A method and system for optimizing the manufacturing process of a scroll plate

Abstract

This invention discloses a method and system for optimizing the manufacturing process of a scroll disk, relating to the field of intelligent manufacturing technology. The method includes: selecting an initial machining trajectory based on the target workpiece design parameters by traversing an experience database; simulating scroll disk machining using a digital twin model; adjusting the initial machining trajectory based on the simulation results to obtain a first optimized machining trajectory; repeating the machining simulation and trajectory adjustment to obtain multiple candidate machining trajectories; constructing a trajectory optimization function; combining multiple candidate machining trajectories to optimize the machining trajectory and obtain the optimal machining trajectory for scroll disk machining production. This invention solves the technical problem in the prior art where unreasonable scroll disk machining trajectory design causes tool chatter, affecting workpiece quality and tool life. By optimizing the scroll disk machining trajectory and controlling the amplitude of tool chatter during the cutting process, it achieves the technical effects of improving workpiece quality and reducing tool wear.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, specifically to a method and system for optimizing the manufacturing process of a scroll disk. Background Technology

[0002] The scroll plate is a crucial component of a scroll compressor, consisting of a moving scroll plate and a stationary scroll plate. Gas compression relies on the cooperation of the moving and stationary plates, requiring high dimensional consistency and surface smoothness in both components to ensure compression performance. However, the scroll profile of a scroll plate is often a combination of one or more tangential curves. Due to the curvature variations in the profile, the cutting tool is prone to chattering during machining, leading to rapid tool wear and affecting workpiece quality. Summary of the Invention

[0003] This application provides a method and system for optimizing the manufacturing process of a scroll plate, which solves the technical problem in the prior art where tool chatter is caused by unreasonable design of the scroll plate machining trajectory, affecting workpiece quality and tool life.

[0004] The first aspect of this application provides a method for optimizing the manufacturing process of a vortex disk. The method includes: collecting design parameters of a target workpiece; selecting an initial machining trajectory by traversing an experience database based on the design parameters of the target workpiece, wherein the initial machining trajectory includes multiple sub-trajectories with different cutting speeds; performing vortex disk machining simulation based on the initial machining trajectory and a digital twin model to obtain simulation test results; adjusting the initial machining trajectory based on the simulation test results to obtain a first optimized machining trajectory; repeatedly performing machining simulation and machining trajectory adjustment with reference to the first optimized machining trajectory, and selecting multiple candidate machining trajectories by combining tool chatter data thresholds and workpiece quality thresholds; constructing a trajectory optimization function, performing machining trajectory optimization with the multiple candidate machining trajectories to obtain an optimal machining trajectory; and performing vortex disk machining production with reference to the optimal machining trajectory.

[0005] A second aspect of this application provides a manufacturing process optimization system for a vortex disk. The system includes: an initial machining trajectory acquisition module, which collects design parameters of a target workpiece and selects an initial machining trajectory by traversing an experience database based on the design parameters. The initial machining trajectory includes multiple sub-trajectories with different cutting speeds. A simulation test result acquisition module is used to simulate vortex disk machining based on the initial machining trajectory and a digital twin model, and obtain simulation test results. A first optimized machining trajectory acquisition module is used to... Based on the simulation test results, the initial machining trajectory is adjusted to obtain a first optimized machining trajectory; a candidate machining trajectory acquisition module is used to repeatedly perform machining simulation and machining trajectory adjustment with reference to the first optimized machining trajectory, and to filter out multiple candidate machining trajectories by combining tool chatter data threshold and workpiece quality threshold; an optimal machining trajectory acquisition module is used to construct a trajectory optimization function, combine the multiple candidate machining trajectories to perform machining trajectory optimization, and obtain the optimal machining trajectory; a vortex disk machining production module is used to perform vortex disk machining production with reference to the optimal machining trajectory.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application provides a method for optimizing the manufacturing process of a scroll disk, relating to the field of intelligent manufacturing technology. By using the design parameters of the target workpiece, an initial machining trajectory is selected by traversing an experience database. Multiple scroll disk machining simulations are then performed using a digital twin model. Based on the simulation results, the initial machining trajectory is adjusted multiple times to obtain several alternative machining trajectories. Finally, an optimization function is used to find the optimal machining trajectory for scroll disk machining production. This method solves the technical problem in existing technologies where unreasonable scroll disk machining trajectory design causes tool chatter, affecting workpiece quality and tool life. By optimizing the scroll disk machining trajectory and controlling the amplitude of tool chatter during the cutting process, the method achieves the technical effects of improving workpiece quality and reducing tool wear. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1A schematic flowchart illustrating a method for optimizing the manufacturing process of a vortex disk, as provided in an embodiment of this application;

[0010] Figure 2 A flowchart illustrating the process optimization method for manufacturing a scroll disk provided in this application embodiment to obtain a first optimized machining trajectory;

[0011] Figure 3 A flowchart illustrating the adjustment of the first superior machining trajectory in a method for optimizing the manufacturing process of a scroll disk provided in this application embodiment;

[0012] Figure 4 This is a schematic diagram of a system structure for optimizing the manufacturing process of a vortex disk, provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached figures: Initial machining trajectory acquisition module 11, simulation test result acquisition module 12, first optimized machining trajectory acquisition module 13, alternative machining trajectory acquisition module 14, optimal machining trajectory acquisition module 15, vortex disk machining production module 16. Detailed Implementation

[0014] This application provides a method for optimizing the manufacturing process of a scroll plate, which solves the technical problem in the prior art where unreasonable design of the scroll plate machining trajectory causes tool chatter, affecting workpiece quality and tool life.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices. Example 1

[0017] like Figure 1As shown, this application provides a method for optimizing the manufacturing process of a scroll disk, the method comprising:

[0018] P10: Collect the design parameters of the target workpiece, and select the initial machining trajectory by traversing the experience database based on the design parameters of the target workpiece. The initial machining trajectory contains multiple sub-trajectories, and the multiple sub-trajectories have different cutting speeds.

[0019] It should be understood that the design parameters of the target workpiece are collected. The target workpiece is the scroll disk to be produced. Its design parameters include the scroll tooth height, tooth thickness, pitch, number of scroll tooth turns, and scroll disk diameter. Then, based on the design parameters of the target workpiece, the initial machining trajectory is selected by traversing and matching in the experience database. The experience database is constructed by historical machining experience data, which includes multiple sample workpiece design parameters and corresponding multiple sample machining trajectories. The initial machining trajectory is the sample machining trajectory corresponding to the sample workpiece design parameters most similar to the design parameters of the target workpiece.

[0020] Furthermore, the vortex profile design of the scroll disk is mostly a combination of one or more tangent curves. The curvature change of its profile will cause the cutting force and instantaneous process elastic deformation of the tool to change continuously during the cutting process, causing the tool to be prone to chatter. Therefore, it is necessary to control the machining trajectory of the CNC program to avoid tool chatter. Thus, the initial machining trajectory includes multiple sub-trajectories, and the multiple sub-trajectories have different cutting speeds.

[0021] P20: Based on the initial machining trajectory, combined with the digital twin model, perform vortex disk machining simulation and obtain simulation test results;

[0022] Furthermore, step P20 in this embodiment of the application also includes:

[0023] P21: The simulation test results include tool chatter data and workpiece quality grade, wherein the tool chatter data includes tool chatter amplitude, tool chatter frequency and tool chatter location;

[0024] P22: The workpiece quality grade includes the workpiece shape and size grade and the surface smoothness grade.

[0025] Optionally, based on the equipment parameters of the currently used vortex disk machining equipment, a digital twin model of the current machining equipment is constructed using 3D simulation technology. Using this digital twin model, a 3D digital vortex disk machining simulation is performed with reference to the initial machining trajectory, and simulation test results are obtained. These results include tool chatter data and workpiece quality grades at various trajectory positions during the machining test. The tool chatter data includes the tool chatter amplitude at each location, the tool chatter frequency per unit time, and the tool chatter location. The workpiece quality grade includes the workpiece shape and size grade and the surface smoothness grade. The workpiece shape and size grade refers to the deviation level between the dimensions of each part of the machined workpiece and the design dimensions; a higher grade indicates a smaller dimensional deviation. The surface smoothness grade is set based on the severity of surface cracks, roughness, etc., and reflects the machining quality of the workpiece.

[0026] P30: Based on the simulation test results, adjust the initial machining trajectory to obtain the first optimized machining trajectory;

[0027] Furthermore, such as Figure 2 As shown, step P30 in this embodiment further includes:

[0028] P31: Based on the tool chatter position, obtain multiple trajectory segmentation points;

[0029] P32: Referring to the multiple trajectory segmentation points, perform initial trajectory segmentation to obtain multiple newly added sub-trajectories;

[0030] P33: Adjust the cutting speed of the newly added sub-path based on the tool chatter amplitude, tool chatter frequency, workpiece shape and position size grade, and surface smoothness grade;

[0031] P34: The first optimized processing trajectory is composed of multiple newly added sub-trajectories.

[0032] For example, the tool chatter position is obtained based on the simulation test results, and then multiple trajectory segmentation points are set at each chatter position. The initial machining trajectory is segmented with reference to the multiple trajectory segmentation points, that is, it is segmented based on the original sub-trajectory to obtain multiple new sub-trajectories. Further, the cutting speed of the new sub-trajectories is adjusted according to the tool chatter amplitude, tool chatter frequency, workpiece shape and size level and surface smoothness level at each chatter position. For example, the correlation between cutting speed and tool chatter amplitude, tool chatter frequency, workpiece shape and size level and surface smoothness level is established respectively. Then, the cutting speed of each new sub-trajectory is adjusted according to the correlation, for example, the cutting speed at the position with higher tool chatter amplitude is reduced, and the adjusted multiple new sub-trajectories form the first optimized machining trajectory.

[0033] P40: Referring to the first optimized machining trajectory, repeat the machining simulation and machining trajectory adjustment, and combine the tool chatter data threshold and workpiece quality threshold to select multiple alternative machining trajectories;

[0034] In one possible embodiment of this application, based on tool maintenance requirements and workpiece quality requirements, tool chatter data thresholds and workpiece quality thresholds are set, including tool chatter amplitude thresholds, tool chatter frequency thresholds, workpiece shape and size level thresholds, and surface smoothness level thresholds. Then, referring to the adjustment method of the first optimized machining trajectory, multiple machining simulations and machining trajectory adjustments are repeated to obtain multiple adjusted machining trajectories. Multiple machining trajectories that meet the tool chatter data thresholds and workpiece quality thresholds are selected as candidate machining trajectories, which is the machining trajectory optimization space of the target workpiece.

[0035] P50: Construct a trajectory optimization function, combine the multiple alternative processing trajectories, optimize the processing trajectory, and obtain the optimal processing trajectory;

[0036] Furthermore, step P50 in this embodiment of the application also includes:

[0037] P51: Obtain machining trajectory optimization parameters, including number of segments, cutting speed, tool amplitude, and workpiece quality grade;

[0038] P52: Based on the aforementioned machining trajectory optimization parameters, a trajectory optimization function is constructed to optimize the machining trajectory, as shown in the following equation:

[0039] ;

[0040] Where Q is the function value for evaluating the quality of the processing trajectory, and S is the number of sub-trajectories. Let i be the workpiece quality grade of the i-th sub-trajectory. Let be the tool amplitude of the i-th sub-trajectory. Let be the cutting speed of the i-th sub-trajectory. The length of the i-th sub-trajectory As the first weight, It is the second weight.

[0041] P53: Based on the multiple candidate processing trajectories, multiple first candidate processing trajectories are randomly selected, and multiple first function values ​​are calculated by combining the trajectory optimization function;

[0042] P54: Based on multiple first function values, multiple first-grade superior machining trajectories and multiple first-grade equal machining trajectories are obtained;

[0043] P55: Using multiple first-class processing trajectories as cluster centers, and referring to multiple first function values, multiple cluster quantities are allocated to cluster multiple first-class processing trajectories to obtain multiple sets of first-class processing trajectories;

[0044] P56: Within multiple sets of first processing trajectories, the first superior processing trajectory is adjusted according to multiple first-level equal processing trajectories to obtain multiple sets of second processing trajectories;

[0045] P57: Continue iterative optimization in this manner until the preset number of optimizations is reached, and obtain multiple final processing trajectory sets;

[0046] P58: Based on the sum of function values ​​of multiple final processing trajectory sets, select the processing trajectory with the largest function value in the final processing trajectory set with the largest sum of function values ​​and output it to obtain the optimal processing trajectory.

[0047] It should be understood that, by considering the influencing factors of vortex disk machining quality and tool life, as well as their correlation with the machining trajectory, parameters with significant impact are collected as machining trajectory optimization parameters. These include the number of segments, cutting speed, tool amplitude, and workpiece quality grade. The number of segments refers to the number of sub-trajectories, the cutting speed refers to the speed of the sub-trajectories, and the workpiece quality is a comprehensive quality. Based on these machining trajectory optimization parameters, a trajectory optimization function is constructed to optimize the machining trajectory. Where Q is the function value for evaluating the quality of the processing trajectory; the larger the function value, the better the processing trajectory. S is the number of sub-trajectories. Let i be the workpiece quality grade of the i-th sub-trajectory. The tool amplitude of the i-th sub-path is inversely proportional to the workpiece quality grade. Let be the cutting speed of the i-th sub-trajectory. The length of the i-th sub-trajectory As the first weight, It is the second weight.

[0048] Furthermore, from the plurality of candidate processing trajectories, a plurality of candidate processing trajectories are randomly selected as first candidate processing trajectories, and the function value is calculated using the trajectory optimization function to obtain a plurality of first function values. Then, the trajectory types are divided according to the magnitude of the plurality of first function values, and the trajectory with the larger function value is the first superior processing trajectory, and the trajectory with the smaller function value is the first first-class processing trajectory.

[0049] Furthermore, multiple first-class processing trajectories are used as cluster centers, and multiple cluster numbers are allocated according to the proportion of the first function value of each first-class processing trajectory to the total function value. Multiple first-class processing trajectories are clustered according to the number of clusters, and the first-class processing trajectories with high similarity to each cluster center are assigned to the same cluster to form multiple clusters, which are multiple sets of first-class processing trajectories.

[0050] Furthermore, within multiple sets of first processing trajectories, the first superior processing trajectory is adjusted using multiple first-order equal processing trajectories as references to obtain multiple adjusted processing trajectories, which are also multiple second processing trajectories. These second processing trajectories are then filtered according to their function values, retaining those with larger function values. Clustering is then used to obtain multiple sets of second processing trajectories. This process is iteratively optimized until a preset number of optimizations is reached. The set of multiple processing trajectories obtained from the last iteration is retained as multiple final processing trajectory sets. The processing trajectory with the largest function value within the final processing trajectory set with the largest sum of function values ​​is selected as the optimal processing trajectory and output.

[0051] Furthermore, such as Figure 3 As shown, step P56 in this embodiment further includes:

[0052] P56-1: Based on multiple sets of first processing trajectories, multiple first equal processing trajectories are used as adjustment directions to obtain multiple first adjustment directions;

[0053] P56-2: Using the number of segments and the cutting speed as adjustment step sizes respectively, and combining multiple first adjustment directions, each first superior processing trajectory is adjusted to obtain multiple second processing trajectories;

[0054] P56-3: Calculate multiple second function values ​​for multiple second processing trajectories, compare and filter them with the first processing trajectory, update and obtain multiple second superior processing trajectories and multiple second equal processing trajectories, and obtain a set of multiple second processing trajectories.

[0055] Specifically, for multiple sets of first processing trajectories, the number of segments and the segmentation speed of multiple first-class processing trajectories within the set are used as adjustment directions to obtain multiple first adjustment directions. Then, the adjustment step size of the number of segments and the cutting speed are set respectively. For example, one segment is adjusted each time. In combination with multiple first adjustment directions, each first-class processing trajectory is adjusted multiple times according to the adjustment step size to obtain multiple second processing trajectories.

[0056] Furthermore, multiple second function values ​​are calculated for multiple second processing trajectories, and the first processing trajectory is compared and filtered by comparing the function values. The processing trajectory with the larger function value is retained, and multiple second superior processing trajectories and multiple second equal processing trajectories are obtained. Then, multiple sets of second processing trajectories are clustered to obtain multiple sets of second processing trajectories.

[0057] P60: Refer to the optimal machining trajectory to perform vortex disk machining production.

[0058] Furthermore, step P60 in this embodiment of the application also includes:

[0059] P61: Collect initial tool wear data;

[0060] P62: Based on the optimal machining trajectory, conduct multiple small-scale physical machining tests to obtain multiple physical machining samples and collect multiple tool wear data after machining;

[0061] P63: Perform quality testing on the multiple processed physical samples to obtain multiple sample quality grades;

[0062] P64: Based on the initial tool wear data and multiple post-machining tool wear data, tool wear assessment is performed, and based on multiple sample quality grades, sample quality assessment is performed to obtain tool wear assessment results and sample quality assessment results;

[0063] P65: The optimal machining trajectory is calibrated based on the tool wear assessment results and sample quality assessment results.

[0064] Specifically, initial tool wear data of the currently used equipment is collected, and multiple small-scale physical machining tests are conducted with reference to the optimal machining trajectory. For example, fifty spindle production processes are performed to obtain multiple physical machining samples. At the end of production, multiple post-machining tool wear data are collected. Furthermore, the multiple physical machining samples are subjected to quality inspection to obtain multiple sample quality grades, including sample shape and size grades and surface smoothness grades. Further, tool wear is evaluated based on the initial tool wear data and multiple post-machining tool wear data to obtain the tool wear degree. Sample quality is then evaluated based on the multiple sample quality grades to obtain the sample comprehensive quality, which serves as the tool wear evaluation result and the sample quality evaluation result. These results are then compared with tool wear maintenance standards and sample quality standards to calibrate the optimal machining trajectory, thereby reducing tool wear and improving workpiece quality.

[0065] In summary, the embodiments of this application have at least the following technical effects:

[0066] This application selects an initial machining trajectory by traversing an experience database based on the design parameters of the target workpiece, performs multiple vortex disk machining simulations using a digital twin model, and adjusts the initial machining trajectory multiple times based on the simulation results to obtain multiple alternative machining trajectories. Finally, it optimizes the trajectory using a trajectory optimization function to obtain the optimal machining trajectory for vortex disk machining production.

[0067] This technology achieves the technical effect of optimizing the machining trajectory of the scroll wheel, controlling the amplitude of tool chatter during the cutting process, improving workpiece quality, and reducing tool wear. Example 2

[0068] Based on the same inventive concept as the optimized manufacturing process of a vortex disk in the foregoing embodiments, such as Figure 4 As shown, this application provides a system for optimizing the manufacturing process of a vortex disk. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0069] The initial machining trajectory acquisition module 11 is used to collect the design parameters of the target workpiece, and select the initial machining trajectory by traversing the experience database according to the design parameters of the target workpiece. The initial machining trajectory includes multiple sub-trajectories, and the multiple sub-trajectories have different cutting speeds.

[0070] The simulation test result acquisition module 12 is used to simulate the machining of the vortex disk based on the initial machining trajectory and combined with the digital twin model, and to acquire the simulation test results.

[0071] The first optimized processing trajectory acquisition module 13 is used to adjust the initial processing trajectory according to the simulation test results to obtain the first optimized processing trajectory;

[0072] The alternative machining trajectory acquisition module 14 is used to repeatedly perform machining simulation and machining trajectory adjustment with reference to the first optimized machining trajectory, and to filter and obtain multiple alternative machining trajectories by combining the tool chatter data threshold and the workpiece quality threshold.

[0073] The optimal processing trajectory acquisition module 15 is used to construct a trajectory optimization function, combine the multiple alternative processing trajectories, perform processing trajectory optimization, and obtain the optimal processing trajectory.

[0074] The vortex disk machining production module 16 is used to perform vortex disk machining production with reference to the optimal machining trajectory.

[0075] Furthermore, the simulation test result acquisition module 12 is also used to perform the following steps:

[0076] The simulation test results include tool chatter data and workpiece quality grade, wherein the tool chatter data includes tool chatter amplitude, tool chatter frequency and tool chatter location;

[0077] The workpiece quality grade includes the workpiece shape and size grade and the surface smoothness grade.

[0078] Furthermore, the first optimized processing trajectory acquisition module 13 is also used to perform the following steps:

[0079] Based on the tool chatter position, multiple trajectory segmentation points are obtained;

[0080] Referring to the multiple trajectory segmentation points, initial trajectory segmentation is performed to obtain multiple newly added sub-trajectories;

[0081] The cutting speed of the newly added sub-path is adjusted based on the tool chatter amplitude, tool chatter frequency, workpiece shape and size grade, and surface smoothness grade.

[0082] The first optimized processing trajectory is composed of multiple newly added sub-trajectories.

[0083] Furthermore, the optimal processing trajectory acquisition module 15 is also used to perform the following steps:

[0084] Obtain machining trajectory optimization parameters, including number of segments, cutting speed, tool amplitude, and workpiece quality grade;

[0085] Based on the aforementioned machining trajectory optimization parameters, a trajectory optimization function is constructed to optimize the machining trajectory, as shown in the following equation:

[0086] ;

[0087] Where Q is the function value for evaluating the quality of the processing trajectory, and S is the number of sub-trajectories. Let i be the workpiece quality grade of the i-th sub-trajectory. Let be the tool amplitude of the i-th sub-trajectory. Let be the cutting speed of the i-th sub-trajectory. The length of the i-th sub-trajectory As the first weight, It is the second weight.

[0088] Furthermore, the optimal processing trajectory acquisition module 15 is also used to perform the following steps:

[0089] Based on the multiple candidate processing trajectories, multiple first candidate processing trajectories are randomly selected, and multiple first function values ​​are calculated by combining the trajectory optimization function.

[0090] Based on multiple first function values, multiple first-grade superior processing trajectories and multiple first-grade equal processing trajectories are obtained;

[0091] Using multiple first-class processing trajectories as cluster centers, and referring to multiple first function values, multiple cluster numbers are assigned to cluster multiple first-class processing trajectories to obtain multiple sets of first-class processing trajectories;

[0092] Within multiple sets of first processing trajectories, the first superior processing trajectory is adjusted according to multiple first-level equal processing trajectories to obtain multiple sets of second processing trajectories;

[0093] This process is repeated iteratively until a preset number of optimizations is reached, resulting in multiple final processing trajectory sets.

[0094] Based on the sum of function values ​​of multiple final processing trajectory sets, the processing trajectory with the largest function value in the final processing trajectory set with the largest sum of function values ​​is selected and output to obtain the optimal processing trajectory.

[0095] Furthermore, the optimal processing trajectory acquisition module 15 is also used to perform the following steps:

[0096] Based on multiple sets of first processing trajectories, multiple first processing trajectories are used as adjustment directions to obtain multiple first adjustment directions;

[0097] The number of segments and the cutting speed are used as adjustment step sizes, and multiple first adjustment directions are combined to adjust each first superior processing trajectory to obtain multiple second processing trajectories;

[0098] Calculate multiple second function values ​​for multiple second processing trajectories, compare and filter them with the first processing trajectory, update and obtain multiple second superior processing trajectories and multiple second equal processing trajectories, and obtain a set of multiple second processing trajectories.

[0099] Furthermore, the vortex disk processing module 16 is also used to perform the following steps:

[0100] Collect initial tool wear data;

[0101] Based on the optimal machining trajectory, multiple small-scale physical machining tests were conducted to obtain multiple physical machining samples and collect multiple tool wear data after machining.

[0102] The multiple processed physical samples are subjected to quality testing to obtain multiple sample quality grades;

[0103] Tool wear assessment is performed based on the initial tool wear data and multiple post-machining tool wear data, and sample quality assessment is performed based on multiple sample quality levels to obtain tool wear assessment results and sample quality assessment results.

[0104] The optimal machining trajectory is calibrated based on the tool wear assessment results and sample quality assessment results.

[0105] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0107] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An optimized manufacturing process method for a scroll disk, characterized in that, The method includes: Collect the design parameters of the target workpiece, and select the initial machining trajectory by traversing the experience database based on the design parameters of the target workpiece. The initial machining trajectory contains multiple sub-trajectories, and the multiple sub-trajectories have different cutting speeds. Based on the initial machining trajectory, combined with the digital twin model, a vortex disk machining simulation was performed to obtain the simulation test results; Based on the simulation test results, the initial machining trajectory was adjusted to obtain the first optimized machining trajectory; Referring to the first optimized machining trajectory, the machining simulation and machining trajectory adjustment are repeated. Combining the tool chatter data threshold and the workpiece quality threshold, multiple alternative machining trajectories are selected. Construct a trajectory optimization function, combine the multiple candidate processing trajectories, optimize the processing trajectory, and obtain the optimal processing trajectory; The vortex disk is manufactured according to the optimal machining trajectory described above. Among them, based on the initial machining trajectory and combined with a digital twin model, a vortex disk machining simulation is performed to obtain simulation test results, including: The simulation test results include tool chatter data and workpiece quality grade, wherein the tool chatter data includes tool chatter amplitude, tool chatter frequency and tool chatter location; The workpiece quality grade includes the workpiece shape and size grade and the surface smoothness grade; The process of adjusting the initial machining trajectory based on the simulation test results to obtain the first optimized machining trajectory includes: Based on the tool chatter position, multiple trajectory segmentation points are obtained; Referring to the multiple trajectory segmentation points, initial trajectory segmentation is performed to obtain multiple newly added sub-trajectories; The cutting speed of the newly added sub-path is adjusted based on the tool chatter amplitude, tool chatter frequency, workpiece shape and size grade, and surface smoothness grade. The first optimized processing trajectory is composed of multiple newly added sub-trajectories; The trajectory optimization function includes: Obtain machining trajectory optimization parameters, including number of segments, cutting speed, tool amplitude, and workpiece quality grade; Based on the aforementioned machining trajectory optimization parameters, a trajectory optimization function is constructed to optimize the machining trajectory, as shown in the following equation: Where Q is the function value for evaluating the quality of the processing trajectory, S is the number of sub-trajectories, and T is the value of the processing trajectory. i Let C be the workpiece quality grade of the i-th sub-trajectory. i Let F be the tool amplitude of the i-th sub-trajectory. i Let L be the cutting speed of the i-th sub-trajectory. i The length of the i-th sub-trajectory, where W1 is the first weight and W2 is the second weight; wherein, combining the multiple candidate processing trajectories, the processing trajectory is optimized to obtain the optimal processing trajectory, including: Based on the multiple candidate processing trajectories, multiple first candidate processing trajectories are randomly selected, and multiple first function values ​​are calculated by combining the trajectory optimization function. Based on multiple first function values, multiple first-grade superior processing trajectories and multiple first-grade equal processing trajectories are obtained; Using multiple first-class processing trajectories as cluster centers, and referring to multiple first function values, multiple cluster numbers are assigned to cluster multiple first-class processing trajectories to obtain multiple sets of first-class processing trajectories; Within multiple sets of first processing trajectories, the first superior processing trajectory is adjusted according to multiple first-level equal processing trajectories to obtain multiple sets of second processing trajectories; This process is repeated iteratively until a preset number of optimizations is reached, resulting in multiple final processing trajectory sets. Based on the sum of function values ​​of multiple final processing trajectory sets, the processing trajectory with the largest function value in the final processing trajectory set with the largest sum of function values ​​is selected and output to obtain the optimal processing trajectory.

2. The method as described in claim 1, characterized in that, The step of adjusting the first superior processing trajectory within a set of multiple first processing trajectories, based on multiple first-level equal processing trajectories, includes: Based on multiple sets of first processing trajectories, multiple first processing trajectories are used as adjustment directions to obtain multiple first adjustment directions; The number of segments and the cutting speed are used as adjustment step sizes, and multiple first adjustment directions are combined to adjust each first superior processing trajectory to obtain multiple second processing trajectories; Calculate multiple second function values ​​for multiple second processing trajectories, compare and filter them with the first processing trajectory, update and obtain multiple second superior processing trajectories and multiple second equal processing trajectories, and obtain a set of multiple second processing trajectories.

3. The method as described in claim 1, characterized in that, Referring to the optimal machining trajectory, the vortex disk is machined and produced, including: Collect initial tool wear data; Based on the optimal machining trajectory, multiple small-scale physical machining tests were conducted to obtain multiple physical machining samples and collect multiple tool wear data after machining. The multiple processed physical samples are subjected to quality testing to obtain multiple sample quality grades; Tool wear assessment is performed based on the initial tool wear data and multiple post-machining tool wear data, and sample quality assessment is performed based on multiple sample quality levels to obtain tool wear assessment results and sample quality assessment results. The optimal machining trajectory is calibrated based on the tool wear assessment results and sample quality assessment results.

4. A manufacturing process optimization system for a vortex disk, characterized in that, The system is used to perform the manufacturing process optimization method for the vortex disk according to any one of claims 1 to 3, the system comprising: An initial machining trajectory acquisition module is used to collect the design parameters of the target workpiece, and select an initial machining trajectory by traversing an experience database based on the design parameters of the target workpiece. The initial machining trajectory includes multiple sub-trajectories, and the multiple sub-trajectories have different cutting speeds. The simulation test result acquisition module is used to simulate the machining of the vortex disk based on the initial machining trajectory and combined with the digital twin model, and to acquire the simulation test results. The first optimized processing trajectory acquisition module is used to adjust the initial processing trajectory according to the simulation test results to obtain the first optimized processing trajectory; The alternative machining trajectory acquisition module is used to repeatedly perform machining simulation and machining trajectory adjustment with reference to the first optimized machining trajectory, and to filter and obtain multiple alternative machining trajectories by combining the tool chatter data threshold and the workpiece quality threshold. The optimal processing trajectory acquisition module is used to construct a trajectory optimization function, combine the multiple alternative processing trajectories, optimize the processing trajectory, and obtain the optimal processing trajectory. A vortex disk machining and production module is used to perform vortex disk machining and production with reference to the optimal machining trajectory.

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