Processing parameter optimization method, device, processing equipment and readable storage medium

Abstract

This application relates to a method, apparatus, processing equipment, and readable storage medium for optimizing processing parameters. The method includes: acquiring a workpiece processing planning position point cloud and a workpiece image contour point cloud under different combinations of processing parameters; determining the processing quality evaluation results under different combinations of processing parameters based on the workpiece processing planning position point cloud and the workpiece image contour point cloud; and determining the optimized processing parameter combination for the workpiece processing process based on the processing quality evaluation results under different combinations of processing parameters and the different processing parameter combinations. This method can effectively adjust processing parameters during workpiece processing, improving upon the problem that traditional processing parameter adjustment relies on engineers' experience and repeated trials, which is time-consuming, labor-intensive, and difficult to guarantee processing efficiency. It can ensure both workpiece processing efficiency and the accuracy and adjustability of the processing process, thereby meeting higher requirements for CNC machining effects.

Description

Technical Field

[0001] This application relates to the field of CNC machining, and in particular to a method, apparatus, machining equipment, and readable storage medium for optimizing machining parameters. Background Technology

[0002] With the continuous development of CNC machining technology and the increasing diversity of CNC machining demands, the requirements for CNC machining effects are becoming increasingly stringent. Precision machining technology is one of the core processes in modern high-end manufacturing, and its machining quality and efficiency ultimately depend on the coordinated configuration of multiple adjustable parameters within the machining system, including motion parameters and process parameters. However, the traditional parameter adjustment process relies on the experience of engineers and repeated trials, which is time-consuming, labor-intensive, and difficult to guarantee machining efficiency. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, processing equipment, and readable storage medium for optimizing processing parameters to address the aforementioned technical problems.

[0004] A method for optimizing processing parameters, comprising:

[0005] Acquire the workpiece machining planning position point cloud and the workpiece image contour point cloud under different combinations of machining parameters; Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, determine the machining quality evaluation results under the action of different combinations of machining parameters; Based on the evaluation results of the processing quality under different combinations of processing parameters and the different combinations of processing parameters, the optimal combination of processing parameters for the workpiece processing process is determined.

[0006] In one embodiment, determining the processing quality evaluation result under different combinations of processing parameters based on the workpiece processing planning position point cloud and the workpiece image contour point cloud includes: Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, determine the workpiece position point cloud transformation matrix; Based on the position point cloud transformation matrix and the workpiece image contour point cloud, the iteratively optimized workpiece image contour point cloud is determined. Based on the iteratively optimized workpiece image contour point cloud and the workpiece machining planning position point cloud, the machining quality evaluation results under the action of different combinations of machining parameters are determined.

[0007] In one embodiment, determining the workpiece position point cloud transformation matrix based on the workpiece machining planning position point cloud and the workpiece image contour point cloud includes: Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, determine the workpiece position point cloud matching set and point cloud error optimization model; The position point cloud transformation matrix of the workpiece is determined based on the position point cloud matching set and the point cloud error optimization model. The step of determining the iteratively optimized workpiece image contour point cloud based on the position point cloud transformation matrix and the workpiece image contour point cloud includes: Obtain the location point cloud error constraint threshold and iteration number constraint value; Using the position point cloud error constraint threshold as a constraint condition, the workpiece image contour point cloud is iteratively optimized according to the position point cloud transformation matrix to determine the iteratively optimized workpiece image contour point cloud.

[0008] In one embodiment, determining the processing quality evaluation result under different combinations of processing parameters based on the iteratively optimized workpiece image contour point cloud and the workpiece processing planning position point cloud includes: Based on the iteratively optimized workpiece image contour point cloud and the workpiece machining planning position point cloud, the position point cloud deviation value of the workpiece is determined; Based on the position point cloud deviation value of the workpiece, the processing quality evaluation result under the action of different processing parameter combinations is determined.

[0009] In one embodiment, determining the optimal combination of processing parameters for the workpiece processing process based on the processing quality evaluation results under different combinations of processing parameters and the different combinations of processing parameters includes: Based on the processing quality evaluation results under different combinations of processing parameters and the different combinations of processing parameters, the probability density model of the processing parameters of the workpiece is determined; Based on the probability density model of the processing parameters, the optimal combination of processing parameters for the workpiece processing process is determined.

[0010] In one embodiment, the processing parameter probability density model includes a first processing parameter probability density model and a second processing parameter probability density model. Determining the processing parameter probability density model of the workpiece based on the processing quality evaluation results under different processing parameter combinations and the different processing parameter combinations includes: Based on the processing quality evaluation results under different processing parameter combinations, the different processing parameter combinations are sorted and divided to determine the first processing parameter set and the second processing parameter set after sorting and division. The processing quality evaluation results of the first processing parameter set and the second processing parameter set are different. Based on the sorted and divided first set of processing parameters, determine the probability density model of the first processing parameters of the workpiece; Based on the sorted and divided second set of processing parameters, the probability density model of the second processing parameters of the workpiece is determined.

[0011] In one embodiment, determining the optimal combination of processing parameters for the workpiece processing process based on the processing parameter probability density model includes: The optimal model for the processing parameters of the workpiece is determined based on the ratio of the probability density model of the first processing parameter to the probability density model of the second processing parameter. Based on the workpiece's machining parameter optimization model, the optimal combination of machining parameters for the workpiece machining process is determined.

[0012] A processing parameter optimization device, comprising: The acquisition module is used to acquire the workpiece machining planning position point cloud and the workpiece image contour point cloud under different combinations of machining parameters; A processing quality evaluation module, connected to the acquisition module, is used to determine the processing quality evaluation results under the action of different processing parameter combinations based on the workpiece processing planning position point cloud and the workpiece image contour point cloud. The processing parameter optimization module is connected to the processing quality evaluation module and is used to determine the optimal processing parameter combination for the workpiece processing process based on the processing quality evaluation results under the action of different processing parameter combinations and the different processing parameter combinations.

[0013] A processing apparatus includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the method described above.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0015] A computer program product that, when run on a terminal device, causes the terminal device to perform any of the methods described above.

[0016] The beneficial effects of the embodiments provided in this application include: This machining parameter optimization method, during workpiece machining, determines a machining quality evaluation result that quantitatively reflects the workpiece machining quality under different combinations of machining parameters, based on the workpiece machining planning position point cloud and the workpiece image contour point cloud under different combinations of machining parameters. Then, based on the obtained machining quality evaluation results under different combinations of machining parameters, iterative optimization processing is performed on different combinations of machining parameters to determine the optimized machining parameter combination that can guide the workpiece machining process. This method enables effective adjustment of machining parameters during workpiece machining, improving upon the problem that traditional machining parameter adjustment relies on engineers' experience and repeated trials, which is time-consuming, labor-intensive, and difficult to guarantee machining efficiency. It ensures both workpiece machining efficiency and the accuracy and adjustability of the machining process, thereby meeting higher requirements for CNC machining effects. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a processing parameter optimization method in one embodiment; Figure 2 This is a schematic diagram of the specific process of step 104 in one embodiment; Figure 3 This is a schematic diagram of the specific process of step 106 in one embodiment; Figure 4 This is a schematic block diagram of the processing parameter optimization device in one embodiment; Figure 5 This is a schematic block diagram of the specific structure of the processing quality evaluation module 40 in one embodiment; Figure 6 This is a schematic block diagram of the specific structure of the processing parameter optimization module 60 in one embodiment; Figure 7 This is a schematic diagram of the processing equipment in one embodiment. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] Figure 1 This is a flowchart illustrating a processing parameter optimization method in one embodiment.

[0022] In this embodiment, as Figure 1 As shown, the processing parameter optimization method includes steps 102 to 106.

[0023] Step 102: Obtain the workpiece machining planning position point cloud and the workpiece image contour point cloud under different combinations of machining parameters.

[0024] The workpiece machining planning position point cloud can be a set of machining position planning data before the start of workpiece machining. The machining parameter combination can be a combination of multiple different types of machining parameters that can be applied to the machine tool control system to perform machining on the workpiece. The workpiece image contour point cloud can be a set of machining position image contour data acquired during the machining process. The machining parameter combination can be a combination of laser processing power, laser output duty cycle, and laser head linear acceleration.

[0025] The scenarios for obtaining workpiece machining planning position point clouds and workpiece image contour point clouds under different combinations of machining parameters include: obtaining workpiece machining planning position point clouds by analyzing workpiece machining planning drawings; and obtaining workpiece image contour point clouds under different combinations of machining parameters by acquiring workpiece images through a machine vision system.

[0026] Step 104: Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, determine the machining quality evaluation results under different combinations of machining parameters.

[0027] The processing quality evaluation results can be formed based on the workpiece processing planning position point cloud and the workpiece image contour point cloud, and can quantitatively reflect the workpiece processing quality under different combinations of processing parameters.

[0028] Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, the following scenarios are used to determine the machining quality evaluation results under different combinations of machining parameters: based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, point cloud matching and point cloud error optimization are performed sequentially to determine the machining quality evaluation results under different combinations of machining parameters after processing.

[0029] Step 106: Based on the evaluation results of the processing quality under different combinations of processing parameters and the different combinations of processing parameters, determine the optimal combination of processing parameters for the workpiece processing process.

[0030] An optimized combination of machining parameters can be a set of machining parameters that can guide the optimization of the workpiece machining process, based on the evaluation results of machining quality under different combinations of machining parameters and different combinations of machining parameters.

[0031] Based on the evaluation results of processing quality under different combinations of processing parameters, the optimal processing parameter combination for the workpiece processing process can be determined in the following ways: sorting and classifying different processing parameter combinations based on the evaluation results of processing quality under different combinations of processing parameters, and performing optimization and iteration processing to determine the optimal processing parameter combination for the processed workpiece processing process.

[0032] The machining parameter optimization method provided in this embodiment determines a machining quality evaluation result that quantitatively reflects the machining quality under different combinations of machining parameters, based on the workpiece machining planning position point cloud and the workpiece image contour point cloud under different combinations of machining parameters during the workpiece machining process. Then, based on the obtained machining quality evaluation results under different combinations of machining parameters, iterative optimization processing is performed on different combinations of machining parameters to determine an optimized machining parameter combination that can provide optimization guidance for the workpiece machining process. This method enables effective adjustment of machining parameters during workpiece machining, improving upon the problem that traditional machining parameter adjustment relies on engineers' experience and repeated trials, which is time-consuming, labor-intensive, and difficult to guarantee machining efficiency. It ensures both workpiece machining efficiency and the accuracy and adjustability of the machining process, thereby meeting higher requirements for CNC machining effects.

[0033] Figure 2 This is a schematic diagram of the specific process of step 104 in one embodiment.

[0034] In this embodiment, as Figure 2 As shown, step 104 includes sub-steps 202 to 206.

[0035] Step 202: Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, determine the workpiece position point cloud transformation matrix.

[0036] The position point cloud transformation matrix can be formed based on the workpiece machining planning position and the workpiece image contour point cloud, and is a matrix that can perform rigid transformation on the workpiece image contour point cloud.

[0037] The scenarios for determining the workpiece's position point cloud transformation matrix based on the workpiece machining plan position point cloud and the workpiece image contour point cloud include: determining the workpiece's position point cloud matching set and point cloud error optimization model based on the workpiece machining plan position point cloud and the workpiece image contour point cloud; and determining the workpiece's position point cloud transformation matrix based on the position point cloud matching set and the point cloud error optimization model.

[0038] Step 204: Based on the position point cloud transformation matrix and the workpiece image contour point cloud, determine the iteratively optimized workpiece image contour point cloud.

[0039] The process of determining the iteratively optimized workpiece image contour point cloud based on the position point cloud transformation matrix and the workpiece image contour point cloud includes: obtaining the position point cloud error constraint threshold and the iteration number constraint value; using the position point cloud error constraint threshold and / or the iteration number constraint value as constraints, performing a rigid transformation on the workpiece image contour point cloud according to the position point cloud transformation matrix, and determining the rigidly transformed workpiece image contour point cloud.

[0040] The rigidly transformed workpiece image contour point cloud can be a new matrix formed by rigidly transforming the matrix corresponding to the workpiece image contour point cloud based on the position point cloud transformation matrix. It should be noted that the matrix corresponding to the rigidly transformed workpiece image contour point cloud is formed by translation and / or rotation transformation compared to the matrix corresponding to the workpiece image contour point cloud.

[0041] Step 206: Based on the iteratively optimized workpiece image contour point cloud and workpiece machining planning position point cloud, determine the machining quality evaluation results under different combinations of machining parameters.

[0042] Based on the iteratively optimized workpiece image contour point cloud and workpiece machining planning position point cloud, the following scenarios determine the machining quality evaluation results under different combinations of machining parameters: based on the iteratively optimized workpiece image contour point cloud and workpiece machining planning position point cloud, determine the workpiece position point cloud deviation value; based on the workpiece position point cloud deviation value, determine the machining quality evaluation results under different combinations of machining parameters.

[0043] By analyzing the point cloud of the workpiece machining planning position and the point cloud of the workpiece image contour under different combinations of machining parameters, the degree of deviation between the two can be determined. This allows for the evaluation of the machining quality under different combinations of machining parameters, facilitating subsequent optimization and iteration of the machining parameter combinations to ensure the quality of subsequent machining.

[0044] Figure 3 This is a schematic diagram of the specific process of step 106 in one embodiment.

[0045] In this embodiment, as Figure 3As shown, step 106 includes sub-steps 302 to 304.

[0046] Step 302: Based on the evaluation results of processing quality under different combinations of processing parameters and the different combinations of processing parameters, determine the probability density model of the processing parameters of the workpiece.

[0047] The probability density model of processing parameters can be a mathematical model that estimates the changing trend of processing parameter combinations based on the processing quality evaluation results under different combinations of processing parameters.

[0048] The processing parameter probability density model includes a first processing parameter probability density model and a second processing parameter probability density model. The first processing parameter probability density model can be a trend estimation model based on processing parameter combinations that are better than the median of processing quality evaluation results under different processing parameter combinations. The second processing parameter probability density model can be a trend estimation model based on processing parameter combinations that are worse than the median of processing quality evaluation results under different processing parameter combinations.

[0049] The scenarios for determining the workpiece's machining parameter probability density model based on the machining quality evaluation results under different combinations of machining parameters include: sorting and dividing different machining parameter combinations according to their evaluation results, determining a first set of machining parameters and a second set of machining parameters after sorting and dividing, where the machining quality evaluation results of the first set of machining parameters and the second set of machining parameters are different; determining the first machining parameter probability density model of the workpiece based on the first set of machining parameters after sorting and dividing; and determining the second machining parameter probability density model of the workpiece based on the second set of machining parameters after sorting and dividing.

[0050] The first set of processing parameters can be a combination of processing parameters that is better than the median of the processing quality evaluation results under different combinations of processing parameters. The second set of processing parameters can be a combination of processing parameters that is worse than the median of the processing quality evaluation results under different combinations of processing parameters.

[0051] Step 304: Based on the probability density model of processing parameters, determine the optimal combination of processing parameters for the workpiece processing process.

[0052] The scenarios for determining the optimal combination of processing parameters for a workpiece based on the probability density model of processing parameters include: determining the optimal processing parameter model for the workpiece based on the ratio of the first probability density model of processing parameters to the second probability density model of processing parameters; and determining the optimal combination of processing parameters for the workpiece based on the optimal processing parameter model of processing parameters.

[0053] Specifically, acquiring workpiece images through a machine vision system to obtain workpiece image contour point clouds under different combinations of processing parameters includes: reading the image, performing grayscale conversion, binarization, and morphological denoising to obtain a preprocessed image; finding contours in the image using an edge detection algorithm; and selecting and determining contours based on area and perimeter features; converting the extracted contour points into point cloud data, where contour points can be represented as... .

[0054] Based on the workpiece machining planning location point cloud and the workpiece image contour point cloud, the following situations determine the matching set of the workpiece location point cloud: Since the machining image and the machine vision shooting field of view are both determined during optimization, the workpiece point cloud (i.e., the workpiece image contour point cloud) can be determined by calculating the actual pixel position of the image and the template position. And standard CAD template point cloud (i.e., workpiece machining planning position point cloud). Perform initial alignment; for each point In standard CAD template point cloud Find the nearest point .

[0055] Based on the workpiece machining planning location point cloud and the workpiece image contour point cloud, the following scenarios are used to determine the workpiece point cloud error optimization model:

[0056] The rotation matrix is ​​solved by minimizing the following error function. Translation vector .

[0057] The cases in which the position point cloud transformation matrix of the workpiece is determined based on the matching set of position point clouds and the point cloud error optimization model include: based on the matched point pairs Calculate the rigid transformation matrix Rigid transformations include rotation matrices. Translation vector , can be represented as:

[0058] Based on the position point cloud transformation matrix and the workpiece image contour point cloud, the determination of the rigidly transformed workpiece image contour point cloud includes: [The text abruptly shifts to a different topic] ...the calculated transformation matrix... Application to workpiece point cloud The above yields a new workpiece image contour point cloud. That is, with the position point cloud error constraint threshold and / or iteration number constraint value as constraints, the workpiece image contour point cloud is rigidly transformed according to the position point cloud transformation matrix to determine the rigidly transformed workpiece image contour point cloud; that is, if the check error is less than a certain threshold or the maximum number of iterations is reached, the iteration stops; otherwise, the nearest point matching step is returned to continue the iteration.

[0059] The cases for determining the workpiece position point cloud deviation value based on the iteratively optimized workpiece image contour point cloud and workpiece machining planning position point cloud include: for each point and its corresponding point Calculate the Euclidean distance between them. That is, the point cloud deviation at all locations:

[0060] Calculate the distance standard deviation for all point cloud biases:

[0061] In the formula, It is the average of all deviations:

[0062] In the formula, n is the total number of point clouds, i is the index value, and d i Let be the deviation distance of the i-th point. This is the average of all deviation distances.

[0063] Based on the workpiece's position point cloud deviation, the following scenarios determine the processing quality evaluation results under different combinations of processing parameters: For processing quality, a smaller standard deviation σ is better, but the goal of the Bayesian optimization algorithm is to find a set of parameters that maximizes the corresponding y value. Therefore, the quality score (i.e., the processing quality evaluation result) is defined as y = -σ; different combinations of processing parameters θ i and its corresponding mass fraction y i Store the data in the historical dataset D to complete one iteration.

[0064] Based on the processing quality evaluation results under different combinations of processing parameters, the different processing parameter combinations are sorted and divided to determine the first and second sets of processing parameters after sorting and division. This includes using a Bayesian optimization algorithm to divide the historical dataset D into two parts based on the median performance score y: the high-performance set. (i.e., the first set of processing parameters) and the low-performance set (i.e., the second set of processing parameters); among which the high-performance set Includes data points with high performance scores; low-performance sets Includes data points with low performance scores.

[0065] The cases in which the probability density model of the first processing parameter of a workpiece is determined based on the sorted and partitioned first processing parameter set include: for each parameter KDE (Kernel Density Estimation) was used to calculate its performance on high-performance ensembles. and low-performance collection The probability density in high-performance assemblies :

[0066] In the formula, It is the number of data points in the high-performance dataset. It is the first in the high-performance collection There are several parameter points.

[0067] Furthermore, the probability density is estimated using a Gaussian kernel function, with the kernel function K in the form of:

[0068] Determine bandwidth according to Scott's rule :

[0069] In the formula, It is the standard deviation of the data. It represents the number of data points.

[0070] Based on the sorted and partitioned second set of processing parameters, determine the probability density model of the second processing parameters for the workpiece, i.e., the probability density of the low-performance set. :

[0071] In the formula, It represents the number of data points in the low-performance set.

[0072] The case where the workpiece's machining parameter optimization model is determined based on the ratio of the first machining parameter probability density model to the second machining parameter probability density model includes: using the probability density of a high-performance ensemble. l ( θ Probability density of low-performance sets g ( θ To calculate the Expected Improvement (EI) function:

[0073] This function is in l ( θ High value and g ( θ The algorithm takes the maximum value in the region with the low value, which guides the algorithm to explore the parameter space with better performance than the existing observations.

[0074] Based on the workpiece machining parameter optimization model, the optimal combination of machining parameters for the workpiece machining process can be determined in the following ways: by optimizing EI (… θ The function finds the parameter point for the next evaluation. :

[0075] The optimization loop terminates when the number of physical experiments reaches a preset maximum or when the visual quality index falls below a predetermined threshold after several consecutive iterations. The system will then output the parameter combination θ that yields the optimal processing quality from the entire historical dataset D. and its corresponding mass fraction y .

[0076] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least one sub-step described above may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps. It should be noted that the different embodiments described above can be combined with each other.

[0077] Figure 4 This is a schematic block diagram of the processing parameter optimization device in one embodiment.

[0078] In this embodiment, as Figure 4 As shown, the machining parameter optimization device is applied to machining trajectories including interpolation axes and coupling axes. The machining parameter optimization device includes an acquisition module 20, a machining quality evaluation module 40, and a machining parameter optimization module 60.

[0079] The acquisition module 20 is used to acquire the workpiece machining planning position point cloud and the workpiece image contour point cloud under different combinations of machining parameters.

[0080] The processing quality evaluation module 40, connected to the acquisition module 20, is used to determine the processing quality evaluation results under different combinations of processing parameters based on the workpiece processing planning position point cloud and the workpiece image contour point cloud.

[0081] The machining parameter optimization module 60 is connected to the machining quality evaluation module 40 and is used to determine the optimal machining parameter combination for the workpiece machining process based on the machining quality evaluation results under different machining parameter combinations and different machining parameter combinations.

[0082] In this embodiment, each module is used to execute Figure 1 For details of each step in the corresponding embodiment, please refer to the documentation. Figure 1 as well as Figure 1 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0083] The machining parameter optimization device provided in this embodiment determines, during the workpiece machining process, a machining quality evaluation result that quantitatively reflects the workpiece machining quality under different machining parameter combinations, based on the workpiece machining planning position point cloud and the workpiece image contour point cloud under different machining parameter combinations. Then, based on the obtained machining quality evaluation results under different machining parameter combinations, iterative optimization processing is performed on different machining parameter combinations to determine an optimized machining parameter combination that can provide optimization guidance for the workpiece machining process after iterative optimization. This enables effective adjustment of machining parameters during workpiece machining, improving upon the problem that traditional machining parameter adjustment processes rely on engineers' experience and repeated trials, which are time-consuming, labor-intensive, and difficult to guarantee machining efficiency. It can ensure both workpiece machining efficiency and the accuracy and adjustability of the machining process, thereby meeting higher requirements for CNC machining effects.

[0084] Figure 5 This is a schematic block diagram of the specific structure of the processing quality evaluation module 40 in one embodiment.

[0085] In this embodiment, as Figure 5 As shown, the processing quality evaluation module 40 includes a transformation matrix determination unit 420, a contour point cloud determination unit 440, and a processing quality evaluation unit 460.

[0086] The transformation matrix determination unit 420 is used to determine the transformation matrix of the workpiece's position point cloud based on the workpiece machining planning position point cloud and the workpiece image contour point cloud.

[0087] The contour point cloud determination unit 440 is connected to the transformation matrix determination unit 420 and is used to determine the rigidly transformed workpiece image contour point cloud based on the position point cloud transformation matrix and the workpiece image contour point cloud.

[0088] The processing quality evaluation unit 460 is connected to the contour point cloud determination unit 440 and is used to determine the processing quality evaluation results under different combinations of processing parameters based on the iteratively optimized workpiece image contour point cloud and workpiece processing planning position point cloud.

[0089] In this embodiment, each unit is used to perform Figure 2 For details of each step in the corresponding embodiment, please refer to the documentation. Figure 2 as well as Figure 2 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0090] Figure 6 This is a schematic block diagram of the specific structure of the processing parameter optimization module 60 in one embodiment.

[0091] In this embodiment, as Figure 6 As shown, the processing parameter optimization module 60 includes a density model determination unit 620 and a processing parameter optimization unit 640.

[0092] The density model determination unit 620 is used to determine the probability density model of the workpiece's processing parameters based on the processing quality evaluation results under different combinations of processing parameters and different combinations of processing parameters.

[0093] The processing parameter optimization unit 640 is connected to the density model determination unit 620 and is used to determine the optimal combination of processing parameters for the workpiece processing process based on the processing parameter probability density model.

[0094] In this embodiment, each unit is used to perform Figure 3 For details of each step in the corresponding embodiment, please refer to the documentation. Figure 3 as well as Figure 3 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0095] The units in this embodiment are used to perform the steps in the corresponding embodiments described above. For details, please refer to the relevant descriptions in the corresponding embodiments described above, which will not be repeated here.

[0096] The division of the various modules in the above-described processing parameter optimization device is only for illustrative purposes. In other embodiments, the processing parameter optimization device can be divided into different modules as needed to complete all or part of the functions of the above-described processing parameter optimization device.

[0097] Specific limitations regarding the machining parameter optimization device can be found in the limitations of the machining parameter optimization method described above, and will not be repeated here. Each module in the aforementioned machining parameter optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the machining equipment in hardware form or independent of it, or stored in the memory of the machining equipment in software form, so that the processor can call and execute the operations corresponding to each module.

[0098] Figure 7 This is a schematic diagram of the processing equipment in one embodiment.

[0099] In this embodiment, as Figure 7 As shown, the processing equipment includes a memory A1 and a processor A2; it may also include a display screen A3, a communication interface, and a bus. Optionally, the processing equipment may be a CNC machining equipment.

[0100] The memory A1, processor A2, display screen A3, and communication interface can communicate with each other via a bus; the display screen A3 is configured to display the user operation interface preset in the initial setting mode, and the display screen A3 can also display the process control window; the communication interface can transmit information; the memory A1 stores computer programs, and the processor A2 can call the logical instructions in the memory A1 to execute the methods in the above embodiments.

[0101] Furthermore, the logic instructions in the aforementioned memory A1 can be implemented as software functional units and, when sold or used as independent workpieces, can be stored in a computer-readable storage medium.

[0102] Memory A1, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of this application. Processor A2 executes functional applications and data processing by running the software programs, instructions, or modules stored in memory A1, thereby implementing the methods in the above embodiments.

[0103] Memory A1 includes a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, memory A1 may include high-speed random access memory and may also include non-volatile memory.

[0104] Processor A2 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0105] This application also provides a computer-readable storage medium. One or more non-volatile computer-readable storage media containing computer-executable instructions, which, when executed by one or more processors, cause the processors to perform the methods described above.

[0106] This application also provides a computer program product that, when run on a terminal device, causes the terminal device to execute the methods described in the above embodiments.

[0107] The processing parameter optimization method, apparatus, processing equipment, and readable storage medium provided in the above embodiments, during the workpiece processing, determine the processing quality evaluation results that quantitatively reflect the workpiece processing quality under different processing parameter combinations based on the workpiece processing planning position point cloud and the workpiece image contour point cloud under different processing parameter combinations. Based on the obtained processing quality evaluation results under different processing parameter combinations, iterative optimization processing is performed on different processing parameter combinations to determine the optimized processing parameter combination that can guide the workpiece processing process after iterative optimization. This enables effective adjustment of processing parameters during workpiece processing, improving the problem that traditional processing parameter adjustment relies on engineers' experience and repeated trials, which is time-consuming, labor-intensive, and difficult to guarantee processing efficiency. It can ensure both workpiece processing efficiency and the accuracy and adjustability of the workpiece processing process, thereby meeting higher requirements for CNC machining effects, and has significant economic and practical value.

[0108] Any references to memory, storage, databases, or other media used in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which is used as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for optimizing processing parameters, characterized in that, include: Acquire the workpiece machining planning position point cloud and the workpiece image contour point cloud under different combinations of machining parameters; Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, determine the machining quality evaluation results under the action of different combinations of machining parameters; Based on the evaluation results of the processing quality under different combinations of processing parameters and the different combinations of processing parameters, the optimal combination of processing parameters for the workpiece processing process is determined.

2. The processing parameter optimization method according to claim 1, characterized in that, The step of determining the processing quality evaluation results under different combinations of processing parameters based on the workpiece processing planning position point cloud and the workpiece image contour point cloud includes: Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, determine the workpiece position point cloud transformation matrix; Based on the position point cloud transformation matrix and the workpiece image contour point cloud, the iteratively optimized workpiece image contour point cloud is determined. Based on the iteratively optimized workpiece image contour point cloud and the workpiece machining planning position point cloud, the machining quality evaluation results under the action of different combinations of machining parameters are determined.

3. The processing parameter optimization method according to claim 2, characterized in that, The step of determining the workpiece's position point cloud transformation matrix based on the workpiece machining planning position point cloud and the workpiece image contour point cloud includes: Based on the workpiece machining planning position point cloud and the workpiece image contour point cloud, determine the workpiece position point cloud matching set and point cloud error optimization model; The position point cloud transformation matrix of the workpiece is determined based on the position point cloud matching set and the point cloud error optimization model. The step of determining the iteratively optimized workpiece image contour point cloud based on the position point cloud transformation matrix and the workpiece image contour point cloud includes: Obtain the location point cloud error constraint threshold and iteration number constraint value; Using the position point cloud error constraint threshold as a constraint condition, the workpiece image contour point cloud is iteratively optimized according to the position point cloud transformation matrix to determine the iteratively optimized workpiece image contour point cloud.

4. The processing parameter optimization method according to claim 2, characterized in that, The step of determining the processing quality evaluation results under different combinations of processing parameters based on the iteratively optimized workpiece image contour point cloud and the workpiece processing planning position point cloud includes: Based on the iteratively optimized workpiece image contour point cloud and the workpiece machining planning position point cloud, the position point cloud deviation value of the workpiece is determined; Based on the position point cloud deviation value of the workpiece, the processing quality evaluation result under the action of different processing parameter combinations is determined.

5. The method for optimizing processing parameters according to any one of claims 1 to 4, characterized in that, The step of determining the optimal combination of processing parameters for the workpiece processing process based on the processing quality evaluation results under different combinations of processing parameters and the different combinations of processing parameters includes: Based on the processing quality evaluation results under different combinations of processing parameters and the different combinations of processing parameters, the probability density model of the processing parameters of the workpiece is determined; Based on the probability density model of the processing parameters, the optimal combination of processing parameters for the workpiece processing process is determined.

6. The processing parameter optimization method according to claim 5, characterized in that, The processing parameter probability density model includes a first processing parameter probability density model and a second processing parameter probability density model. Determining the processing parameter probability density model of the workpiece based on the processing quality evaluation results under different processing parameter combinations and the different processing parameter combinations includes: Based on the processing quality evaluation results under different processing parameter combinations, the different processing parameter combinations are sorted and divided to determine the first processing parameter set and the second processing parameter set after sorting and division. The processing quality evaluation results of the first processing parameter set and the second processing parameter set are different. Based on the sorted and divided first set of processing parameters, determine the probability density model of the first processing parameters of the workpiece; Based on the sorted and divided second set of processing parameters, the probability density model of the second processing parameters of the workpiece is determined.

7. The processing parameter optimization method according to claim 6, characterized in that, The step of determining the optimal combination of processing parameters for the workpiece processing process based on the processing parameter probability density model includes: The optimal model for the processing parameters of the workpiece is determined based on the ratio of the probability density model of the first processing parameter to the probability density model of the second processing parameter. Based on the workpiece's machining parameter optimization model, the optimal combination of machining parameters for the workpiece machining process is determined.

8. A processing parameter optimization device, characterized in that, include: The acquisition module is used to acquire the workpiece machining planning position point cloud and the workpiece image contour point cloud under different combinations of machining parameters; A processing quality evaluation module, connected to the acquisition module, is used to determine the processing quality evaluation results under the action of different processing parameter combinations based on the workpiece processing planning position point cloud and the workpiece image contour point cloud. The processing parameter optimization module is connected to the processing quality evaluation module and is used to determine the optimal processing parameter combination for the workpiece processing process based on the processing quality evaluation results under the action of different processing parameter combinations and the different processing parameter combinations.

9. A processing equipment, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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