition module, which is used to communicate with the heat treatment equipment to obtain initial heat treatment control parameters, where the initial heat treatment control parameters include initial temperature rise timing information, initial peak temperature information, initial heat preservation duration information, and initial temperature drop timing information; a first control quality coefficient acquisition module, which is used to perform a control quality assessment on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information according to the composition ratio of the hardware workpiece and the desired properties of the hardware workpiece to obtain a first control quality coefficient; a process optimization result acquisition module, which is used to optimize the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain a heat treatment process optimization result when the first control quality coefficient does not meet the control quality coefficient threshold; a heat treatment control module, which is used to perform heat treatment control on the heat treatment equipment according to the heat treatment process optimization result.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: By obtaining heat treatment request information of a metal workpiece, where the heat treatment request information of the metal workpiece includes the composition ratio of the metal workpiece and the desired properties of the metal workpiece; communicating with a heat treatment device to obtain initial heat treatment control parameters, where the initial heat treatment control parameters include initial temperature rise timing information, initial peak temperature information, initial heat preservation duration information, and initial temperature drop timing information; performing a control quality assessment on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information according to the composition ratio of the metal workpiece and the desired properties of the metal workpiece to obtain a first control quality coefficient; when the first control quality coefficient does not meet the control quality coefficient threshold, optimizing the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain a heat treatment process optimization result; performing heat treatment control on the heat treatment device according to the heat treatment process optimization result effectively solves the problem that the prior art cannot adjust heat treatment parameters according to the composition and required properties of metal workpieces, cannot identify processing parameters that are not suitable for the current metal workpiece, and cannot adjust heat treatment parameters in a timely manner, realizes precise control of the heat treatment device, thereby improving the quality and performance of metal workpieces, enhancing product quality, reducing the scrap rate, and improving production efficiency.

[0009] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following specifically gives the specific implementation manners of the present application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0011] Figure 1 It is a schematic flowchart of a heat treatment process optimization method for a metal workpiece of the present application; Figure 2 It is a schematic structural diagram of a heat treatment process optimization system for a metal workpiece of the present application.

[0012] Description of the Reference Numerals: Request information acquisition module 11, control parameter acquisition module 12, first control quality coefficient acquisition module 13, process optimization result acquisition module 14, heat treatment control module 15. Detailed implementation mode

[0013] By providing a method and system for optimizing the heat treatment process of hardware workpieces, the present application solves the problems in the prior art that the heat treatment parameters cannot be adjusted according to the composition and required performance of the hardware workpieces, the processing parameters unsuitable for the current hardware workpieces cannot be identified, and the heat treatment parameters cannot be adjusted in time, realizes the precise control of the heat treatment equipment, thereby improving the quality and performance of the hardware workpieces, enhancing the product quality, reducing the rejection rate, and improving the production efficiency.

[0014] Next, the technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application. In addition, it should be noted that for the sake of description, only the parts related to the present application are shown in the accompanying drawings rather than all.

[0015] Embodiment 1 Please refer to the attached Figure 1 , the present application provides a method for optimizing the heat treatment process of hardware workpieces. Among them, the method is applied to a system for optimizing the heat treatment process of hardware workpieces, and the method specifically includes the following steps: S1: Obtain the heat treatment request information of the hardware workpiece. Among them, the heat treatment request information of the hardware workpiece includes the composition ratio of the hardware workpiece and the desired performance of the hardware workpiece; Specifically, after receiving the heat treatment request information of the hardware workpiece, first analyze the composition ratio of the hardware workpiece in detail. This ratio will directly affect the effect of heat treatment because different material compositions respond differently to heat treatment. For example, the carbon content will affect the hardness and strength of steel, while the type and content of alloying elements may affect parameters such as the heat treatment temperature and time. Next, clarify the desired performance of the hardware workpiece. These performances may include hardness, toughness, corrosion resistance, wear resistance, etc.

[0016] S2: Communicate with the heat treatment equipment to obtain the initial heat treatment control parameters. Among them, the initial heat treatment control parameters include the initial temperature rise timing information, the initial peak temperature information, the initial holding time information, and the initial temperature drop timing information; Specifically, the initial heat treatment control parameters include initial temperature ramp timing information, initial peak temperature information, initial holding time information, and initial temperature drop timing information. The initial temperature ramp timing information parameter describes the time arrangement from the start of heat treatment to reaching the peak temperature. A reasonable temperature ramp timing can ensure that the workpiece is uniformly heated and avoid internal stress or workpiece deformation caused by overheating. The initial peak temperature information refers to the highest temperature that needs to be reached during the heat treatment process. The selection of the peak temperature depends on the material of the workpiece and the desired organizational structure change. Too high or too low peak temperature may affect the final performance of the workpiece. The initial holding time information The holding time refers to the time to maintain this temperature after reaching the peak temperature. This period is crucial for ensuring the full transformation of the internal organizational structure of the workpiece. Insufficient holding time may lead to incomplete transformation of the organizational structure, while too long holding time may cause grain coarsening and affect the performance of the workpiece. The initial temperature drop timing information This parameter describes the time arrangement from the peak temperature to room temperature. A reasonable cooling rate can prevent cracks or internal stress in the workpiece and ensure that the workpiece obtains the required mechanical properties.

[0017] S3: According to the composition ratio of the hardware workpiece and the desired performance of the hardware workpiece, conduct a control quality assessment on the initial temperature ramp timing information, the initial peak temperature information, the initial holding time information, and the initial temperature drop timing information to obtain a first control quality coefficient; Specifically, analyze the composition ratio of the hardware workpiece, the material composition of the workpiece, alloying elements and their ratios, and clarify the performance indicators that the workpiece should achieve after heat treatment, such as hardness, toughness, corrosion resistance, etc. According to the material and desired performance of the workpiece, evaluate whether the temperature ramp rate is appropriate. Too fast a temperature ramp may increase the internal stress of the workpiece, while too slow may affect production efficiency. The selection of the peak temperature directly affects the tissue transformation of the workpiece. According to the material and required performance of the workpiece, evaluate whether the peak temperature is appropriate to ensure an ideal organizational structure. According to the material, thickness and required performance of the workpiece, evaluate whether the holding time is sufficient. The cooling rate has a great impact on the final performance of the workpiece. Evaluate whether the cooling timing is reasonable to avoid excessive internal stress or workpiece deformation. Based on the results of the above evaluations, give a quantitative coefficient, that is, the first control quality coefficient. This coefficient can reflect the matching degree between the initial heat treatment control parameters and the composition ratio and desired performance of the workpiece.

[0018] S4: When the first control quality coefficient does not meet the control quality coefficient threshold, optimize the initial temperature ramp timing information, the initial peak temperature information, the initial holding time information, and the initial temperature drop timing information to obtain the heat treatment process optimization result.

[0019] Specifically, when the first control quality coefficient does not meet the control quality coefficient threshold, that is, the first quality evaluation factor is less than the first quality factor threshold, clarify the optimization goal, such as improving hardness, toughness or corrosion resistance, or reducing problems such as deformation and cracking. According to the material, shape, size and production requirements of the hardware workpiece, set the constraint conditions for each parameter, such as temperature range, time limit, etc. Genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, etc. can be used to search for the optimal solution in the parameter space, and the optimal parameter combination that meets the performance requirements can be found through iterative and mutation operations. Parameters such as the initial temperature rise time series information, the initial peak temperature information, the initial holding time information, and the initial temperature drop time series information are used as the input of the optimization algorithm. Set parameters such as the iteration number and mutation rate of the algorithm, and start the optimization process. During the optimization process, the algorithm will continuously adjust the values of each parameter to find the optimal combination.

[0020] S5: Perform heat treatment control on the heat treatment equipment according to the heat treatment process optimization result.

[0021] Specifically, calibrate the equipment to ensure the accuracy of temperature and time control. According to the optimization result, accurately set parameters such as the temperature rise time series, peak temperature, holding time, and temperature drop time series in the control system of the heat treatment equipment. Ensure that these parameters can be accurately executed to match the requirements of the optimized heat treatment process. Introduce a specific gas or gas mixture during the heat treatment process to improve the heat treatment effect. For example, introducing nitrogen gas during the cooling stage can reduce the oxidation of the part surface. During the heat treatment process, key parameters such as temperature and time are monitored in real time through sensors on the equipment. Fine-tune the control parameters according to the real-time monitoring data to ensure the stability and accuracy of the entire heat treatment process.

[0022] Furthermore, step S3 of the present application further includes: Taking the composition ratio of the hardware workpiece and the expected performance of the hardware workpiece as constraints, perform shared log matching in a preset time zone through the heat treatment sharing blockchain to obtain a shared log matching result; Taking the initial temperature rise time series information, the initial peak temperature information, the initial holding time information, and the initial temperature drop time series information as a reference, traverse the shared log matching result for adjacent sorting to obtain an adjacent log sorting result; Statistically calculate the ratio of the number of the first log records in the adjacent log sorting result to the number of the second log records in the shared log matching result, and set it as the first quality evaluation factor; Traverse the adjacent log sorting result, and perform performance deviation analysis in combination with the expected performance of the hardware workpiece to obtain a set of performance deviation indexes; Perform quality analysis on the set of performance deviation indexes to obtain a second quality evaluation factor; Add the first quality evaluation factor and the second quality evaluation factor into the first control quality coefficient.

[0023] Specifically, collect the composition ratio data and expected performance data of the hardware workpiece. Determine the preset time zone, that is, the time range to be searched. Access the heat treatment shared blockchain and retrieve the shared logs that match the composition ratio and expected performance of the hardware workpiece within the preset time zone. Use the query function of the smart contract or blockchain to filter and match the eligible log records. Obtain the shared log matching result set, which contains the heat treatment records that match the composition ratio and expected performance of the hardware workpiece. Prepare the initial temperature rise timing information, peak temperature information, heat preservation duration information, and temperature drop timing information. Traverse the shared log matching results and compare the similarity of each record with the initial control parameters. According to the similarity threshold, screen out the adjacent logs, that is, the records with similar control parameters. Obtain the adjacent log sorting result set, and these records are similar to the initial control parameters. Count the number of log records in the adjacent log sorting result, the first log record number. Count the total number of records in the shared log matching result, the second log record number. Calculate the ratio of the first log record number to the second log record number. Set the calculated ratio as the first quality evaluation factor. Prepare the expected performance data of the hardware workpiece. Traverse the adjacent log sorting result and compare the actual performance of each record with the expected performance of the hardware workpiece. Calculate the performance deviation, such as using indicators such as mean square error and absolute error. Obtain the performance deviation index set, which reflects the deviation degree between the actual performance and the expected performance. Conduct statistical analysis on the performance deviation index set, such as calculating the average value, standard deviation, etc. Evaluate the overall situation of the performance deviation based on the statistical results. Based on the results of the quality analysis, calculate the second quality evaluation factor, which can be a comprehensive index or score. Obtain the second quality evaluation factor. Add the first quality evaluation factor and the second quality evaluation factor to the first control quality coefficient in an appropriate manner, such as weighted average, simple addition, etc. The first control quality coefficient can be normalized or standardized. Obtain the updated first control quality coefficient, which now contains more quality control information.

[0024] Furthermore, this application also includes: According to the shared log matching result, extract the first shared log, where the first shared log includes the first temperature rise timing information, the first peak temperature information, the first heat preservation duration information, and the first temperature drop timing information; Based on the first temperature rise timing information, the first peak temperature information, the first heat preservation duration information, and the first temperature drop timing information, perform distance analysis based on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain the adjacent sorting distance; When the adjacent sorting distance is less than or equal to the adjacent sorting distance threshold, add the first shared log to the adjacent log sorting result.

[0025] Specifically, extract the first temperature rise time series information, the first peak temperature information, the first heat preservation duration information, and the first temperature drop time series information from the first shared log. Correlate the time series information extracted from the first shared log with the initial time series information, the initial temperature rise time series information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop time series information. Use an appropriate distance metric method, such as Euclidean distance, Manhattan distance, etc., to calculate the distance between the two sets of time series information. This distance reflects the similarity between the two sets of time series information, which is called the adjacent sorting distance. Preset an adjacent sorting distance threshold to determine whether the two sets of time series information are similar enough. Compare the calculated adjacent sorting distance with the adjacent sorting distance threshold. If the adjacent sorting distance is less than or equal to the adjacent sorting distance threshold, it means that the first shared log has a high similarity with the initial time series information, and add it to the adjacent log sorting result. If the adjacent sorting distance is greater than the adjacent sorting distance threshold, do not add the first shared log to the adjacent log sorting result. If there are multiple shared logs to be analyzed, repeat the above steps until all shared logs have been processed.

[0026] Furthermore, this application also includes: Construct a first temperature rise curve in the first coordinate system according to the first temperature rise time series information, and construct a second temperature rise curve in the first coordinate system according to the initial temperature rise time series information; Connect the head of the second temperature rise curve and the first temperature rise curve, and connect the tail of the second temperature rise curve and the first temperature rise curve, obtain the area of the enclosed figure and perform normalization processing, which is set as the first adjacent evaluation factor, where the first adjacent evaluation factor has a first weight; Calculate the deviation between the initial peak temperature information and the first peak temperature information and perform normalization processing to obtain a first eigenvalue, calculate the deviation between the initial heat preservation duration information and the first heat preservation duration information and perform normalization processing to obtain a second eigenvalue; Calculate the product of the first eigenvalue and the second eigenvalue, which is set as the second adjacent evaluation factor, where the second adjacent evaluation factor has a second weight; Construct a first temperature drop curve in the first coordinate system according to the first temperature drop time series information, and construct a second temperature drop curve in the first coordinate system according to the initial temperature drop time series information; Connect the head of the second temperature sinking curve to the head of the first temperature sinking curve, and connect the tail of the second temperature sinking curve to the tail of the first temperature sinking curve, to obtain the area of the enclosed figure and perform normalization processing, which is set as the third proximity evaluation factor, where the third proximity evaluation factor has a third weight; According to the first weight, the second weight, and the third weight, sum the first proximity evaluation factor, the second proximity evaluation factor, and the third proximity evaluation factor to obtain the proximity sorting distance.

[0027] Specifically, in the first coordinate system, the first temperature rise timing information is used to plot the first temperature rise curve. In the same coordinate system, the initial temperature rise timing information is used to plot the second temperature rise curve. Connect the starting points of the second temperature rise curve and the first temperature rise curve to form the first side of the closed figure. Connect the ending points of the two curves to form the second side of the closed figure. Calculate the area of the closed figure thus formed. This can be done through integration or graphic analysis software. To enable comparison of the areas of closed figures of different sizes and units, normalization is required. Normalization can be achieved by dividing the area of the closed figure by a reference area, such as the area of the entire coordinate system or the area of a specific region, to obtain a value between 0 and 1. Set a weight value for the first proximity evaluation factor, which reflects the importance of the temperature rise curve similarity in the overall evaluation. Multiply the normalized area of the closed figure by the first weight to obtain the first proximity evaluation factor. Calculate the deviation between the initial peak temperature information and the first peak temperature information. Calculate the deviation between the initial holding time information and the first holding time information. Normalize the deviation of the peak temperature to obtain the first eigenvalue. This can be achieved by dividing the deviation by a reference range, such as the maximum possible deviation of the peak temperature. Normalize the deviation of the holding time to obtain the second eigenvalue. Similarly, this can be achieved by dividing the deviation by a reference range, such as the maximum possible deviation of the holding time. Multiply the first eigenvalue and the second eigenvalue to obtain a comprehensive deviation index. Set a second weight for this comprehensive deviation index, which reflects the importance of the peak temperature and the holding time in the overall evaluation. Multiply the comprehensive deviation index by the second weight to obtain the second proximity evaluation factor. In the first coordinate system, the first temperature drop timing information is used to plot the first temperature drop curve. In the same coordinate system, the initial temperature drop timing information is used to plot the second temperature drop curve. Similar to the treatment of the temperature rise curve, connect the starting points of the second temperature drop curve and the first temperature drop curve, and connect the ending points of the two curves to form a closed figure. Normalize the area of the closed figure to obtain a value between 0 and 1 to eliminate the influence of different sizes and units. According to the importance of the temperature drop curve in the overall evaluation, set a weight value for the third proximity evaluation factor, i.e., the third weight. Multiply the normalized area of the closed figure by the third weight to obtain the third proximity evaluation factor. Use the first weight, the second weight, and the third weight to weight the first proximity evaluation factor, the second proximity evaluation factor, and the third proximity evaluation factor respectively. Add the weighted three proximity evaluation factors to obtain the proximity sorting distance. The proximity sorting distance is a comprehensive index that takes into account multiple aspects of information such as temperature rise, peak temperature and holding time, and temperature drop, and is used to comprehensively evaluate the similarity or difference between two timing information. The smaller this distance value, the more similar the two timing information; conversely, the greater the difference.

[0028] Furthermore, the present application further includes: According to the adjacent log sorting result, extract the first adjacent log, wherein the first adjacent log includes performance index recorded values; Traverse the performance index attributes and match the deviation distance threshold set; Compare the performance index recorded values with the expected performance of the hardware workpiece to obtain a deviation distance set; Statistically calculate the proportion of the number of performance index attributes in the deviation distance set that is greater than or equal to the deviation distance threshold set, set it as the first performance deviation index, and add it to the performance deviation index set.

[0029] Specifically, select from the adjacent log sorting result the log record that is closest to the current hardware workpiece, that is, the first adjacent log. The first adjacent log should contain the recorded values of the performance indexes of the hardware workpiece, and these performance indexes may be related to hardness, durability, tensile strength, etc. Identify all the performance indexes that need to be evaluated for the hardware workpiece, such as hardness, tensile strength, corrosion resistance, wear resistance, etc. For each performance index, determine an acceptable deviation range or threshold. These thresholds may be set based on industry standards, historical data, or customer requirements. The expected performance of the hardware workpiece is the performance index required by the customer or set according to industry standards or internal enterprise standards. Compare the performance index recorded values in the first adjacent log with the expected performance of the hardware workpiece. Calculate the deviation distance between the two, which can be done through simple difference calculation, percentage deviation, or other applicable mathematical methods. Collect the actual deviation distances of all performance indexes to form a set. Compare the deviation distance of each performance index with its corresponding deviation distance threshold. Calculate the number of performance indexes whose deviation distance is greater than or equal to its corresponding threshold. Divide this number by the total number of performance indexes to obtain the first performance deviation index. Add it to the performance deviation index set. This index set can be used to monitor the quality control in the production process of the hardware workpiece and serve as a basis for improving the process or adjusting the production parameters.

[0030] Furthermore, the present application further includes: Perform an analysis of variance on the performance deviation index set to obtain a performance deviation discrete parameter; When the performance deviation discrete parameter is greater than or equal to the discrete parameter threshold, set the second quality evaluation factor to 0; When the performance deviation discrete parameter is less than the discrete parameter threshold, use 1 minus the mean of the performance deviation index set, and set it as the second quality evaluation factor.

[0031] Specifically, first, calculate the mean of the set of performance deviation indices. Then, calculate the square of the difference between each performance deviation index and the mean. Next, calculate the mean of these squared differences to obtain the variance. The variance is the average measure of the difference between data points and their mean, and is used to quantify the dispersion of the data. The square root of the variance is the standard deviation, which can be used as the performance deviation dispersion parameter. The standard deviation provides a measure of the magnitude of the fluctuations in the set of performance deviation indices. Set a discrete parameter threshold as the boundary for determining whether the performance deviation is acceptable. When the calculated performance deviation dispersion parameter, i.e., the standard deviation, is greater than or equal to this discrete parameter threshold, it means that the performance deviation is too large and the performance indicators of the hardware workpiece may not be stable enough. At this time, set the second quality evaluation factor to 0, indicating that the quality is unacceptable. If the performance deviation dispersion parameter is less than the discrete parameter threshold, it means that the performance indicators of the hardware workpiece are relatively concentrated and the quality is relatively stable. At this time, the second quality evaluation factor is set to 1 minus the mean of the set of performance deviation indices. This is done to reflect the overall level of performance deviation. The smaller the mean, the lower the overall level of deviation, so the second quality evaluation factor will increase accordingly, and vice versa.

[0032] Further, step S4 of the present application further includes: Configure a temperature ramp rate constraint interval, a peak temperature constraint interval, an insulation duration constraint interval, and a temperature sink rate constraint interval to construct a high-dimensional solution space; Step 1: Randomly select the number of solution constraints based on the solution constraint number interval, and obtain a number of initial solutions by taking values through a random distribution function in combination with the high-dimensional solution space; Step 2: Traverse the number of initial solutions for control quality assessment to obtain a number of initial control quality coefficients; Step 3: Obtain the optimal initial solution of the number of initial solutions according to the number of initial control quality coefficients; Step 4: Configure a random perturbation distance threshold, and locate in the high-dimensional solution space in combination with the optimal initial solution to obtain a target search area; Step 5: Obtain a preset number of inferior solutions of the number of initial solutions according to the number of initial control quality coefficients; Step 6: Locate the preset number of inferior solutions in the high-dimensional solution space to obtain a starting position; Step 7: Starting from the starting position, perform a first preset number of traveling searches on random positions in the target search area to obtain an expanded solution set; Step 8: Repeat steps 2 to 7 for a second preset number of times to obtain an end optimal solution; When the control quality coefficient of the end optimal solution meets the control quality coefficient threshold, set the end optimal solution as the optimized result of the heat treatment process; When the control quality coefficient of the end optimal solution does not meet the control quality coefficient threshold, steps 1 to 8 are repeatedly executed.

[0033] Specifically, define the constraint intervals of the temperature rise rate, peak temperature, heat preservation duration, and temperature sink rate. These intervals will constitute the high-dimensional solution space of the optimization problem. Randomly select a quantity within the interval of the number of solution constraints. Use a random distribution function to generate the corresponding number of initial solutions in the high-dimensional solution space. Traverse each initial solution and evaluate the quality of each solution according to evaluation criteria such as a cost function or fitness function. Obtain the control quality coefficient of each initial solution. Select the optimal initial solution according to the control quality coefficient. Configure a random perturbation distance threshold. With the optimal initial solution as the center, determine a target search area in the high-dimensional solution space in combination with the perturbation distance threshold. Select a certain number of inferior solutions, that is, solutions with poor quality, according to the initial control quality coefficient. Locate these inferior solutions in the high-dimensional solution space as the starting positions of the search. Starting from the starting positions, perform a traveling search for a certain number of times at random positions within the target search area. In this way, new solutions are obtained, called the expanded solution set. Repeat the above processes of control quality evaluation, selection of the optimal solution, location of the target search area, selection of inferior solutions, and search. In each iteration, try to find a better solution until an end optimal solution is reached. If the control quality coefficient of the end optimal solution meets the preset control quality coefficient threshold, then take this end optimal solution as the optimization result of the heat treatment process. If not, repeat the entire optimization process starting from step 1.

[0034] In summary, a method for optimizing the heat treatment process of a hardware workpiece provided by this application has the following technical effects: By obtaining the heat treatment request information of the hardware workpiece, where the heat treatment request information of the hardware workpiece includes the component ratio of the hardware workpiece and the desired performance of the hardware workpiece; communicating with the heat treatment equipment to obtain the initial heat treatment control parameters, where the initial heat treatment control parameters include the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information; performing a control quality assessment on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information according to the component ratio of the hardware workpiece and the desired performance of the hardware workpiece to obtain the first control quality coefficient; when the first control quality coefficient does not meet the control quality coefficient threshold, optimizing the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain the heat treatment process optimization result; performing heat treatment control on the heat treatment equipment according to the heat treatment process optimization result, effectively solving the problem that the prior art cannot adjust the heat treatment parameters according to the composition and required performance of the hardware workpiece, cannot identify the processing parameters that are not suitable for the current hardware workpiece, and cannot adjust the heat treatment parameters in time, realizing the precise control of the heat treatment equipment, thereby improving the quality and performance of the hardware workpiece, enhancing the product quality, reducing the scrap rate, and improving the production efficiency.

[0035] Embodiment 2 Based on a heat treatment process optimization method for a hardware workpiece in the foregoing embodiment and with the same inventive concept, the present application also provides a heat treatment process optimization system for a hardware workpiece. Please refer to the appendix Figure 2 , the system includes: A request information acquisition module 11, which is used to obtain the heat treatment request information of the hardware workpiece, where the heat treatment request information of the hardware workpiece includes the component ratio of the hardware workpiece and the desired performance of the hardware workpiece; A control parameter acquisition module 12, which is used to communicate with the heat treatment equipment to obtain the initial heat treatment control parameters, where the initial heat treatment control parameters include the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information; A first control quality coefficient acquisition module 13, which is used to perform a control quality assessment on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information according to the component ratio of the hardware workpiece and the desired performance of the hardware workpiece to obtain the first control quality coefficient; A process optimization result acquisition module 14, which is used to optimize the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information when the first control quality coefficient does not meet the control quality coefficient threshold, so as to obtain the heat treatment process optimization result; A heat treatment control module 15, which is used to perform heat treatment control on the heat treatment equipment according to the heat treatment process optimization result.

[0036] Furthermore, the first control quality coefficient acquisition module 13 in the system is also used for: Taking the component ratio of the hardware workpiece and the expected performance of the hardware workpiece as constraints, performing shared log matching in a preset time zone through the heat treatment shared blockchain to obtain a shared log matching result; Taking the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information as a reference, traversing the shared log matching result for adjacent sorting to obtain an adjacent log sorting result; Calculating the ratio of the first log record quantity of the adjacent log sorting result to the second log record quantity of the shared log matching result, and setting it as the first quality evaluation factor; Traversing the adjacent log sorting result, and performing performance deviation analysis in combination with the expected performance of the hardware workpiece to obtain a set of performance deviation indexes; Performing quality analysis on the set of performance deviation indexes to obtain a second quality evaluation factor; Adding the first quality evaluation factor and the second quality evaluation factor into the first control quality coefficient.

[0037] Furthermore, the system further includes a log sorting result acquisition module, which is used for: Extracting a first shared log according to the shared log matching result, where the first shared log includes first temperature rise timing information, first peak temperature information, first heat preservation duration information, and first temperature drop timing information; Based on the first temperature rise timing information, the first peak temperature information, the first heat preservation duration information, and the first temperature drop timing information, performing distance analysis based on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain an adjacent sorting distance; When the adjacent sorting distance is less than or equal to the adjacent sorting distance threshold, adding the first shared log into the adjacent log sorting result.

[0038] Further, the system further includes a proximity distance acquisition module, and the proximity distance acquisition module is configured to: Construct a first temperature rise curve in a first coordinate system according to the first temperature rise timing information, and construct a second temperature rise curve in the first coordinate system according to the initial temperature rise timing information; Connect the head of the second temperature rise curve and the first temperature rise curve, connect the tail of the second temperature rise curve and the first temperature rise curve, obtain the area of the enclosed figure and perform normalization processing, and set it as the first proximity evaluation factor, where the first proximity evaluation factor has a first weight; Calculate the deviation between the initial peak temperature information and the first peak temperature information and perform normalization processing to obtain a first eigenvalue, calculate the deviation between the initial heat preservation duration information and the first heat preservation duration information and perform normalization processing to obtain a second eigenvalue; Calculate the product of the first eigenvalue and the second eigenvalue, and set it as the second proximity evaluation factor, where the second proximity evaluation factor has a second weight; Construct a first temperature drop curve in a first coordinate system according to the first temperature drop timing information, and construct a second temperature drop curve in the first coordinate system according to the initial temperature drop timing information; Connect the head of the second temperature drop curve and the first temperature drop curve, connect the tail of the second temperature drop curve and the first temperature drop curve, obtain the area of the enclosed figure and perform normalization processing, and set it as the third proximity evaluation factor, where the third proximity evaluation factor has a third weight; According to the first weight, the second weight, and the third weight, sum the first proximity evaluation factor, the second proximity evaluation factor, and the third proximity evaluation factor to obtain the proximity sorting distance.

[0039] Further, the system further includes a performance deviation index acquisition module, and the performance deviation index acquisition module is configured to: Extract a first proximity log according to the proximity log sorting result, where the first proximity log includes performance index recorded values; Traverse the performance index attributes and match the deviation distance threshold set; Compare the performance index recorded values with the expected performance of the hardware workpiece to obtain a deviation distance set; Count the proportion of the number of performance index attributes in the deviation distance set that is greater than or equal to the deviation distance threshold set, set it as the first performance deviation index, and add it to the performance deviation index set.

[0040] Further, the system further includes a second quality evaluation factor acquisition module, and the second quality evaluation factor acquisition module is used for: Perform an analysis of variance on the set of performance deviation indexes to obtain a performance deviation dispersion parameter; When the performance deviation dispersion parameter is greater than or equal to the dispersion parameter threshold, set the second quality evaluation factor to 0; When the performance deviation dispersion parameter is less than the dispersion parameter threshold, use 1 minus the mean of the set of performance deviation indexes, and set it as the second quality evaluation factor.

[0041] Further, the process optimization result acquisition module 14 in the system is further used for: Configure a temperature ramp rate constraint interval, a peak temperature constraint interval, a heat preservation duration constraint interval, and a temperature sink rate constraint interval to construct a high-dimensional solution space; Step 1: Randomly select the number of solution constraints based on the solution constraint number interval, and combine with the high-dimensional solution space to take values through a random distribution function to obtain a number of initial solutions; Step 2: Traverse the number of initial solutions to perform control quality evaluation to obtain a number of initial control quality coefficients; Step 3: Obtain the optimal initial solution of the number of initial solutions according to the number of initial control quality coefficients; Step 4: Configure a random perturbation distance threshold, and combine with the optimal initial solution to locate in the high-dimensional solution space to obtain a target search area; Step 5: Obtain a preset number of inferior solutions of the number of initial solutions according to the number of initial control quality coefficients; Step 6: Locate the preset number of inferior solutions in the high-dimensional solution space to obtain a starting position; Step 7: Starting from the starting position, perform a first preset number of traveling searches on random positions in the target search area to obtain an expanded solution set; Step 8: Repeat steps 2 to 7 for a second preset number of times to obtain an end optimal solution; When the control quality coefficient of the end optimal solution meets the control quality coefficient threshold, set the end optimal solution as the heat treatment process optimization result; When the control quality coefficient of the end optimal solution does not meet the control quality coefficient threshold, repeat steps 1 to 8.

[0042] The various embodiments in this specification are described in a progressive manner, and the key points of each embodiment are the differences from other embodiments. The foregoing Figure 1The optimization method and specific examples of the heat treatment process of a hardware workpiece in Embodiment 1 are equally applicable to the heat treatment process optimization system of a hardware workpiece in this embodiment. Through the detailed description of the optimization method of the heat treatment process of a hardware workpiece above, those skilled in the art can clearly know the heat treatment process optimization system of a hardware workpiece in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0043] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0044] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A method for optimizing the heat treatment process of a hardware workpiece, characterized in that, Including: Obtain heat treatment request information of the hardware workpiece, where the heat treatment request information of the hardware workpiece includes the composition ratio of the hardware workpiece and the desired performance of the hardware workpiece; Communicate with the heat treatment equipment to obtain initial heat treatment control parameters, where the initial heat treatment control parameters include initial temperature rise timing information, initial peak temperature information, initial heat preservation duration information, and initial temperature drop timing information; Based on the composition ratio of the hardware workpiece and the desired performance of the hardware workpiece, conduct a control quality assessment on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain a first control quality coefficient; When the first control quality coefficient does not meet the control quality coefficient threshold, optimize the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain a heat treatment process optimization result; Conduct heat treatment control on the heat treatment equipment according to the heat treatment process optimization result.

2. The method according to claim 1, wherein Based on the composition ratio of the hardware workpiece and the desired performance of the hardware workpiece, conduct a control quality assessment on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain a first control quality coefficient, including: With the composition ratio of the hardware workpiece and the desired performance of the hardware workpiece as constraints, perform shared log matching in a preset time zone through the heat treatment shared blockchain to obtain a shared log matching result; Based on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information, traverse the shared log matching result for proximity sorting to obtain a proximity log sorting result; Statistically calculate the ratio of the number of first log records in the proximity log sorting result to the number of second log records in the shared log matching result, and set it as the first quality evaluation factor; Traverse the proximity log sorting result and conduct performance deviation analysis in combination with the desired performance of the hardware workpiece to obtain a set of performance deviation indices; Perform quality analysis on the set of performance deviation indices to obtain a second quality evaluation factor; Add the first quality evaluation factor and the second quality evaluation factor to the first control quality coefficient.

3. The method according to claim 2, wherein Based on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information, traverse the shared log matching result for proximity sorting to obtain a proximity log sorting result, including: Extract a first shared log according to the shared log matching result, where the first shared log includes first temperature rise timing information, first peak temperature information, first heat preservation duration information, and first temperature drop timing information; Based on the first temperature rise timing information, the first peak temperature information, the first heat preservation duration information, and the first temperature drop timing information, perform distance analysis based on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain the proximity sorting distance; When the proximity sorting distance is less than or equal to the proximity sorting distance threshold, add the first shared log to the proximity log sorting result.

4. The method according to claim 3, wherein Based on the first temperature rise timing information, the first peak temperature information, the first heat preservation duration information, and the first temperature drop timing information, perform distance analysis based on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information to obtain the proximity sorting distance, including: Construct a first temperature rise curve in the first coordinate system according to the first temperature rise timing information, and construct a second temperature rise curve in the first coordinate system according to the initial temperature rise timing information; Connect the head of the second temperature rise curve and the first temperature rise curve, and connect the tail of the second temperature rise curve and the first temperature rise curve, obtain the area of the enclosed figure and perform normalization processing, and set it as the first proximity evaluation factor, where the first proximity evaluation factor has a first weight; Calculate the deviation between the initial peak temperature information and the first peak temperature information and perform normalization processing to obtain a first eigenvalue, calculate the deviation between the initial heat preservation duration information and the first heat preservation duration information and perform normalization processing to obtain a second eigenvalue; Calculate the product of the first eigenvalue and the second eigenvalue, and set it as the second proximity evaluation factor, where the second proximity evaluation factor has a second weight; Construct a first temperature drop curve in the first coordinate system according to the first temperature drop timing information, and construct a second temperature drop curve in the first coordinate system according to the initial temperature drop timing information; Connect the head of the second temperature drop curve and the first temperature drop curve, and connect the tail of the second temperature drop curve and the first temperature drop curve, obtain the area of the enclosed figure and perform normalization processing, and set it as the third proximity evaluation factor, where the third proximity evaluation factor has a third weight; According to the first weight, the second weight, and the third weight, sum the first proximity evaluation factor, the second proximity evaluation factor, and the third proximity evaluation factor to obtain the proximity sorting distance.

5. The method according to claim 2, characterized in that, Traverse the proximity log sorting result, and combine with the expected performance of the hardware workpiece to perform performance deviation analysis to obtain a set of performance deviation indexes, including: Extract the first proximity log according to the proximity log sorting result, where the first proximity log includes performance index recorded values; Traverse the performance index attributes and match the deviation distance threshold set; Compare the performance index recorded values with the expected performance of the hardware workpiece to obtain a set of deviation distances; Count the proportion of the number of performance index attributes in the deviation distance set that is greater than or equal to the deviation distance threshold set, which is set as the first performance deviation index, and add it to the performance deviation index set.

6. The method according to claim 2, wherein Perform quality analysis on the performance deviation index set to obtain a second quality evaluation factor, including: Perform variance analysis on the performance deviation index set to obtain a performance deviation discrete parameter; When the performance deviation discrete parameter is greater than or equal to the discrete parameter threshold, set the second quality evaluation factor to 0; When the performance deviation discrete parameter is less than the discrete parameter threshold, use 1 minus the mean of the performance deviation index set, which is set as the second quality evaluation factor.

7. The method according to claim 1, characterized in that When the first control quality coefficient does not meet the control quality coefficient threshold, optimize the initial temperature rise time series information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop time series information to obtain a heat treatment process optimization result, including: Configure a temperature rise rate constraint interval, a peak temperature constraint interval, a heat preservation duration constraint interval, and a temperature drop rate constraint interval to construct a high-dimensional solution space; Step 1: Randomly select the number of solution constraints based on the solution constraint number interval, and combine with the high-dimensional solution space to obtain several initial solutions through a random distribution function; Step 2: Traverse the several initial solutions for control quality evaluation to obtain several initial control quality coefficients; Step 3: Obtain the optimal initial solution of the several initial solutions according to the several initial control quality coefficients; Step 4: Configure a random perturbation distance threshold, and combine with the optimal initial solution to locate in the high-dimensional solution space to obtain a target search area; Step 5: Obtain a preset number of inferior solutions of the several initial solutions according to the several initial control quality coefficients; Step 6: Locate the preset number of inferior solutions in the high-dimensional solution space to obtain a starting position; Step 7: Starting from the starting position, perform a first preset number of traveling searches on the random positions in the target search area to obtain an expanded solution set; Step 8: Repeat steps 2 to 7 for a second preset number of times to obtain an end optimal solution; When the control quality coefficient of the end optimal solution meets the control quality coefficient threshold, set the end optimal solution as the heat treatment process optimization result; When the control quality coefficient of the end optimal solution does not meet the control quality coefficient threshold, repeat steps 1 to 8.

8. A heat treatment process optimization system for hardware workpieces, characterized in that, For implementing the steps of the method according to any one of claims 1 to 7, the system includes: A request information acquisition module, which is used to obtain heat treatment request information of a hardware workpiece, wherein the heat treatment request information of the hardware workpiece includes the composition ratio of the hardware workpiece and the expected performance of the hardware workpiece; A control parameter acquisition module, which is used to communicate with a heat treatment device to obtain initial heat treatment control parameters, wherein the initial heat treatment control parameters include initial temperature rise time series information, initial peak temperature information, initial heat preservation duration information, and initial temperature drop time series information; The first control quality coefficient acquisition module is configured to perform a control quality assessment on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information according to the composition ratio of the hardware workpiece and the desired performance of the hardware workpiece, so as to obtain a first control quality coefficient; The process optimization result acquisition module is configured to perform optimization on the initial temperature rise timing information, the initial peak temperature information, the initial heat preservation duration information, and the initial temperature drop timing information when the first control quality coefficient does not meet the control quality coefficient threshold, so as to obtain a heat treatment process optimization result; The heat treatment control module is configured to perform heat treatment control on the heat treatment equipment according to the heat treatment process optimization result.

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