Workpiece production scheduling method and system based on tool optimization

Abstract

The invention provides a workpiece production scheduling method and system based on tool optimization. The method comprises the following steps that the number of types of workpieces to be machined, tool demand data corresponding to the workpieces and the available capacity of a machine tool magazine are obtained; the use frequency of various tools is calculated according to the tool demand data corresponding to all the workpieces, a high-frequency use tool is preferentially configured to a machine tool magazine, and the high-frequency use tool is the tool with the demand frequency exceeding a preset threshold value in all the to-be-machined workpieces; the number of types of preferentially configured tools is removed from the tool demand data corresponding to all the workpieces, and the number of types of remaining tools is counted; and according to a comparison result of the number of types of the remaining tools and a preset value, different optimization methods are selected in combination with machine tool magazine available capacity constraints, and a final workpiece machining production scheduling strategy is obtained. According to the method, on the premise that the machining requirement is guaranteed, the tool use frequency, the machine tool tool magazine capacity constraint and the remaining tool scheduling requirement are comprehensively considered, the production scheduling scheme with the minimum tool changing frequency is obtained, and the production efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of optimization scheduling technology for CNC machine tool processing production lines, specifically to a workpiece scheduling method and system based on tool optimization. Background Technology

[0002] In CNC machine tool processing, when multiple types of workpieces need to be processed within a specific timeframe, different workpieces require different types of cutting tools. Traditional workpiece scheduling methods are typically optimized based on workpiece process requirements or equipment utilization, with less consideration given to tool management optimization. This leads to: (1) Too many tool changes increase non-production time (such as tool changes, tool inspection, tool feeding, etc.).

[0003] (2) Low tool utilization rate. Frequently used tools were not properly prioritized for placement in the tool magazine, resulting in frequent tool changes.

[0004] (3) When there are many types of workpieces and complex types of cutting tools, it is difficult to quickly find a reasonable production sequence.

[0005] Currently, some intelligent scheduling systems use heuristic search or rule-based scheduling, but lack comprehensive optimization strategies for tool usage frequency, tool type and quantity, tool magazine capacity, and machining sequence. Summary of the Invention

[0006] This application provides a workpiece scheduling method and system based on tool optimization, which can solve the technical problems in the prior art, such as the high number of tool changes, low tool utilization, difficulty in quickly determining a reasonable scheduling sequence, and lack of comprehensive optimization strategies in intelligent scheduling systems, due to the fact that traditional scheduling methods rarely consider tool management optimization.

[0007] In a first aspect, this application provides a workpiece scheduling method based on tool optimization, comprising the following steps: Obtain the number of types of workpieces to be processed, the tool requirements for each workpiece, and the available capacity of the machine tool magazine; If the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, the usage frequency of each type of tool is calculated based on the tool requirement data corresponding to each workpiece, and high-frequency tools are prioritized for allocation to the machine tool magazine. The high-frequency tools are those whose demand exceeds a preset threshold among all workpieces to be processed. Remove the number of preferred tool types from the tool requirement data for each workpiece, and count the remaining tool types. Based on the comparison between the number of remaining tool types and the preset value, and combined with the available capacity constraints of the machine tool magazine, different optimization methods are selected to obtain the final workpiece machining scheduling strategy.

[0008] Furthermore, the acquisition of the number of types of workpieces to be processed, the tool requirements data corresponding to each workpiece, and the available capacity of the machine tool magazine specifically includes the following steps: Obtain the number of types of workpieces to be processed; Obtain the types and quantities of cutting tools required for each type of workpiece to be processed, as well as the sum of the types and quantities of cutting tools required for all workpieces to be processed; Check the available capacity of the machine tool's tool magazine.

[0009] Furthermore, if the available capacity of the machine tool's tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, the usage frequency of each type of tool is calculated based on the tool requirement data corresponding to each workpiece, and high-frequency used tools are preferentially configured into the machine tool's tool magazine. The high-frequency used tools are those whose demand exceeds a preset threshold among all workpieces to be processed. Specifically, this includes the following steps: If the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, iterate through the tool requirement list of all workpieces and count the frequency of each tool in the overall processing task. Tools whose usage frequency exceeds a frequency threshold are marked as high-frequency tools; Prioritize loading high-frequency cutting tools into the fixed tool positions of the machine tool's tool magazine.

[0010] Furthermore, the step of removing the number of preferred tool types from the tool requirement data corresponding to each workpiece and counting the remaining tool types specifically includes the following steps: Construct a tooling requirement set for all workpieces; Remove the configured high-frequency tool records from the tool requirement set of all workpieces; Calculate the number of remaining tool types that need dynamic management, and obtain the remaining tool types.

[0011] Furthermore, the step of selecting different optimization methods based on the comparison results of the remaining number of tool types with the preset value, combined with the machine tool magazine's available capacity constraints, to obtain the final workpiece machining scheduling strategy specifically includes the following steps: When the number of remaining tool types does not exceed the first critical value, an exhaustive search method is used to generate all workpiece scheduling sequence combinations that satisfy the tool magazine capacity constraint, and the workpiece scheduling scheme with the fewest tool changes is selected from them. When the number of remaining tool types is between the first and second critical values, the state transition optimization method is used in combination with the machine tool magazine available capacity constraint, and the optimal production sequence is calculated by recursive formula. When the number of remaining tool types exceeds the second critical value, a probabilistic convergence method is adopted. This method generates neighborhood solutions that satisfy capacity constraints and uses a penalty function combined with temperature parameters to control the search process, thereby obtaining an approximate optimal solution.

[0012] Furthermore, the implementation process of the exhaustive search method includes: An exhaustive search method is used to generate all possible workpiece scheduling sequence combinations that satisfy the tool magazine capacity constraint; For each possible combination of workpiece production sequence, simulate the tool loading process and obtain the corresponding tool loading scheme. Calculate the total number of tool changes for each tool loading scheme; The optimal solution is the tool loading scheme that minimizes the number of tool changes.

[0013] Furthermore, the implementation process of the state transition optimization method includes: Establish a mapping relationship between tool magazine status and unprocessed workpieces; Define the cost function for state transitions based on the mapping relationship; The minimum number of tool changes under each state is solved step by step using a recursive formula; The optimal workpiece scheduling plan is obtained by reverse tracing the minimum cost path based on the recursive results.

[0014] Furthermore, the implementation process of the probabilistic convergence method includes: Initialize the random processing order as the current solution; Generate a new solution based on the current solution through neighborhood operations; Calculate the acceptance probability based on the change in the objective function of the current solution and the new solution; By dynamically adjusting the acceptance probability threshold using temperature parameters, and through a simulated annealing mechanism, a gradual convergence is achieved from high-temperature wide-area exploration to low-temperature local optimization, ultimately outputting the optimal workpiece scheduling scheme.

[0015] Secondly, this application provides a workpiece scheduling system based on tool optimization, comprising: The data acquisition module is used to obtain the number of types of workpieces to be processed, the tool requirements for each workpiece, and the available capacity of the machine tool magazine. A high-frequency tool priority configuration module is communicatively connected to the data acquisition module. It is used to calculate the usage frequency of each type of tool based on the tool requirement data corresponding to each workpiece if the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed. The high-frequency tool is the tool whose demand exceeds a preset threshold among all workpieces to be processed. The remaining tool statistics module is communicatively connected to the data acquisition module and the high-frequency tool priority configuration module. It is used to remove the number of types of priority configuration tools from the tool requirement data corresponding to each workpiece and count the number of remaining tool types. The production scheduling strategy acquisition module is communicatively connected to the data acquisition module and the remaining tool statistics module. It is used to select different optimization methods based on the comparison results of the number of remaining tool types with preset values ​​and the available capacity constraints of the machine tool magazine, and to obtain the final workpiece machining production scheduling strategy.

[0016] Furthermore, the production scheduling strategy acquisition module includes: The first production scheduling strategy acquisition unit is communicatively connected to the remaining tool statistics module. When the number of remaining tool types does not exceed the first critical value, it uses an exhaustive search method to generate all workpiece production scheduling sequence combinations that satisfy the tool magazine capacity constraint, and selects the workpiece production scheduling scheme with the fewest tool changes from them. The second production scheduling strategy acquisition unit is connected to the remaining tool statistics module. When the number of remaining tool types is between the first and second critical values, the optimal production scheduling order is calculated by using a recursive formula in combination with the state transition optimization method and the available capacity constraint of the machine tool magazine. The third production scheduling strategy acquisition unit is connected in communication with the remaining tool statistics module. When the number of remaining tool types exceeds the second critical value, it adopts the probabilistic convergence method, which generates neighborhood solutions that satisfy the capacity constraints and combines the penalty function with temperature parameters to control the search process and obtain an approximate optimal solution.

[0017] The beneficial effects of the technical solutions provided in this application include at least the following: By prioritizing the allocation of frequently used tools to the machine tool magazine, the number of tool changes and tool wear is significantly reduced. At the same time, the optimization method is dynamically selected based on the number of remaining tool types, taking into account the adaptability of both simple and complex production scheduling scenarios. Combined with the tool magazine capacity constraints, the tool resources are accurately allocated. Finally, through the hierarchical optimization strategy, the production cycle and cost are reduced while ensuring the continuity of processing. It is especially suitable for flexible production lines with multiple types of workpieces. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the workpiece scheduling method based on tool optimization provided in this application embodiment; Figure 2 The overall flowchart of the workpiece scheduling method based on tool optimization provided in the embodiments of this application is shown below; Figure 3 The workpiece processing sequence optimization flowchart provided in this application embodiment; Figure 4 This application provides a process for calculating the number of tool changes in workpiece scheduling; Figure 5 This is a flowchart illustrating the workpiece scheduling method based on tool optimization proposed in this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0020] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0021] In the description of the embodiments in this application, terms such as "exemplary," "for example," or "for instance" are used as examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0023] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0025] Firstly, such as Figure 1 and Figure 2 This application provides a workpiece scheduling method based on tool optimization, including the following steps: Step S1: Obtain the number of types of workpieces to be processed, the tool requirements for each workpiece, and the available capacity of the machine tool magazine; Step S2: If the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, calculate the usage frequency of each type of tool based on the tool requirement data corresponding to each workpiece, and prioritize the allocation of high-frequency used tools to the machine tool magazine. The high-frequency used tools are those whose demand exceeds a preset threshold among all workpieces to be processed. Step S3: Remove the number of preferred tool types from the tool requirement data corresponding to each workpiece, and count the number of remaining tool types; Step S4: Based on the comparison results of the remaining number of tool types with the preset value, and combined with the available capacity constraints of the machine tool magazine, select different optimization methods to obtain the final workpiece machining scheduling strategy.

[0026] This application significantly reduces tool changes and tool wear by prioritizing the allocation of frequently used tools to the machine tool magazine. At the same time, it dynamically selects optimization methods based on the number of remaining tool types, taking into account both simple and complex production scheduling scenarios. Combined with the constraints of tool magazine capacity, it achieves precise allocation of tool resources. Finally, through a hierarchical optimization strategy, it reduces production cycle and cost while ensuring processing continuity, making it particularly suitable for flexible production lines with multiple types of workpieces.

[0027] In one embodiment, step S1, obtaining the number of types of workpieces to be processed, the tool requirement data corresponding to each workpiece, and the available capacity of the machine tool magazine, specifically includes the following steps: Obtain the number N of types of workpieces to be processed; Obtain the types and quantities of cutting tools required for various workpieces to be processed. and the sum of the types and quantities of cutting tools required for all workpieces to be processed. , for The sum of For the first The number of cutting tools of each type to be processed; Test the available capacity of the machine tool tool magazine Also known as the total capacity of the machine tool magazine.

[0028] In one embodiment, such as Figure 3 As shown, if the machine tool magazine has an available capacity Not less than the sum of the types and quantities of cutting tools required for all workpieces to be processed If so, the priority configuration of high-frequency tools in step S2 and the production order optimization of the remaining tool types in step S3 will not be performed.

[0029] In one embodiment, step S2: if the machine tool magazine has available capacity Less than the sum of the types and quantities of tools required for all workpieces to be machined Based on the tool requirement data corresponding to each workpiece, the usage frequency of various tools is calculated, and high-frequency tools are prioritized for allocation to the machine tool magazine. The high-frequency tools are those whose demand exceeds a preset threshold among all workpieces to be processed. The specific steps include: If the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, iterate through the tool requirement list of all workpieces and count the frequency of each tool in the overall processing task. The tag is used more frequently than the frequency threshold. The cutting tools are marked as high-frequency cutting tools; Prioritize loading high-frequency tools into the fixed tool positions of the machine tool's tool magazine to reduce the computational workload of subsequent tool change optimization.

[0030] In one embodiment, step S3: removing the number of preferred tool types from the tool requirement data corresponding to each workpiece and counting the remaining tool types specifically includes the following steps: Construct a tooling requirement set for all workpieces; Remove the configured high-frequency tool records from the tool requirement set of all workpieces; Calculate the number of remaining tool types that require dynamic management, and obtain the remaining tool type count. .

[0031] In one embodiment, step S4: Based on the comparison result of the remaining number of tool types with the preset value, and combined with the machine tool magazine's available capacity constraint, different optimization methods are selected to obtain the final workpiece machining scheduling strategy, specifically including the following steps: Step S4A: When the number of remaining tool types Not exceeding the first critical value At that time, an exhaustive search method is used to generate all workpiece scheduling sequence combinations that satisfy the tool magazine capacity constraint, and the workpiece scheduling scheme with the fewest tool changes is selected from them. Step S4B: When the number of remaining tool types Between the first critical value Second critical value During this period, the state transition optimization method is used to calculate the optimal tool scheduling scheme where the tool union does not exceed the tool magazine capacity using a recursive formula; Step S4C: When the number of remaining tool types Exceeding the second critical value At that time, a probabilistic convergence method is adopted, which obtains an approximate optimal solution by generating a neighborhood solution that satisfies the capacity constraint and using a penalty function combined with temperature parameters to control the search process.

[0032] This embodiment uses a phased dynamic selection optimization method, combined with tool magazine capacity constraints, to significantly reduce the number of tool changes and improve production scheduling efficiency while ensuring machining feasibility.

[0033] In the embodiments of this application, such as Figure 3 As shown, step S4 can also be transformed into selecting different optimization methods based on the comparison result of the number of types of workpieces to be processed and the preset value, combined with the available capacity constraint of the machine tool magazine, to obtain the final workpiece processing scheduling strategy. Then, the subsequent steps S4A, S4B, and S4C are executed, replacing the number of remaining tool types with the number of types of workpieces to be processed.

[0034] In one embodiment, the exhaustive search method in step S4A includes the following steps: An exhaustive search method is used to generate all possible workpiece scheduling sequence combinations that satisfy the tool magazine capacity constraint; For each possible combination of workpiece production sequence, simulate the tool loading process and obtain the corresponding tool loading scheme. Calculate the total number of tool changes for each tool loading scheme; The optimal solution is the tool loading scheme that minimizes the number of tool changes.

[0035] The exhaustive search method provided in this embodiment is suitable for situations with a small number of workpiece types. By traversing all possible workpiece processing sequences, calculating the number of tool changes, and selecting the optimal solution, it is suitable for small-scale workpiece scheduling and ensures that the global optimal solution is found.

[0036] In one specific embodiment, step S4A is specifically implemented as follows: (1) Generate all possible workpiece scheduling sequences that satisfy the tool magazine capacity constraint. Arrangement.

[0037] (2) Calculate the number of tool changes for each workpiece production sequence, detailed below: Given a production schedule for a certain type of workpiece, obtain the current tool magazine status of the machine tool, load the required tools for the workpiece into the tool magazine, and calculate the minimum number of tool changes. The calculation method is as follows: 1) Let the set of tool types currently loaded in the tool magazine be . The set of all tool types required for machining workpiece k is as follows: .

[0038] 2) Prioritize retaining the cutting tools needed for machining the workpiece k. (Current tool magazine prioritizes retention) (Required knives); 3) Calculate the set of new tool types that need to be added. and number of idle tools : a. record This refers to the set of tool types required for machining the workpiece k, but which are currently loaded in the tool magazine. Knives not found in the text; b. Calculate the number of remaining tool positions in the tool magazine, and record... ; c. When In this case, simply add a new tool without changing the tool; d. such as Figure 4 As shown, when In such cases, some of the cutting tools must be replaced; 4) If a tool change is required, the tool change strategy shall be executed according to the following rules, as follows: a. Calculate the current tool magazine These are the kinds of knives that will still be used in the future.

[0039] b. Prioritize replacing tools that are no longer in use.

[0040] c. If all tools will be used in the future, replace the least frequently used tool based on global statistics.

[0041] d. If there are multiple tools with the lowest frequency of use, replace the last tool used according to the workpiece scheduling; 5) Following the steps 1) to 4) above, calculate the minimum number of tool changes for the workpiece under the production schedule.

[0042] 6) Calculate the minimum number of tool changes for each type of workpiece under the production schedule.

[0043] (3) Output the workpiece scheduling scheme with the fewest total tool changes.

[0044] In one embodiment, the implementation process of the state transition optimization method in step S4B includes: Establish a mapping relationship between tool magazine status and unprocessed workpieces; Define the cost function for state transitions based on the mapping relationship; The minimum number of tool changes under each state is solved step by step using a recursive formula; The optimal workpiece scheduling plan is obtained by reverse tracing the minimum cost path based on the recursive results.

[0045] The dynamic programming method provided in this embodiment is applicable to medium-sized tool scheduling problems. It reduces the amount of computation by using a state transition method, is suitable for medium-sized workpiece scheduling, and improves computational efficiency.

[0046] In one embodiment, step S4B is specifically implemented as follows: (1) Define the state transition function as follows: : in, A collection of unprocessed workpieces. This refers to the set of tool types currently loaded in the tool magazine. In order to be in Complete the remaining unprocessed workpiece in the state The minimum number of tool changes.

[0047] (2) State transition, recursive calculation Solve step by step to find the optimal processing sequence: exist Select an unprocessed workpiece Perform machining and update the tool magazine status to... The collection of unprocessed workpieces has been updated to... The tool magazine arrive The number of tool changes is cost( , The dynamic programming recurrence formula is:

[0048] in, The tool magazine status is At that time, the remaining workpieces are processed. The minimum number of tool changes.

[0049] In one embodiment, the implementation process of the probabilistic convergence method in step S4C includes: Initialize the random processing order as the current solution; Generate a new solution based on the current solution through neighborhood operations; Calculate the acceptance probability based on the change in the objective function of the current solution and the new solution; By dynamically adjusting the acceptance probability threshold using temperature parameters, and through a simulated annealing mechanism, a gradual convergence is achieved from high-temperature wide-area exploration to low-temperature local optimization, ultimately outputting the optimal workpiece scheduling scheme.

[0050] The simulated annealing algorithm provided in this embodiment is suitable for situations with a large number of tool types and an excessively large search space. It uses a probabilistic acceptance criterion to find an approximate optimal solution.

[0051] In one embodiment, step S4C is specifically implemented as follows: (1) Initialize parameters: Set the initial workpiece sequence ,in Representing the Workpiece; temperature and cooling coefficient ; Objective function:

[0052] in Representative workpiece arrive Number of tool changes required; (2) Iterative search: 1) Based on the current production schedule, randomly generate a new production order. (e.g., swapping the order of two workpieces or reversing a random subsequence).

[0053] 2) Calculate the number of tool changes for the new workpiece sequence. ; 3) Calculate the change in the number of tool changes ; 4) Calculate the probability of accepting the new solution. : if This indicates that the new process is superior. ; if This indicates that the new process is worse. The new solution is accepted based on the probability of the P-value.

[0054] (3) Cooling down: gradually reduce the temperature This continues until the solution converges to the optimal solution.

[0055] Step 4: Output the optimized production scheduling plan Determine the final workpiece processing sequence.

[0056] Output tool changing optimization strategies and apply them to actual production scheduling.

[0057] Secondly, such as Figure 5As shown, this application provides a workpiece scheduling system based on tool optimization, including a data acquisition module 100, a high-frequency tool priority configuration module 200, a remaining tool statistics module 300, and a scheduling strategy acquisition module 400. The data acquisition module 100 is used to acquire the number of types of workpieces to be processed, the tool requirement data corresponding to each workpiece, and the available capacity of the machine tool magazine. The high-frequency tool priority configuration module 200 is communicatively connected to the data acquisition module 100 and is used to calculate the usage frequency of each type of tool based on the tool requirement data corresponding to each workpiece if the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, and to prioritize the configuration of high-frequency tools. The high-frequency used tools in the machine tool magazine are those whose demand exceeds a preset threshold among all workpieces to be processed. The remaining tool statistics module 300 is communicatively connected to the data acquisition module 100 and the high-frequency tool priority configuration module 200, and is used to remove the number of priority configuration tool types from the tool demand data corresponding to each workpiece to count the number of remaining tool types. The scheduling strategy acquisition module 400 is communicatively connected to the data acquisition module 100 and the remaining tool statistics module 300, and is used to select different optimization methods based on the comparison result of the number of remaining tool types with the preset value, combined with the available capacity constraint of the machine tool magazine, to obtain the final workpiece processing scheduling strategy.

[0058] In one embodiment, the production scheduling strategy acquisition module includes: The first production scheduling strategy acquisition unit is communicatively connected to the remaining tool statistics module. When the number of remaining tool types does not exceed the first critical value, it uses an exhaustive search method to generate all workpiece production scheduling sequence combinations that satisfy the tool magazine capacity constraint, and selects the workpiece production scheduling scheme with the fewest tool changes from them. The second production scheduling strategy acquisition unit is connected in communication with the remaining tool statistics module. When the number of remaining tool types is between the first critical value and the second critical value, the state transition optimization method is used to calculate the optimal tool scheduling scheme in which the union of tools does not exceed the tool magazine capacity through the recursive formula. The third production scheduling strategy acquisition unit is connected in communication with the remaining tool statistics module. When the number of remaining tool types exceeds the second critical value, it adopts the probabilistic convergence method, which generates neighborhood solutions that satisfy the capacity constraints and combines the penalty function with temperature parameters to control the search process and obtain an approximate optimal solution.

[0059] The functions of each module in the tool-optimized workpiece scheduling system described above correspond to the steps in the tool-optimized workpiece scheduling method embodiment described above, and their functions and implementation processes will not be described in detail here.

[0060] Thirdly, embodiments of this application provide a workpiece scheduling device based on tool optimization. The workpiece scheduling device based on tool optimization can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0061] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the tool-optimized workpiece scheduling equipment, as well as interfaces used for interconnecting the tool-optimized workpiece scheduling equipment with other devices (such as other computing devices or user devices). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user devices can be displays, keyboards, etc.

[0062] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0063] The processor can be a general-purpose processor, which can call a tool-optimized workpiece scheduling program stored in memory and execute the tool-optimized workpiece scheduling method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the tool-optimized workpiece scheduling program is called can be referred to in the various embodiments of the tool-optimized workpiece scheduling method of this application, and will not be repeated here.

[0064] Fourthly, embodiments of this application also provide a readable storage medium.

[0065] The present application has a readable storage medium storing a tool-optimized workpiece scheduling program, wherein when the tool-optimized workpiece scheduling program is executed by a processor, it implements the steps of the tool-optimized workpiece scheduling method described above.

[0066] The method implemented when the tool-optimized workpiece scheduling program is executed can be referred to in the various embodiments of the tool-optimized workpiece scheduling method of this application, and will not be repeated here.

[0067] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0068] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0069] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A workpiece scheduling method based on tool optimization, characterized in that, Includes the following steps: Obtain the number of types of workpieces to be processed, the tool requirements for each workpiece, and the available capacity of the machine tool magazine; If the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, the usage frequency of each type of tool is calculated based on the tool requirement data corresponding to each workpiece, and high-frequency tools are prioritized for allocation to the machine tool magazine. The high-frequency tools are those whose demand exceeds a preset threshold among all workpieces to be processed. Remove the number of preferred tool types from the tool requirement data for each workpiece, and count the remaining tool types. Based on the comparison between the number of remaining tool types and the preset value, and combined with the available capacity constraints of the machine tool magazine, different optimization methods are selected to obtain the final workpiece machining scheduling strategy.

2. The workpiece scheduling method based on tool optimization as described in claim 1, characterized in that, The process of obtaining the number of types of workpieces to be processed, the tool requirements for each workpiece, and the available capacity of the machine tool magazine specifically includes the following steps: Obtain the number of types of workpieces to be processed; Obtain the types and quantities of cutting tools required for each type of workpiece to be processed, as well as the sum of the types and quantities of cutting tools required for all workpieces to be processed; Check the available capacity of the machine tool's tool magazine.

3. The workpiece scheduling method based on tool optimization as described in claim 1, characterized in that, If the available capacity of the machine tool's tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, the usage frequency of each type of tool is calculated based on the tool requirement data corresponding to each workpiece. High-frequency used tools are prioritized for allocation to the machine tool's tool magazine. These high-frequency used tools are those whose required frequency exceeds a preset threshold among all workpieces to be processed. This process specifically includes the following steps: If the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed, iterate through the tool requirement list of all workpieces and count the frequency of each tool in the overall processing task. Tools whose usage frequency exceeds a frequency threshold are marked as high-frequency tools; Prioritize loading high-frequency cutting tools into the fixed tool positions of the machine tool's tool magazine.

4. The workpiece scheduling method based on tool optimization as described in claim 1, characterized in that, The process of removing the number of preferred tool types from the tool requirement data corresponding to each workpiece and counting the remaining tool types specifically includes the following steps: Construct a tooling requirement set for all workpieces; Remove the configured high-frequency tool records from the tool requirement set of all workpieces; Calculate the number of remaining tool types that need dynamic management, and obtain the remaining tool types.

5. The workpiece scheduling method based on tool optimization as described in claim 1, characterized in that, Based on the comparison between the number of remaining tool types and the preset value, and combined with the machine tool magazine's available capacity constraints, different optimization methods are selected to obtain the final workpiece machining scheduling strategy. This specifically includes the following steps: When the number of remaining tool types does not exceed the first critical value, an exhaustive search method is used to generate all workpiece scheduling sequence combinations that satisfy the tool magazine capacity constraint, and the workpiece scheduling scheme with the fewest tool changes is selected from them. When the number of remaining tool types is between the first and second critical values, the state transition optimization method is used in combination with the machine tool magazine available capacity constraint, and the optimal production sequence is calculated by recursive formula. When the number of remaining tool types exceeds the second critical value, a probabilistic convergence method is adopted. This method generates neighborhood solutions that satisfy capacity constraints and uses a penalty function combined with temperature parameters to control the search process, thereby obtaining an approximate optimal solution.

6. The workpiece scheduling method based on tool optimization as described in claim 5, characterized in that, The implementation process of the exhaustive search method includes: An exhaustive search method is used to generate all possible workpiece scheduling sequence combinations that satisfy the tool magazine capacity constraint; For each possible combination of workpiece production sequence, simulate the tool loading process and obtain the corresponding tool loading scheme. Calculate the total number of tool changes for each tool loading scheme; The optimal solution is the tool loading scheme that minimizes the number of tool changes.

7. The workpiece scheduling method based on tool optimization as described in claim 5, characterized in that, The implementation process of the state transition optimization method includes: Establish a mapping relationship between tool magazine status and unprocessed workpieces; Define the cost function for state transitions based on the mapping relationship; The minimum number of tool changes under each state is solved step by step using a recursive formula; The optimal workpiece scheduling plan is obtained by reverse tracing the minimum cost path based on the recursive results.

8. The workpiece scheduling method based on tool optimization as described in claim 5, characterized in that, The implementation process of the probabilistic convergence method includes: Initialize the random processing order as the current solution; Generate a new solution based on the current solution through neighborhood operations; Calculate the acceptance probability based on the change in the objective function of the current solution and the new solution; By dynamically adjusting the acceptance probability threshold using temperature parameters, and through a simulated annealing mechanism, a gradual convergence is achieved from high-temperature wide-area exploration to low-temperature local optimization, ultimately outputting the optimal workpiece scheduling scheme.

9. A workpiece scheduling system based on tool optimization, characterized in that, include: The data acquisition module is used to obtain the number of types of workpieces to be processed, the tool requirements for each workpiece, and the available capacity of the machine tool magazine. A high-frequency tool priority configuration module is communicatively connected to the data acquisition module. It is used to calculate the usage frequency of each type of tool based on the tool requirement data corresponding to each workpiece if the available capacity of the machine tool magazine is less than the sum of the number of tool types required for all workpieces to be processed. The high-frequency tool is the tool whose demand exceeds a preset threshold among all workpieces to be processed. The remaining tool statistics module is communicatively connected to the data acquisition module and the high-frequency tool priority configuration module. It is used to remove the number of types of priority configuration tools from the tool requirement data corresponding to each workpiece and count the number of remaining tool types. The production scheduling strategy acquisition module is communicatively connected to the data acquisition module and the remaining tool statistics module. It is used to select different optimization methods based on the comparison results of the number of remaining tool types with preset values ​​and the available capacity constraints of the machine tool magazine, and to obtain the final workpiece machining production scheduling strategy.

10. The workpiece scheduling system based on tool optimization as described in claim 9, characterized in that, The production scheduling strategy acquisition module includes: The first production scheduling strategy acquisition unit is communicatively connected to the remaining tool statistics module. When the number of remaining tool types does not exceed the first critical value, it uses an exhaustive search method to generate all workpiece production scheduling sequence combinations that satisfy the tool magazine capacity constraint, and selects the workpiece production scheduling scheme with the fewest tool changes from them. The second production scheduling strategy acquisition unit is connected to the remaining tool statistics module. When the number of remaining tool types is between the first and second critical values, the optimal production scheduling order is calculated by using a recursive formula in combination with the state transition optimization method and the available capacity constraint of the machine tool magazine. The third production scheduling strategy acquisition unit is connected in communication with the remaining tool statistics module. When the number of remaining tool types exceeds the second critical value, it adopts the probabilistic convergence method, which generates neighborhood solutions that satisfy the capacity constraints and combines the penalty function with temperature parameters to control the search process and obtain an approximate optimal solution.

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