Machining control method, equipment, system and medium based on five-axis machining center

Abstract

This application discloses a machining control method, device, system, and medium based on a five-axis machining center. The method includes obtaining geometric feature parameters and tool feature information of a workpiece to be machined, establishing a cutting force model based on the obtained geometric feature parameters, and calculating the cutting force distribution under different paths; in the cutting force model, using a path optimization algorithm to generate an optimal tool path corresponding to the tool feature information based on the cutting force distribution and geometric feature parameters; establishing a simulation environment for the five-axis machining center based on the generated tool path and tool feature information to simulate the motion state of the tool in different paths; setting a reward function including path smoothness and cutting force stability in the simulation environment; using a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path; verifying the generated smooth tool path in the simulation environment; and triggering a machining execution instruction. This application improves machining accuracy and surface quality.

Description

Technical Field

[0001] The present application relates to the field of CNC machining technology, and in particular to a machining control method, equipment, system and medium based on a five-axis machining center. Background Art

[0002] With the development of modern manufacturing, five-axis machining centers are widely used in aerospace, automobile manufacturing, medical equipment and other fields due to their high precision, high efficiency and powerful machining capabilities. Especially in the machining of complex curved parts, five-axis machining centers can achieve multi-angle and multi-directional machining, greatly improving machining quality and production efficiency.

[0003] Existing five-axis machining centers typically use traditional tool path planning and posture optimization methods in the processing of complex curved parts. These methods mainly rely on empirical parameters and simple mathematical models. Although they can meet the processing requirements to a certain extent, there is still much room for improvement in terms of accuracy and surface quality.

[0004] Moreover, the existing tool path planning methods are difficult to accurately generate tool trajectories, resulting in overcutting or undercutting; the tool path is not smooth enough, resulting in unstable tool movement during processing, producing vibration marks and tool marks, which affect the surface quality. Summary of the Invention

[0005] In order to solve the problems of inaccurate tool path planning and smoothness of tool paths in the processing of complex curved surface parts by existing five-axis machining centers, and to improve machining accuracy and surface quality, the present application provides a machining control method, equipment, system and medium based on a five-axis machining center.

[0006] In the first aspect, the invention objectives of this application are achieved by adopting the following technical solutions:

[0007] The machining control method based on the five-axis machining center includes:

[0008] Obtain the geometric characteristic parameters of the workpiece and the tool characteristic information, establish a cutting force model based on the obtained geometric characteristic parameters, and calculate the cutting force distribution under different paths;

[0009] In the cutting force model, based on the cutting force distribution and geometric characteristic parameters, a path optimization algorithm is used to generate an optimal tool path corresponding to the tool characteristic information;

[0010] According to the generated tool path and the tool feature information, a simulation environment of a five-axis machining center is established to simulate the motion state of the tool under different paths;

[0011] In the simulation environment, a reward function including path smoothness and cutting force stability is set; a reinforcement learning algorithm is used to optimize the smoothness of the tool path and generate a smooth tool path;

[0012] The generated smooth tool path is verified in the simulation environment; the verified smooth tool path is transmitted to the five-axis machining center, and a machining execution instruction is triggered.

[0013] By adopting the above technical solution, the cutting force model describes the relationship between the force between the tool and the workpiece during the cutting process. By calculating the cutting force distribution under different paths, the change of cutting force under different tool paths is analyzed, providing a scientific basis for path optimization based on the current workpiece to be processed and the processing tool, which is beneficial to reducing overcutting and undercutting, and improving the smoothness and dimensional accuracy of the processed surface. The path optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm. The smoothness of the processing path and the stability of the cutting force are improved through path optimization. At the same time, through the simulation environment and the design reward function, the feasibility of the path can be detected in the virtual environment, the smoothness of the tool path can be optimized, and errors and risks in actual processing can be effectively avoided. Through the reinforcement learning algorithm, the path can be continuously optimized to generate a smooth tool path, reduce vibration and impact during the processing process, and through simulation verification, ensure that the generated smooth path meets the processing standard process requirements, and improve the processing surface quality and efficiency. Therefore, the present invention achieves the purpose of solving the problems of inaccurate tool path planning and smoothness of the tool path in the processing of complex curved surface parts of existing five-axis machining centers.

[0014] In a preferred example of the present application, in the simulation environment, a reward function including path smoothness and cutting force stability is set; and after using a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path, the method further includes:

[0015] In a preset multi-objective optimization model, multiple optimization objectives are set, wherein the multiple optimization objectives include minimizing cutting force, maximizing tool life, and minimizing surface roughness;

[0016] In combination with the tool feature information, the tool posture is parameterized to obtain tool posture parameters; based on the smooth tool path and the tool control parameters based on the time series, an initial tool posture trajectory is determined;

[0017] In the multi-objective optimization model, a multi-objective optimization algorithm is adopted to optimize tool posture parameters based on the initial tool posture trajectory to obtain an optimized tool posture trajectory.

[0018] By adopting the above-mentioned technical solution, the present invention also tracks, monitors and optimizes the tool posture of the tool performing the processing. Based on the consideration of the changing factors of the tool posture, it is conducive to providing a processing control solution with higher precision and more comprehensive detection; specifically, the multi-objective optimization model is used to evaluate and optimize multiple performance indicators, and multiple optimization targets are formulated based on the high-precision standard process parameter requirements of the five-axis processing workpiece for monitoring and optimization, so as to analyze and improve the control solution that can provide higher precision in the five-axis processing machine tool and track, detect and optimize the optimizable area; the present invention ensures the comprehensiveness of the optimization in the machine tool processing process by setting multiple optimization targets, minimizes the cutting force fluctuation to improve the processing stability, maximizes the tool life to reduce the production cost, and minimizes the surface roughness to improve the processing quality. By optimizing the tool posture parameters, the present invention can reduce tool wear, reduce the surface roughness of the workpiece, improve the processing quality, and meet the high-quality processing requirements.

[0019] In a preferred example of the present application, in the simulation environment, setting a reward function including path smoothness and cutting force stability includes:

[0020] In the simulation environment, a corresponding reward function objective is defined according to a plurality of preset reward function component factors based on the generated tool path and simulation environment parameters;

[0021] Assigning weights to a plurality of the reward function component factors;

[0022] Construct a multi-objective reward function based on the defined reward function objectives and weights.

[0023] By adopting the above technical solution, the simulation environment is a virtual five-axis machining machine model used to simulate the movement of the tool and the machining process. Through multiple reward function components and their objectives, multiple performance indicators of the tool path can be evaluated to ensure the comprehensiveness and effectiveness of the path optimization process. By assigning an importance coefficient to each reward function component factor, it is used to balance the priorities between different objectives and clarify the optimization target, which can ensure that the optimization process can be optimized and improved in a targeted manner, so that the simulation environment is conducive to assisting in path pros and cons evaluation and iterative optimization of the algorithm for efficient performance evaluation and improvement.

[0024] In a preferred example of the present application, the multiple reward function components include a path smoothness factor, a cutting force stability factor, a machining time factor, and a surface quality factor; and constructing a multi-objective reward function based on the defined reward function objectives and weights includes:

[0025] The multi-objective reward function is expressed as formula (1):

[0026] R=w1R smo+w2R for +w3R t +w4R q (1)

[0027] Among them, w1, w2, w3, w4 are weight coefficients, R smo is the path smoothness factor, R for is the cutting force stability factor, R t is the processing time factor, R q is the surface quality factor.

[0028] By adopting the above technical solution, path smoothness refers to minimizing the path's roughness and reducing sudden changes in the path; cutting force stability refers to minimizing cutting force fluctuations and improving the stability of the cutting process; processing time refers to minimizing the total processing time and improving processing efficiency; surface quality refers to minimizing surface roughness and improving the quality of the processed surface. The present invention can assign weights to each component of the reward function based on actual processing requirements and priorities. By constructing a multi-objective reward function, it can comprehensively evaluate multiple performance indicators of the path to ensure the comprehensiveness and optimality of the optimization results. The multi-objective reward function can effectively guide the optimization algorithm to find the optimal balance between multiple objectives, improve processing accuracy and surface quality, and reduce processing time.

[0029] In a preferred embodiment of the present application, the method further includes:

[0030] The path smoothness factor R is defined according to the reward function objective defined smo The reward function is expressed as formula (2):

[0031]

[0032] Among them, p i is the i-th point on the path, ||.|| represents the Euclidean distance, and n is the number of points on the path;

[0033] The cutting force stability factor R is defined according to the reward function objective for The reward function is expressed as formula (3):

[0034]

[0035] Among them, F i is the cutting force at the i-th point, is the average cutting force;

[0036] The processing time factor R is defined according to the reward function objective t The reward function is expressed as formula (4):

[0037]

[0038] Among them, t i is the processing time at the i-th point;

[0039] The surface quality factor R is defined according to the reward function objective defined q The reward function is expressed as formula (5)

[0040] Among them, σ i is the surface roughness at the i-th point.

[0041] By adopting the above technical solution, the path smoothness factor calculates the second-order difference of adjacent points on the path, which can quantify the smoothness of the path. The path smoothness factor R smo Taking a negative value makes the path smoother and the reward value larger; cutting force stability factor R for : By calculating the standard deviation of the cutting force, the fluctuation of the cutting force can be quantified; the cutting force stability factor R for Taking a negative value makes the cutting force more stable and the bonus value larger; the processing time factor R t By calculating the processing time of each point on the path, the total processing time can be quantified; the processing time factor R t Taking a negative value makes the shorter the processing time, the greater the reward value; the surface quality factor R q The surface quality can be quantified by calculating the surface roughness at each point on the path; the surface quality factor R q Taking a negative value makes the reward value larger the smaller the surface roughness.

[0042] In a preferred embodiment of the present application, after transmitting the verified smooth tool path to the five-axis machining center and triggering the machining execution instruction, the process further includes:

[0043] Acquiring machining task parameters and a tool path based on the machining execution instruction, and acquiring three-dimensional position information of the workpiece to be machined and the tool; matching the three-dimensional position information with the starting point coordinates of the tool path, and machining the workpiece to be machined;

[0044] During the machining process, the relative position information between the tool and the workpiece to be machined is obtained in real time through the preset sensors of the machine tool, and the real-time cutting parameters and machine tool performance parameters are calculated. The relative position information includes distance and angle;

[0045] Based on the real-time cutting parameters and the machine tool performance parameters, a machine tool processing status result is obtained; based on the comparison between the machine tool processing status result and a preset threshold value, a processing parameter comparison result is obtained.

[0046] By adopting the above technical solution, the processing task parameters include processing speed, feed speed, cutting depth, etc.; the three-dimensional position information is the position information of the workpiece to be processed and the tool in three-dimensional space; the starting point coordinates refer to the coordinates of the starting point of the tool path in three-dimensional space; relative position information: including the distance and angle between the tool and the workpiece to be processed; the present invention obtains the processing task parameters and the tool path to ensure that the parameters and paths of the processing process are accurate, and provides accurate data support for subsequent matching and processing; and by matching the three-dimensional position information and the starting point coordinates of the tool path, it is ensured that the tool starts processing from the correct starting point to avoid processing errors caused by inaccurate starting points; then, by obtaining the relative position information between the tool and the workpiece to be processed in real time, the processing process can be dynamically adjusted to ensure the accuracy and stability of the processing. Finally, through the generated machine tool processing status results, the status of the processing process can be comprehensively evaluated to ensure the stability and quality of the processing; by comparing the machine tool processing status results with the preset threshold, abnormal situations can be discovered and handled in time to ensure the smooth progress of the processing process.

[0047] In the second aspect, the invention objective of this application is achieved by adopting the following technical solutions:

[0048] A machining control system based on a five-axis machining center is applied to the machining control method based on the five-axis machining center as described above, and the system comprises:

[0049] The parameter acquisition module is used to obtain the geometric characteristic parameters of the workpiece to be processed and the tool characteristic information, establish a cutting force model based on the obtained geometric characteristic parameters, and calculate the cutting force distribution under different paths;

[0050] an initial path optimization module, configured to generate an optimal tool path corresponding to tool feature information using a path optimization algorithm in the cutting force model based on the cutting force distribution and geometric feature parameters;

[0051] A simulation environment establishment module is used to establish a simulation environment for a five-axis machining center based on the generated tool path and the tool feature information, and simulate the motion state of the tool under different paths;

[0052] A path optimization smoothing module is used to set a reward function including path smoothness and cutting force stability in the simulation environment; use a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path;

[0053] The simulation verification execution module is used to verify the generated smooth tool path in the simulation environment; transmit the verified smooth tool path to the five-axis machining center, and trigger a machining execution instruction.

[0054] By adopting the above-mentioned technical solution, this application calculates the cutting force distribution under different paths to analyze the changes in cutting force under different tool paths, providing a scientific basis for path optimization based on the current workpiece to be processed and processing tool, which is beneficial to reduce overcutting and undercutting, and improve the smoothness and dimensional accuracy of the processed surface. The path optimization algorithm includes genetic algorithm and particle swarm optimization algorithm. The smoothness of the processing path and the stability of the cutting force are improved through path optimization. At the same time, through the simulation environment and the design reward function, the feasibility of the path can be detected in the virtual environment, the smoothness of the tool path can be optimized, and the errors and risks in actual processing can be effectively avoided; through the reinforcement learning algorithm, the path can be continuously optimized, a smooth tool path can be generated, and vibration and impact during the processing process can be reduced. Through simulation verification, it is ensured that the generated smooth path meets the processing standard process requirements and improves the processing surface quality and efficiency.

[0055] In a preferred embodiment of the present application, the system further includes:

[0056] A multi-objective optimization module is used to set multiple optimization objectives in a preset multi-objective optimization model, wherein the multiple optimization objectives include minimizing cutting force, maximizing tool life, and minimizing surface roughness;

[0057] An initial posture trajectory determination module is used to parameterize the tool posture in combination with the tool feature information to obtain tool posture parameters; and to determine the initial tool posture trajectory based on a smooth tool path and a tool control parameter based on a time series;

[0058] The posture trajectory optimization module is used to optimize the tool posture parameters based on the initial tool posture trajectory in the multi-objective optimization model by adopting a multi-objective optimization algorithm to obtain an optimized tool posture trajectory.

[0059] By adopting the above-mentioned technical solution, the present invention also tracks, monitors and optimizes the tool posture of the tool performing the processing. Based on the consideration of the changing factors of the tool posture, it is beneficial to provide a processing control solution with higher precision and more comprehensive detection; specifically, the multi-objective optimization model is used to evaluate and optimize multiple performance indicators. By correspondingly formulating multiple optimization targets based on the high-precision standard process parameter requirements of the five-axis processing workpiece, monitoring and optimization are carried out, so as to analyze and improve the control solution that can provide higher precision in the five-axis processing machine tool and track, detect and optimize the optimizable area.

[0060] In a third aspect, the invention objective of this application is achieved by adopting the following technical solutions:

[0061] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the machining control method based on a five-axis machining tool are implemented.

[0062] Fourthly, the invention objectives of this application are achieved by adopting the following technical solutions:

[0063] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned machining control method based on a five-axis machining center.

[0064] In summary, this application includes at least one of the following beneficial technical effects:

[0065] 1. This application calculates the cutting force distribution under different paths to analyze the changes in cutting force under different tool paths, providing a scientific basis for path optimization based on the current workpiece to be processed and the processing tool, which is conducive to reducing overcutting and undercutting, and improving the smoothness and dimensional accuracy of the processed surface. The path optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm. Through path optimization, the smoothness of the processing path and the stability of the cutting force are improved. At the same time, through the simulation environment and the design reward function, the feasibility of the path can be detected in the virtual environment, the smoothness of the tool path can be optimized, and errors and risks in actual processing can be effectively avoided. Through the reinforcement learning algorithm, the path can be continuously optimized to generate a smooth tool path, reduce vibration and impact during the processing process, and through simulation verification, it is ensured that the generated smooth path meets the processing standard process requirements and improves the processing surface quality and efficiency.

[0066] 2. The present invention also tracks, monitors, and optimizes the tool posture of the tool performing the machining. Taking into account factors affecting tool posture, this approach facilitates the development of a machining control solution with higher precision and more comprehensive detection. Specifically, a multi-objective optimization model is used to evaluate and optimize multiple performance indicators. By developing and optimizing multiple optimization objectives based on the high-precision standard process parameters required for five-axis machining of workpieces, this model facilitates the analysis and improvement of control solutions that provide higher precision in five-axis machining centers and the tracking, detection, and target optimization of optimizable areas.

[0067] 3. The present invention obtains the processing task parameters and tool path to ensure that the parameters and path of the processing process are accurate, providing accurate data support for subsequent matching and processing; and by matching the three-dimensional position information and the starting point coordinates of the tool path, it ensures that the tool starts processing from the correct starting point, avoiding processing errors caused by inaccurate starting points; then, by obtaining the relative position information between the tool and the workpiece to be processed in real time, the processing process can be dynamically adjusted to ensure the accuracy and stability of the processing, and finally, through the generated machine tool processing status results, the status of the processing process can be comprehensively evaluated to ensure the stability and quality of the processing; by comparing the machine tool processing status results with the preset threshold, abnormal situations can be discovered and handled in time to ensure the smooth progress of the processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of a machining control method based on a five-axis machining center in one embodiment of the present application;

[0069] Figure 2 This is a flowchart after step S4 in the machining control method based on a five-axis machining center in one embodiment of the present application;

[0070] Figure 3 This is a flowchart of step S4 in the machining control method based on a five-axis machining center in one embodiment of the present application;

[0071] Figure 4 This is a flowchart after step S5 in the machining control method based on a five-axis machining center in one embodiment of the present application;

[0072] Figure 5 It is a schematic diagram of a device in one embodiment of the present application. DETAILED DESCRIPTION

[0073] The present application is further described in detail below with reference to the accompanying drawings.

[0074] In one embodiment, if Figure 1 As shown, the present application discloses a processing control method based on a five-axis machining center, which specifically includes the following steps:

[0075] S1: Obtain the geometric characteristic parameters of the workpiece and the tool characteristic information, establish a cutting force model based on the obtained geometric characteristic parameters, and calculate the cutting force distribution under different paths.

[0076] In this embodiment, the geometric feature parameters include size volume, curvature, normal vector, surface roughness, etc.; the tool feature information includes the type, diameter, length and material of the tool; the cutting force distribution refers to the change of cutting force under different tool paths.

[0077] Specifically, cutting parameters such as cutting speed, feed rate, cutting depth, etc. are input; then, based on the input cutting parameters, a cutting force model is constructed, taking into account factors such as material properties and tool type. The cutting force model is used to describe the force relationship between the tool and the workpiece during the process.

[0078] S2: In the cutting force model, based on the cutting force distribution and geometric characteristic parameters, the path optimization algorithm is used to generate the optimal tool path corresponding to the tool feature information.

[0079] Specifically, an initial tool path is generated as the starting point for optimization; optimization goals are set, such as minimizing overcutting and undercutting to ensure the smoothness and dimensional accuracy of the machined surface; then an optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.) is applied to combine geometric features and cutting force models to generate the optimal tool path.

[0080] S3: Based on the generated tool path and tool feature information, a simulation environment of the five-axis machining center is established to simulate the motion state of the tool under different paths.

[0081] In this embodiment, the simulation environment refers to a virtual five-axis machining machine model, which is used to simulate the movement of a tool and the machining process.

[0082] Specifically, the parameters of the five-axis CNC machine tool are input, such as the travel range and maximum speed of each axis; based on the input machine tool parameters, a simulation environment of the five-axis CNC machine tool is constructed, taking into account the dynamic characteristics of the machine tool.

[0083] S4: In the simulation environment, set a reward function including path smoothness and cutting force stability; use a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path.

[0084] In this embodiment, smoothness refers to the smoothness of the path and the reduction of sudden changes in the path.

[0085] Specifically, first initialize the simulation environment, set the initial state and initial path, and then define the state space and action space. The state space includes tool position, posture, cutting force, etc.; the action space includes path adjustment, posture adjustment, etc., and then strengthen learning training, that is, use reinforcement learning algorithms (such as DQN or PPO) to optimize the smoothness of the tool path through interaction with the environment and generate a smooth tool path.

[0086] S5: Verify the generated smooth tool path in the simulation environment; transmit the verified smooth tool path to the five-axis machining center and trigger the machining execution instruction.

[0087] Specifically, the generated tool path is verified in a virtual environment to ensure the feasibility of the path; through simulation, the path smoothness, cutting force stability and other indicators are checked to ensure that the path meets the processing requirements, and the verified smooth tool path is transmitted to the five-axis machining center; through simulation verification, it is ensured that the generated smooth tool path meets the processing requirements, avoiding errors and risks in actual processing, and improving the success rate and reliability of processing.

[0088] In one embodiment, if Figure 2 As shown, after step S4, the processing control method based on the five-axis machining center further includes:

[0089] S501: In a preset multi-objective optimization model, multiple optimization objectives are set, where the multiple optimization objectives include minimizing cutting force, maximizing tool life, and minimizing surface roughness.

[0090] Specifically, the optimization objectives are defined, including minimizing cutting force fluctuations, maximizing tool life, and minimizing surface roughness; weights are assigned to each optimization objective and adjusted according to actual processing requirements; a multi-objective optimization model is constructed, integrating the optimization objectives and weights into a comprehensive optimization function.

[0091] S502: Parameterizing the tool posture in combination with the tool feature information to obtain tool posture parameters; determining an initial tool posture trajectory based on a smooth tool path and a tool control parameter based on a time series.

[0092] In this embodiment, the tool posture includes parameters such as the rake angle and the side rake angle.

[0093] S503: In the multi-objective optimization model, a multi-objective optimization algorithm is used to optimize the tool posture parameters based on the initial tool posture trajectory to obtain an optimized tool posture trajectory.

[0094] In this embodiment, the multi-objective optimization algorithm includes NSGA-II or MOEA / D.

[0095] Specifically, the multi-objective optimization algorithm is initialized and the initial parameters are set; in the multi-objective optimization model, multi-objective optimization is performed based on the initial tool posture trajectory; the tool posture parameters are optimized to obtain the optimized tool posture trajectory; and the optimized tool posture trajectory is verified in a simulation environment to ensure its feasibility.

[0096] In one embodiment, if Figure 3 As shown, in step S4, in the simulation environment, a reward function including path smoothness and cutting force stability is set, including:

[0097] S41: In the simulation environment, a corresponding reward function objective is defined according to the generated tool path and simulation environment parameters and a plurality of preset reward function component factors.

[0098] In this embodiment, the reward function component factors are specific indicators that constitute the reward function; the reward function target definition is to set specific goals for each reward function component factor, such as minimizing path roughness and minimizing cutting force fluctuation.

[0099] Specifically, in the simulation environment, the generated tool path and simulation environment parameters are first loaded, such as the travel range and maximum speed of each axis of the five-axis machining center; then multiple reward function components are preset, such as path smoothness, cutting force stability, processing time, surface roughness, etc.; then specific goals are set for each reward function component, for example: path smoothness refers to minimizing the roughness of the path and reducing the mutation points in the path; cutting force stability refers to minimizing the fluctuation of the cutting force and improving the stability of the cutting process; processing time refers to minimizing the total processing time and improving processing efficiency; surface roughness refers to minimizing the surface roughness and improving the quality of the processed surface.

[0100] S42: Assign weights to multiple reward function components.

[0101] In this embodiment, the weight refers to the importance coefficient assigned to each reward function component factor, which is used to balance the priorities between different objectives.

[0102] S43: Construct a multi-objective reward function based on the defined reward function objectives and weights.

[0103] In this embodiment, the multi-objective reward function refers to a reward function that comprehensively evaluates the quality of a path by integrating multiple reward function component factors and weights.

[0104] Specifically, in a simulation environment, the constructed multi-objective reward function is used to evaluate the pros and cons of different paths and guide the iterative process of the optimization algorithm.

[0105] In this embodiment, multiple reward function components include path smoothness factor, cutting force stability factor, machining time factor, and surface quality factor. Based on the defined reward function objectives and weights, a multi-objective reward function is constructed, including:

[0106] The multi-objective reward function is expressed as formula (1):

[0107] R=w1R smo +w2R for +w3R t +w4R q (1)

[0108] Among them, w1, w2, w3, w4 are weight coefficients, R smo is the path smoothness factor, R foris the cutting force stability factor, R t is the processing time factor, R q is the surface quality factor.

[0109] Specifically, the path smoothness factor R is defined according to the reward function objective defined smo The reward function is expressed as formula (2):

[0110]

[0111] Among them, p i is the i-th point on the path, ||.|| represents the Euclidean distance, and n is the number of points on the path;

[0112] The cutting force stability factor R is defined according to the reward function objective for The reward function is expressed as formula (3):

[0113]

[0114] Among them, F i is the cutting force at the i-th point, is the average cutting force;

[0115] The processing time factor R is defined according to the reward function objective t The reward function is expressed as formula (4):

[0116]

[0117] Among them, t i is the processing time at the i-th point;

[0118] The surface quality factor R is defined according to the reward function objective defined q The reward function is expressed as formula (5)

[0119] Among them, σ i is the surface roughness at the i-th point.

[0120] In this embodiment, the path smoothness factor R smo It is an indicator to measure the smoothness of the path. It quantifies the roughness of the path by calculating the second-order difference of adjacent points on the path. The cutting force stability factor R for It is an indicator to measure the stability of cutting force. The fluctuation of cutting force is quantified by calculating the standard deviation of cutting force. The processing time factor R t It is an indicator to measure the processing time. It quantifies the total processing time by calculating the processing time of each point on the path; the surface quality factor R qIt is an indicator to measure surface quality, and the surface quality is quantified by calculating the surface roughness of each point on the path; multiple reward function components are preset, such as path smoothness, cutting force stability, processing time, surface quality, etc.; then specific goals are set for each reward function component, for example: path smoothness: minimize the roughness of the path and reduce the mutation points in the path; cutting force stability: minimize the fluctuation of the cutting force and improve the stability of the cutting process; processing time: minimize the total processing time and improve processing efficiency; surface quality: minimize the surface roughness and improve the quality of the processed surface.

[0121] In one embodiment, if Figure 4 As shown, after step S5, the processing control method based on the five-axis machining center further includes:

[0122] S511: Obtaining machining task parameters and tool paths based on machining execution instructions, and obtaining three-dimensional position information of the workpiece to be machined and the tool.

[0123] In this embodiment, the processing task parameters include processing speed, feed speed, cutting depth, etc.; the tool path is an optimized smooth tool path used to guide the movement of the machine tool; and the three-dimensional position information is the position information of the workpiece to be processed and the tool in three-dimensional space.

[0124] Specifically, the processing execution instructions are received and processing task parameters such as processing speed, feed speed, cutting depth, etc. are extracted from them; the optimized smooth tool path is obtained from the processing execution instructions; and a three-dimensional scanner or other positioning equipment is used to obtain the position information of the workpiece to be processed and the tool in three-dimensional space.

[0125] S512: Matching is performed based on the three-dimensional position information and the starting point coordinates of the tool path, and the workpiece is processed.

[0126] Specifically, the starting point coordinates refer to the coordinates of the starting point of the tool path in three-dimensional space; the acquired three-dimensional position information of the workpiece and the tool is matched with the starting point coordinates of the tool path to ensure that the tool starts processing from the correct starting point, and controls the five-axis machining center to perform processing according to the optimized tool path.

[0127] S513: During the machining process, the relative position information between the tool and the workpiece to be machined is obtained in real time through the preset sensors of the machine tool, and the real-time cutting parameters and machine tool performance parameters are calculated. The relative position information includes distance and angle.

[0128] In this embodiment, the preset sensor refers to a sensor installed on the machine tool, which is used to monitor the relative position information between the tool and the workpiece in real time; the real-time cutting parameters refer to parameters such as cutting force and cutting speed measured in real time during the machining process; the relative position information includes the distance and angle between the tool and the workpiece;

[0129] Specifically, during the machining process, the relative position information between the tool and the workpiece to be machined, including distance and angle, is obtained in real time through preset sensors; based on the real-time position information, real-time cutting parameters such as cutting force and cutting speed are calculated; and the performance parameters of the machine tool, such as vibration and temperature, are monitored.

[0130] S514: Obtain a machine tool processing status result based on the real-time cutting parameters and the machine tool performance parameters; and obtain a processing parameter comparison result based on the machine tool processing status result and a preset threshold value.

[0131] In this embodiment, the machine tool processing status result is a processing status obtained based on real-time cutting parameters and machine tool performance parameters; the preset threshold refers to a pre-set standard value for evaluating the processing status; the processing parameter comparison result is the result obtained by comparing the machine tool processing status result with the preset threshold.

[0132] Specifically, based on real-time cutting parameters and machine tool performance parameters, machine tool processing status results, such as cutting force stability and vibration, are generated. These results are then compared with preset thresholds to assess whether the machining process is normal. Based on these comparisons, machining parameter comparison results are generated for monitoring and adjusting the machining process. By generating these machine tool processing status results, the state of the machining process can be comprehensively assessed to ensure machining stability and quality. Comparing these results with preset thresholds allows for the timely detection and resolution of anomalies, ensuring smooth machining and improving machining reliability and safety.

[0133] It should be understood that the serial numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0134] In one embodiment, a machining control system based on a five-axis machining center is provided. The machining control system based on the five-axis machining center corresponds to the machining control method based on the five-axis machining center in the above embodiment.

[0135] The machining control system for a five-axis machining center includes a parameter acquisition module, an initial path optimization module, a simulation environment establishment module, a path optimization and smoothing module, and a simulation verification execution module. A detailed description of each functional module is as follows: The parameter acquisition module is used to obtain the geometric characteristic parameters of the workpiece to be machined and the tool characteristic information. Based on the obtained geometric characteristic parameters, it establishes a cutting force model and calculates the cutting force distribution under different paths;

[0136] The initial path optimization module is used to generate the optimal tool path corresponding to the tool feature information using the path optimization algorithm based on the cutting force distribution and geometric feature parameters in the cutting force model;

[0137] The simulation environment establishment module is used to establish a simulation environment for the five-axis machining center based on the generated tool path and tool feature information, and simulate the motion state of the tool under different paths;

[0138] The path optimization smoothing module is used to set a reward function including path smoothness and cutting force stability in the simulation environment; it uses a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path;

[0139] The simulation verification execution module is used to verify the generated smooth tool path in the simulation environment; transmit the verified smooth tool path to the five-axis machining center and trigger the machining execution instruction.

[0140] Optional, five-axis machining control system also includes:

[0141] The multi-objective optimization module is used to set multiple optimization objectives in a preset multi-objective optimization model. The multiple optimization objectives include minimizing cutting force, maximizing tool life, and minimizing surface roughness.

[0142] The initial posture trajectory determination module is used to parameterize the tool posture by combining the tool feature information to obtain the tool posture parameters; the initial tool posture trajectory is determined based on the smooth tool path and the tool control parameters based on the time series;

[0143] The posture trajectory optimization module is used to optimize the tool posture parameters based on the initial tool posture trajectory in a multi-objective optimization model by adopting a multi-objective optimization algorithm to obtain an optimized tool posture trajectory.

[0144] For the specific limitations of the machining control system based on the five-axis machining center, please refer to the limitations of the machining control method based on the five-axis machining center above, which will not be repeated here; the various modules in the above-mentioned machining control system based on the five-axis machining center can be implemented in whole or in part through software, hardware and their combination; the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0145] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store cutting force models, simulation environments of five-axis machining centers and reinforcement learning algorithms, etc. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a processing control method based on a five-axis machining center is implemented.

[0146] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are performed:

[0147] S1: Obtain the geometric characteristic parameters of the workpiece and the tool characteristic information, establish a cutting force model based on the obtained geometric characteristic parameters, and calculate the cutting force distribution under different paths;

[0148] S2: In the cutting force model, based on the cutting force distribution and geometric characteristic parameters, the path optimization algorithm is used to generate the optimal tool path corresponding to the tool characteristic information;

[0149] S3: Based on the generated tool path and tool feature information, a simulation environment for the five-axis machining center is established to simulate the motion state of the tool under different paths;

[0150] S4: In the simulation environment, set a reward function that includes path smoothness and cutting force stability; use a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path;

[0151] S5: Verify the generated smooth tool path in the simulation environment; transmit the verified smooth tool path to the five-axis machining center and trigger the machining execution instruction.

[0152] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0153] S1: Obtain the geometric characteristic parameters of the workpiece and the tool characteristic information, establish a cutting force model based on the obtained geometric characteristic parameters, and calculate the cutting force distribution under different paths;

[0154] S2: In the cutting force model, based on the cutting force distribution and geometric characteristic parameters, the path optimization algorithm is used to generate the optimal tool path corresponding to the tool characteristic information;

[0155] S3: Based on the generated tool path and tool feature information, a simulation environment for the five-axis machining center is established to simulate the motion state of the tool under different paths;

[0156] S4: In the simulation environment, set a reward function that includes path smoothness and cutting force stability; use a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path;

[0157] S5: Verify the generated smooth tool path in the simulation environment; transmit the verified smooth tool path to the five-axis machining center and trigger the machining execution instruction.

[0158] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0159] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0160] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A machining control method based on a five-axis machining center, characterized in that: include: Obtain the geometric characteristic parameters of the workpiece and the tool characteristic information, establish a cutting force model based on the obtained geometric characteristic parameters, and calculate the cutting force distribution under different paths; In the cutting force model, based on the cutting force distribution and geometric characteristic parameters, a path optimization algorithm is used to generate an optimal tool path corresponding to the tool characteristic information; According to the generated tool path and the tool feature information, a simulation environment of a five-axis machining center is established to simulate the motion state of the tool under different paths; In the simulation environment, a reward function including path smoothness and cutting force stability is set; a reinforcement learning algorithm is used to optimize the smoothness of the tool path and generate a smooth tool path; The generated smooth tool path is verified in the simulation environment; the verified smooth tool path is transmitted to the five-axis machining center, and a machining execution instruction is triggered.

2. The processing control method based on a five-axis machining center according to claim 1, characterized in that: Said reward function including path smoothness and cutting force stability is set in said simulation environment; Using reinforcement learning algorithms to optimize the smoothness of tool paths and generate smooth tool paths, it also includes: In a preset multi-objective optimization model, multiple optimization objectives are set, wherein the multiple optimization objectives include minimizing cutting force fluctuation, maximizing tool life, and minimizing surface roughness; In combination with the tool feature information, the tool posture is parameterized to obtain tool posture parameters; based on the smooth tool path and the tool control parameters based on the time series, an initial tool posture trajectory is determined; In the multi-objective optimization model, a multi-objective optimization algorithm is adopted to optimize tool posture parameters based on the initial tool posture trajectory to obtain an optimized tool posture trajectory.

3. The processing control method based on a five-axis machining center according to claim 1, characterized in that: In the simulation environment, setting a reward function including path smoothness and cutting force stability includes: In the simulation environment, a corresponding reward function objective is defined according to a plurality of preset reward function component factors based on the generated tool path and simulation environment parameters; Assigning weights to a plurality of the reward function component factors; Construct a multi-objective reward function based on the defined reward function objectives and weights.

4. The processing control method based on a five-axis machining center according to claim 3, characterized in that: The multiple reward function component factors include a path smoothness factor, a cutting force stability factor, a processing time factor, and a surface quality factor; and constructing a multi-objective reward function based on the defined reward function objectives and weights includes: The multi-objective reward function is expressed as formula (1): R=w1R smo +w2R for +w3R t +w4R q (1) Among them, w1, w2, w3, w4 are weight coefficients, R smo is the path smoothness factor, R for is the cutting force stability factor, R t is the processing time factor, R q is the surface quality factor.

5. The processing control method based on a five-axis machining center according to claim 4, characterized in that: The method also includes: The path smoothness factor R is defined according to the reward function objective defined smo The reward function is expressed as formula (2): Among them, p i is the i-th point on the path, ||.|| represents the Euclidean distance, and n is the number of points on the path; The cutting force stability factor R is defined according to the reward function objective for The reward function is expressed as formula (3): Among them, F i is the cutting force at the i-th point, is the average cutting force; The processing time factor R is defined according to the reward function objective t The reward function is expressed as formula (4): Among them, t i is the processing time at the i-th point; The surface quality factor R is defined according to the reward function objective defined q The reward function is expressed as formula (5) Among them, σ i is the surface roughness at the i-th point.

6. The processing control method based on a five-axis machining center according to claim 4, characterized in that: After transmitting the verified smooth tool path to the five-axis machining center and triggering the machining execution instruction, the method further includes: Acquiring machining task parameters and a tool path based on the machining execution instruction, and acquiring three-dimensional position information of the workpiece to be machined and the tool; matching the three-dimensional position information with the starting point coordinates of the tool path, and machining the workpiece to be machined; During the machining process, the relative position information between the tool and the workpiece to be machined is obtained in real time through the preset sensors of the machine tool, and the real-time cutting parameters and machine tool performance parameters are calculated. The relative position information includes distance and angle; Based on the real-time cutting parameters and the machine tool performance parameters, a machine tool processing status result is obtained; based on the comparison between the machine tool processing status result and a preset threshold value, a processing parameter comparison result is obtained.

7. A processing control system based on a five-axis machining center, characterized in that: Applied to the machining control method based on a five-axis machining center according to any one of claims 1 to 6, the system comprises: The parameter acquisition module is used to obtain the geometric characteristic parameters of the workpiece to be processed and the tool characteristic information, establish a cutting force model based on the obtained geometric characteristic parameters, and calculate the cutting force distribution under different paths; an initial path optimization module, configured to generate an optimal tool path corresponding to tool feature information using a path optimization algorithm in the cutting force model based on the cutting force distribution and geometric feature parameters; A simulation environment establishment module is used to establish a simulation environment for a five-axis machining center based on the generated tool path and the tool feature information, and simulate the motion state of the tool under different paths; A path optimization smoothing module is used to set a reward function including path smoothness and cutting force stability in the simulation environment; use a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path; The simulation verification execution module is used to verify the generated smooth tool path in the simulation environment; transmit the verified smooth tool path to the five-axis machining center, and trigger a machining execution instruction.

8. The processing control system based on a five-axis machining center according to claim 7, characterized in that: The system further comprises: A multi-objective optimization module is used to set multiple optimization objectives in a preset multi-objective optimization model, wherein the multiple optimization objectives include minimizing cutting force fluctuation, maximizing tool life, and minimizing surface roughness; An initial posture trajectory determination module is used to parameterize the tool posture in combination with the tool feature information to obtain tool posture parameters; and to determine the initial tool posture trajectory based on a smooth tool path and a tool control parameter based on a time series; The posture trajectory optimization module is used to optimize the tool posture parameters based on the initial tool posture trajectory in the multi-objective optimization model by adopting a multi-objective optimization algorithm to obtain an optimized tool posture trajectory.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the machining control method based on a five-axis machining center as claimed in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the machining control method based on a five-axis machining center as claimed in any one of claims 1 to 6 are implemented.

Images