Intelligent numerical control machining method and system for mold manufacturing

Abstract

The invention discloses an intelligent numerical control machining method and system for mold manufacturing, and the method comprises the steps: obtaining a to-be-cut pattern of a mold, planning a milling cutter feeding route, dividing the to-be-cut pattern into a plurality of milling cutter feeding sub-routes, determining milling cutter control tracks composed of spline curves, carrying out the clustering of the milling cutter control tracks, and obtaining a plurality of track sets; and in the milling process, a real-time machining track is collected, a curvature deviation value is calculated, if the curvature deviation value exceeds a preset value, a milling cutter control track is corrected, the milling cutter priority is ranked according to the size of the working radius, and proper track sets are screened out in sequence for milling till all the track sets are machined. Through intelligent path planning and real-time deviation correction, the use efficiency and machining precision of the milling cutter are remarkably improved, and high quality and high stability of mold manufacturing are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an intelligent numerical control processing method and system for mold manufacturing. Background Art

[0002] In the mold manufacturing process, in order to achieve efficient and accurate processing, it is usually necessary to use a variety of milling cutters of different specifications and types for milling operations. Each milling cutter is suitable for processing paths of different curvatures and complexities according to its geometric shape and material properties. However, with the long-term use of the milling cutter, slight wear will inevitably occur, which will directly affect the cutting performance and processing accuracy of the milling cutter, and then lead to a decrease in the surface quality and dimensional accuracy of the mold.

[0003] Traditional mold processing methods often rely on experienced operators to manually adjust the milling cutter path and parameters, which is not only inefficient but also difficult to ensure the consistency and stability of processing. In addition, due to the lack of effective path optimization and wear compensation mechanism, excessive wear or tool damage is prone to occur during the processing, increasing production costs and maintenance frequency. Summary of the invention

[0004] In order to solve at least one of the technical problems mentioned above, the present invention provides an intelligent CNC machining method and system for mold manufacturing.

[0005] In a first aspect, the present invention provides an intelligent CNC machining method for mold manufacturing, the method comprising:

[0006] Acquire a pattern to be cut of a mold, plan a milling cutter feed route according to the pattern to be cut, divide the milling cutter feed route into a plurality of milling cutter feed sub-routes based on a plurality of preset curvature values, and determine a milling cutter control trajectory according to the milling cutter feed sub-routes, wherein the milling cutter control trajectory is composed of a spline curve;

[0007] Clustering the milling cutter control trajectories to obtain multiple milling cutter control trajectory sets, milling the mold according to the priority order of the milling cutter and the milling cutter control trajectory sets, and collecting real-time processing trajectories during the milling process;

[0008] Calculate the curvature deviation value of the corresponding point between the real-time machining trajectory and the milling cutter feed sub-route. If the curvature deviation value is greater than the preset deviation value, calculate the deviation data of the real-time machining trajectory and the milling cutter feed sub-route, and correct the milling cutter control trajectory according to the deviation data;

[0009] The method of milling the mold according to the priority order of the milling cutter and the milling cutter control trajectory set comprises:

[0010] According to the priority of the milling cutter from high to low, the milling cutter control trajectory whose maximum curvature value is less than the curvature value of the corresponding milling cutter working surface is selected in sequence;

[0011] The screened results are determined as the working trajectory of the corresponding milling cutter, and milling is performed until all the control trajectory sets of the milling cutters have completed milling, wherein the priority of the milling cutter is positively correlated with the working radius of the milling cutter.

[0012] Preferably, the calculating of the deviation data between the real-time machining trajectory and the milling cutter feeding sub-route, and correcting the milling cutter control trajectory according to the deviation data, comprises:

[0013] The milling cutter feed route is expressed as:

[0014]

[0015] Where t is the weight and t∈[0,1], which is used to represent the position of the point on the curve, P i is the i-th control point of the milling cutter feed sub-route, is the number of combinations, which means the number of combinations of i elements taken from n different elements;

[0016] Perform spline curve fitting on the real-time machining trajectory to obtain the real-time machining trajectory representation C(t);

[0017] Calculate the difference between the milling cutter feed sub-path representation and the real-time processing trajectory representation to obtain the deviation data

[0018]

[0019] Calculate the deviation data for each control point P i The partial derivative of is expressed as follows:

[0020]

[0021]

[0022] In the formula, is the number of combinations, which means the number of combinations of i-1 elements taken from n different elements;

[0023] Based on the gradient descent method and the calculated partial derivatives, the control points of the milling cutter control trajectory are corrected.

[0024] Preferably, clustering the milling cutter control trajectories to obtain a plurality of milling cutter control trajectory sets includes:

[0025] Obtain the curvature values ​​of the two end points of the milling cutter control trajectory to obtain a curvature value pair;

[0026] Based on the curvature value pairs, the K-Means algorithm is used to classify the milling cutter control trajectory into M milling cutter control trajectory sets, where M is the number of milling cutter types.

[0027] Preferably, determining the screened result as the working trajectory of the corresponding milling cutter includes:

[0028] Integrate the selected milling cutter control trajectory set to obtain multiple trajectory segments, and obtain the coordinate points at both ends of the trajectory segments;

[0029] Based on the coordinate points at both ends of the trajectory segment, a dynamic programming algorithm is used to solve the shortest total path connecting all trajectory segments to determine the working trajectory of the milling cutter.

[0030] In a second aspect, the present invention further provides an intelligent CNC machining system for mold manufacturing, the system comprising:

[0031] a milling cutter control trajectory determination module, used for acquiring a pattern to be cut of a mold, planning a milling cutter feed route according to the pattern to be cut, dividing the milling cutter feed route into a plurality of milling cutter feed sub-routes based on a plurality of preset curvature values, and determining a milling cutter control trajectory according to the milling cutter feed sub-routes, wherein the milling cutter control trajectory is composed of a spline curve;

[0032] A real-time machining trajectory acquisition module is used to cluster the milling cutter control trajectory to obtain multiple milling cutter control trajectory sets, mill the mold according to the priority order of the milling cutter and the milling cutter control trajectory set, and collect the real-time machining trajectory during the milling process;

[0033] The milling cutter control trajectory correction module is used to calculate the curvature deviation value of the corresponding point between the real-time machining trajectory and the milling cutter feed sub-route. If the curvature deviation value is greater than the preset deviation value, the deviation data of the real-time machining trajectory and the milling cutter feed sub-route is calculated, and the milling cutter control trajectory is corrected according to the deviation data;

[0034] The real-time processing trajectory acquisition module is also used to include:

[0035] According to the priority of the milling cutter from high to low, the milling cutter control trajectory whose maximum curvature value is less than the curvature value of the corresponding milling cutter working surface is selected in sequence;

[0036] The screened results are determined as the working trajectory of the corresponding milling cutter, and milling is performed until all the control trajectory sets of the milling cutters have completed milling, wherein the priority of the milling cutter is positively correlated with the working radius of the milling cutter.

[0037] Preferably, the milling cutter control trajectory correction module is also used for:

[0038] The milling cutter feed route is expressed as:

[0039]

[0040] Where t is the weight and t∈[0,1], which is used to represent the position of the point on the curve, P i is the i-th control point of the milling cutter feed sub-route, is the number of combinations, which means the number of combinations of i elements taken from n different elements;

[0041] Perform spline curve fitting on the real-time machining trajectory to obtain the real-time machining trajectory representation C(t);

[0042] Calculate the difference between the milling cutter feed sub-path representation and the real-time processing trajectory representation to obtain the deviation data

[0043]

[0044] Calculate the deviation data for each control point P i The partial derivative of is expressed as follows:

[0045]

[0046]

[0047] In the formula, is the number of combinations, which means the number of combinations of i-1 elements taken from n different elements;

[0048] Based on the gradient descent method and the calculated partial derivatives, the control points of the milling cutter control trajectory are corrected.

[0049] Preferably, the real-time processing trajectory acquisition module is also used for:

[0050] Obtain the curvature values ​​of the two end points of the milling cutter control trajectory to obtain a curvature value pair;

[0051] Based on the curvature value pairs, the K-Means algorithm is used to classify the milling cutter control trajectory into M milling cutter control trajectory sets, where M is the number of milling cutter types.

[0052] Preferably, the real-time processing trajectory acquisition module is also used for:

[0053] Integrate the selected milling cutter control trajectory set to obtain multiple trajectory segments, and obtain the coordinate points at both ends of the trajectory segments;

[0054] Based on the coordinate points at both ends of the trajectory segment, a dynamic programming algorithm is used to solve the shortest total path connecting all trajectory segments to determine the working trajectory of the milling cutter.

[0055] In a third aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0056] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation thereof.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1) The present invention ensures that each section of the milling cutter feed sub-route can be matched with the milling cutter of the most suitable size by reasonably dividing the milling cutter feed route, thereby improving the material removal rate while avoiding overcutting. Secondly, the milling cutter control trajectory is classified with the help of a clustering algorithm, so that large-sized milling cutters are preferentially used on the main molding part, while small-sized milling cutters are responsible for detail processing. This division of labor and cooperation mode not only improves the processing efficiency, but also ensures the surface quality and precision of the final product. Furthermore, by real-time monitoring of trajectory deviations during the processing process and making corrections in a timely manner, the adaptability and flexibility of the milling cutter are further enhanced, and stable processing accuracy and efficiency can be maintained even in the face of complex mold structures.

[0059] 2) The present invention uses Bezier curves and spline curves to represent the theoretical milling cutter feed sub-route and the actual processing trajectory respectively, calculates the deviation between the two and uses the gradient descent method to adjust the control points, thereby realizing precise control and adaptive adjustment of the processing path, significantly improving the processing accuracy and stability, while optimizing the processing efficiency and enhancing the robustness and reliability of the system.

[0060] 3) The present invention reasonably plans the sequence of multiple trajectory segments to be milled by the same milling cutter and adopts a dynamic programming algorithm to solve the shortest total path, which significantly improves the processing efficiency and accuracy, reduces energy consumption and resource waste, enhances the flexibility and adaptability of the system, and ensures efficient and precise processing of complex molds.

[0061] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0063] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0064] Figure 1A schematic diagram of a process flow of an intelligent numerical control processing method for mold manufacturing provided by an embodiment of the present invention;

[0065] Figure 2 A schematic structural diagram of an intelligent CNC machining system for mold manufacturing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0068] In the mold manufacturing process, traditional methods are difficult to effectively deal with the slight wear of milling cutters of different sizes after long-term use, resulting in reduced milling accuracy and processing efficiency.

[0069] See also Figure 1 , Figure 1 The present invention provides a flow chart of an intelligent CNC machining method for mold manufacturing. Figure 1 As shown, the method includes:

[0070] S100, obtaining a pattern to be cut of the mold, planning a milling cutter feed route according to the pattern to be cut, dividing the milling cutter feed route into a plurality of milling cutter feed sub-routes based on a plurality of preset curvature values, and determining a milling cutter control trajectory according to the milling cutter feed sub-routes, wherein the milling cutter control trajectory is composed of a spline curve;

[0071] Use professional CAD / CAM software to obtain the pattern to be cut of the mold. These software can read the mold design drawings or three-dimensional model files, and then generate the pattern to be cut. The milling cutter feed route is planned according to the pattern to be cut. This process requires comprehensive consideration of the working surface size of the milling cutter and the shape and size of the area to be milled. Specifically, based on the maximum effective working radius and minimum curvature radius of the milling cutter, combined with the actual contour of the area to be milled, the software's built-in path planning algorithm is used to determine the most suitable milling cutter feed route. Furthermore, it is necessary to divide the milling cutter feed route into multiple milling cutter feed sub-routes based on multiple preset curvature values. The preset curvature value here refers to a series of curvature thresholds pre-set according to the curvature radius of the working surface of different models of milling cutters. The purpose is to ensure that the curvature of all points on each milling cutter feed sub-route is within the allowable range to avoid overcutting caused by excessive curvature. Then, the milling cutter control trajectory is determined according to the milling cutter feed sub-route, that is, through mathematical modeling methods, such as spline interpolation, a smooth and continuous spline curve is constructed as the actual milling cutter control trajectory to ensure that the milling cutter moves efficiently and accurately along the predetermined path.

[0072] S200, clustering the milling cutter control trajectories to obtain multiple milling cutter control trajectory sets, milling the mold according to the priority order of the milling cutters and the milling cutter control trajectory sets, and collecting real-time processing trajectories during the milling process;

[0073] The curvature values ​​of the two end points of the milling cutter control trajectory are obtained to form a series of curvature value pairs. The K-Means clustering algorithm in machine learning is used to divide the milling cutter control trajectory into multiple trajectory sets equal to the number of milling cutter types according to the similarity of the curvature values. Then, in order of the milling cutter priority from high to low, that is, from large-size milling cutters to small-size milling cutters, the milling cutter control trajectory sets suitable for the current milling cutter work are selected for processing. In this process, the real-time processing trajectory data is continuously collected and recorded for subsequent error correction.

[0074] The method of milling the mold according to the priority order of the milling cutter and the milling cutter control trajectory set comprises:

[0075] S210, selecting, in descending order of priority of the milling cutter, the milling cutter control trajectories whose maximum curvature value is smaller than the curvature value of the corresponding milling cutter working surface;

[0076] When selecting the milling cutter control trajectory set, the trajectory sets whose maximum curvature value is less than the curvature value of the current milling cutter working surface are automatically screened out to ensure that the selected milling cutter will not overcut when performing the corresponding milling task. This strategy can not only make full use of the characteristics of large-size milling cutters to quickly remove large amounts of material, but also reduce the workload of small-size milling cutters while ensuring processing accuracy, thereby achieving the purpose of improving overall processing efficiency.

[0077] S220, determining the screened result as the working trajectory of the corresponding milling cutter, and performing milling until all the control trajectory sets of the milling cutters have completed milling, wherein the priority of the milling cutter is positively correlated with the working radius of the milling cutter;

[0078] The milling cutter working trajectory determined after screening will be directly used to guide the actual operation of the milling cutter. As the processing proceeds, the processing results of each step will be continuously monitored and recorded to ensure that all predetermined milling cutter control trajectory sets can successfully complete the milling task. At the same time, since the milling cutter priority is positively correlated with the working radius, the principle of large first and small later is always followed throughout the entire processing process, effectively ensuring the processing quality and efficiency.

[0079] S300, calculate the curvature deviation value of the corresponding point between the real-time machining trajectory and the milling cutter feed sub-route. If the curvature deviation value is greater than the preset deviation value, calculate the deviation data of the real-time machining trajectory and the milling cutter feed sub-route, and correct the milling cutter control trajectory according to the deviation data.

[0080] Calculate the curvature deviation value of the corresponding point between the real-time processing trajectory and the milling cutter feed sub-route. Specifically, it is necessary to calculate the current processing position relative to the theoretical position on the preset milling cutter feed sub-route based on the current milling cutter feed speed and the duration, and then compare the curvature difference between the two. Once it is found that the deviation exceeds the preset standard, adjust the milling cutter control trajectory based on the collected deviation data to ensure that the processing accuracy is not affected.

[0081] In this embodiment, the milling cutter feed route is reasonably divided to ensure that each milling cutter feed sub-route can be matched with a milling cutter of the most suitable size, thereby avoiding overcutting while improving the material removal rate. Secondly, the milling cutter control trajectory is classified with the help of a clustering algorithm, so that large-sized milling cutters are preferentially used on the main molding part, while small-sized milling cutters are responsible for detail processing. This division of labor and cooperation mode not only improves the processing efficiency, but also ensures the surface quality and precision of the final product. Furthermore, by real-time monitoring of trajectory deviations during the processing process and making corrections in a timely manner, the adaptability and flexibility of the milling cutter are further enhanced, and stable processing accuracy and efficiency can be maintained even in the face of complex mold structures.

[0082] Preferably, the calculating of the deviation data between the real-time machining trajectory and the milling cutter feeding sub-route, and correcting the milling cutter control trajectory according to the deviation data, comprises:

[0083] The milling cutter feed route is expressed as:

[0084]

[0085] Where t is the weight and t∈[0,1], which is used to represent the position of the point on the curve, P iis the i-th control point of the milling cutter feed sub-route, is the number of combinations, which means the number of combinations of i elements taken from n different elements;

[0086] Perform spline curve fitting on the real-time machining trajectory to obtain the real-time machining trajectory representation C(t);

[0087] Calculate the difference between the milling cutter feed sub-path representation and the real-time processing trajectory representation to obtain the deviation data

[0088]

[0089] Calculate the deviation data for each control point P i The partial derivative of is expressed as follows:

[0090]

[0091]

[0092] In the formula, is the number of combinations, which means the number of combinations of i-1 elements taken from n different elements;

[0093] Based on the gradient descent method and the calculated partial derivatives, the control points of the milling cutter control trajectory are corrected.

[0094] In this embodiment A milling cutter feed sub-route L(t) represented by a Bezier curve is defined, where t is a weight variable in the range [0,1], which is used to describe the position of any point on the curve, P i is the i-th control point. These control points determine the shape of the curve. The Bezier curve is a commonly used smooth curve that can fit complex machining paths well and ensure the smooth movement of the milling cutter during the machining process.

[0095] C(t) represents the real-time machining trajectory obtained by the spline curve fitting method. The spline curve is a smooth curve composed of multiple low-order polynomials, which can accurately reflect the real motion trajectory of the milling cutter during the actual machining process. This expression is used for subsequent comparison with the theoretical milling cutter feed sub-route L(t) to evaluate the machining accuracy. What is calculated is the overall deviation between the milling cutter feed sub-route L(t) and the real-time machining trajectory C(t). Specifically, it quantifies the overall deviation of the two curves by calculating the sum of the square distances between the points on the two curves in the interval [0,1]. The smaller the deviation value E, the closer the actual machining trajectory is to the theoretical path and the higher the machining accuracy. The calculation is the deviation function E about each control point P i The partial derivative reflects the partial derivative when the control point Pi When a small change occurs, how does the deviation E change accordingly? By calculating these partial derivatives, we can determine which control points are most effective in reducing the deviation, providing a basis for subsequent trajectory corrections. What is calculated is the Bezier curve L(t) about the i-th control point P i Specifically, it describes the rate of change when the control point P i The change of the curve L(t) at any point t when a small change occurs is crucial for calculating the partial derivative of the deviation function E, because only by knowing how L(t) changes with P i Only by analyzing the changes in E can we accurately assess the changing trend of E.

[0096] In this embodiment, Bezier curves and spline curves are used to represent the theoretical milling cutter feed sub-route and the actual processing trajectory respectively, and the deviation between the two is calculated and the control point is adjusted using the gradient descent method, thereby achieving precise control and adaptive adjustment of the processing path, significantly improving the processing accuracy and stability, while optimizing the processing efficiency and enhancing the robustness and reliability of the system.

[0097] Preferably, clustering the milling cutter control trajectories to obtain a plurality of milling cutter control trajectory sets includes:

[0098] Obtain the curvature values ​​of the two end points of the milling cutter control trajectory to obtain a curvature value pair;

[0099] Based on the curvature value pairs, the K-Means algorithm is used to classify the milling cutter control trajectory into M milling cutter control trajectory sets, where M is the number of milling cutter types.

[0100] By clustering the milling cutter control trajectory, the trajectories with similar curvature are classified into the same set, so that the milling cutter type that best suits the current trajectory characteristics can be selected in the actual processing process, thereby significantly improving the processing efficiency and quality. Specifically, the curvature values ​​of the two end points of the milling cutter control trajectory are obtained and a curvature value pair is formed. This process uses high-precision sensors and numerical differentiation methods to ensure the accuracy of the curvature value. Then, based on these curvature value pairs, the K-Means algorithm is used to classify the trajectories, so that the trajectories in each trajectory set have similar curvature characteristics, which not only simplifies the milling cutter selection process, but also avoids processing errors and inefficiencies caused by the selection of inappropriate milling cutters. For example, for trajectories with larger curvatures, a milling cutter with a smaller tip radius can be selected to better adapt to surface changes; while for trajectories with smaller curvatures, a milling cutter with a larger radius can be selected to increase the processing speed.

[0101] Preferably, determining the screened result as the working trajectory of the corresponding milling cutter includes:

[0102] Integrate the selected milling cutter control trajectory set to obtain multiple trajectory segments, and obtain the coordinate points at both ends of the trajectory segments;

[0103] Based on the coordinate points at both ends of the trajectory segment, a dynamic programming algorithm is used to solve the shortest total path connecting all trajectory segments to determine the working trajectory of the milling cutter.

[0104] The selected milling cutter control trajectory set is recorded and integrated through a high-precision sensor or a numerical control system to generate multiple trajectory segments. Each trajectory segment is composed of a series of coordinate points. These coordinate points accurately describe the specific positions that the milling cutter needs to pass through during the processing. Furthermore, the coordinate points at both ends of each trajectory segment are extracted to form a series of start point and end point coordinate pairs. Based on the coordinate points at both ends of these trajectory segments, a dynamic programming algorithm is used to solve the shortest total path connecting all trajectory segments, that is, by establishing a state transfer matrix, each trajectory segment is regarded as a node, and the edge weight between the nodes is the path length when the two trajectory segments are connected. The optimal path from the start point to the end point is found by recursion or iteration, and the working trajectory of the milling cutter is finally determined to ensure that the milling cutter completes the milling task of each trajectory segment in sequence according to the shortest path during the processing.

[0105] In this embodiment, by rationally planning the sequence of multiple trajectory segments to be milled by the same milling cutter, a dynamic programming algorithm is used to solve the shortest total path, which significantly improves processing efficiency and accuracy, reduces energy consumption and resource waste, enhances the flexibility and adaptability of the system, and ensures efficient and precise processing of complex molds.

[0106] In summary, the method provided in this embodiment can at least achieve the following effects:

[0107] 1) The present invention ensures that each section of the milling cutter feed sub-route can be matched with the milling cutter of the most suitable size by reasonably dividing the milling cutter feed route, thereby improving the material removal rate while avoiding overcutting. Secondly, the milling cutter control trajectory is classified with the help of a clustering algorithm, so that large-sized milling cutters are preferentially used on the main molding part, while small-sized milling cutters are responsible for detail processing. This division of labor and cooperation mode not only improves the processing efficiency, but also ensures the surface quality and precision of the final product. Furthermore, by real-time monitoring of trajectory deviations during the processing process and making corrections in a timely manner, the adaptability and flexibility of the milling cutter are further enhanced, and stable processing accuracy and efficiency can be maintained even in the face of complex mold structures.

[0108] 2) The present invention uses Bezier curves and spline curves to represent the theoretical milling cutter feed sub-route and the actual processing trajectory respectively, calculates the deviation between the two and uses the gradient descent method to adjust the control points, thereby realizing precise control and adaptive adjustment of the processing path, significantly improving the processing accuracy and stability, while optimizing the processing efficiency and enhancing the robustness and reliability of the system.

[0109] 3) The present invention reasonably plans the sequence of multiple trajectory segments to be milled by the same milling cutter and adopts a dynamic programming algorithm to solve the shortest total path, which significantly improves the processing efficiency and accuracy, reduces energy consumption and resource waste, enhances the flexibility and adaptability of the system, and ensures efficient and precise processing of complex molds.

[0110] See also Figure 2 In one embodiment, an intelligent CNC machining system for mold manufacturing is also provided, the system comprising:

[0111] A milling cutter control trajectory determination module 100 is used to obtain a pattern to be cut of a mold, plan a milling cutter feed route according to the pattern to be cut, divide the milling cutter feed route into a plurality of milling cutter feed sub-routes based on a plurality of preset curvature values, and determine a milling cutter control trajectory according to the milling cutter feed sub-routes, wherein the milling cutter control trajectory is composed of a spline curve;

[0112] The real-time machining trajectory collection module 200 is used to cluster the milling cutter control trajectory to obtain a plurality of milling cutter control trajectory sets, mill the mold according to the priority order of the milling cutter and the milling cutter control trajectory sets, and collect the real-time machining trajectory during the milling process;

[0113] The milling cutter control trajectory correction module 300 is used to calculate the curvature deviation value of the corresponding point between the real-time machining trajectory and the milling cutter feed sub-route. If the curvature deviation value is greater than the preset deviation value, the deviation data of the real-time machining trajectory and the milling cutter feed sub-route is calculated, and the milling cutter control trajectory is corrected according to the deviation data;

[0114] The real-time processing trajectory acquisition module 200 is also used to include:

[0115] According to the priority of the milling cutter from high to low, the milling cutter control trajectory whose maximum curvature value is less than the curvature value of the corresponding milling cutter working surface is selected in sequence;

[0116] The screened results are determined as the working trajectory of the corresponding milling cutter, and milling is performed until all the control trajectory sets of the milling cutters have completed milling, wherein the priority of the milling cutter is positively correlated with the working radius of the milling cutter.

[0117] Preferably, the milling cutter control trajectory correction module 300 is also used for:

[0118] The milling cutter feed route is expressed as:

[0119]

[0120] Where t is the weight and t∈[0,1], which is used to represent the position of the point on the curve, P i is the i-th control point of the milling cutter feed sub-route, is the number of combinations, which means the number of combinations of i elements taken from n different elements;

[0121] Perform spline curve fitting on the real-time machining trajectory to obtain the real-time machining trajectory representation C(t);

[0122] Calculate the difference between the milling cutter feed sub-path representation and the real-time processing trajectory representation to obtain the deviation data

[0123]

[0124] Calculate the deviation data for each control point P i The partial derivative of is expressed as follows:

[0125]

[0126]

[0127] In the formula, is the number of combinations, which means the number of combinations of i-1 elements taken from n different elements;

[0128] Based on the gradient descent method and the calculated partial derivatives, the control points of the milling cutter control trajectory are corrected.

[0129] Preferably, the real-time processing trajectory acquisition module 200 is also used for:

[0130] Obtain the curvature values ​​of the two end points of the milling cutter control trajectory to obtain a curvature value pair;

[0131] Based on the curvature value pairs, the K-Means algorithm is used to classify the milling cutter control trajectory into M milling cutter control trajectory sets, where M is the number of milling cutter types.

[0132] Preferably, the real-time processing trajectory acquisition module 200 is also used for:

[0133] Integrate the selected milling cutter control trajectory set to obtain multiple trajectory segments, and obtain the coordinate points at both ends of the trajectory segments;

[0134] Based on the coordinate points at both ends of the trajectory segment, a dynamic programming algorithm is used to solve the shortest total path connecting all trajectory segments to determine the working trajectory of the milling cutter.

[0135] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0136] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0137] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0138] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

Claims

1. An intelligent CNC machining method for mold manufacturing, characterized in that: The method comprises: Acquire a pattern to be cut of a mold, plan a milling cutter feed route according to the pattern to be cut, divide the milling cutter feed route into a plurality of milling cutter feed sub-routes based on a plurality of preset curvature values, and determine a milling cutter control trajectory according to the milling cutter feed sub-routes, wherein the milling cutter control trajectory is composed of a spline curve; Clustering the milling cutter control trajectories to obtain multiple milling cutter control trajectory sets, milling the mold according to the priority order of the milling cutter and the milling cutter control trajectory sets, and collecting real-time processing trajectories during the milling process; Calculate the curvature deviation value of the corresponding point between the real-time machining trajectory and the milling cutter feed sub-route. If the curvature deviation value is greater than the preset deviation value, calculate the deviation data of the real-time machining trajectory and the milling cutter feed sub-route, and correct the milling cutter control trajectory according to the deviation data; The method of milling the mold according to the priority order of the milling cutter and the milling cutter control trajectory set comprises: According to the priority of the milling cutter from high to low, the milling cutter control trajectory set whose maximum curvature value is less than the curvature value of the corresponding milling cutter working surface is selected in sequence; The screened results are determined as the working trajectory of the corresponding milling cutter, and milling is performed until all the control trajectory sets of the milling cutters have completed milling, wherein the priority of the milling cutter is positively correlated with the working radius of the milling cutter.

2. The intelligent CNC machining method for mold manufacturing according to claim 1, characterized in that: The method of calculating the deviation data between the real-time machining trajectory and the milling cutter feed sub-route and correcting the milling cutter control trajectory according to the deviation data includes: The milling cutter feed route is expressed as: Where t is the weight and t∈[0,1], which is used to represent the position of the point on the curve, P i is the i-th control point of the milling cutter feeding sub-path, is the number of combinations, which means the number of combinations of i elements taken from n different elements; Perform spline curve fitting on the real-time machining trajectory to obtain the real-time machining trajectory representation C(t); Calculate the difference between the milling cutter feed sub-path representation and the real-time processing trajectory representation to obtain the deviation data Calculate the deviation data for each control point P i The partial derivative of is expressed as follows: In the formula, is the number of combinations, which means the number of combinations of i-1 elements taken from n different elements; Based on the gradient descent method and the calculated partial derivatives, the control points of the milling cutter control trajectory are corrected.

3. The intelligent CNC machining method for mold manufacturing according to claim 1, characterized in that: The milling cutter control trajectory is clustered to obtain a plurality of milling cutter control trajectory sets, including: Obtain the curvature values ​​of the two end points of the milling cutter control trajectory to obtain a curvature value pair; Based on the curvature value pairs, the K-Means algorithm is used to classify the milling cutter control trajectory into M milling cutter control trajectory sets, where M is the number of milling cutter types.

4. The intelligent CNC machining method for mold manufacturing according to claim 1, characterized in that: The step of determining the screened result as the working trajectory of the corresponding milling cutter includes: Integrate the selected milling cutter control trajectory set to obtain multiple trajectory segments, and obtain the coordinate points at both ends of the trajectory segments; Based on the coordinate points at both ends of the trajectory segment, a dynamic programming algorithm is used to solve the shortest total path connecting all trajectory segments to determine the working trajectory of the milling cutter.

5. An intelligent CNC machining system for mold manufacturing, characterized in that: The system comprises: a milling cutter control trajectory determination module, used for acquiring a pattern to be cut of a mold, planning a milling cutter feed route according to the pattern to be cut, dividing the milling cutter feed route into a plurality of milling cutter feed sub-routes based on a plurality of preset curvature values, and determining a milling cutter control trajectory according to the milling cutter feed sub-routes, wherein the milling cutter control trajectory is composed of a spline curve; A real-time machining trajectory acquisition module is used to cluster the milling cutter control trajectory to obtain multiple milling cutter control trajectory sets, mill the mold according to the priority order of the milling cutter and the milling cutter control trajectory set, and collect the real-time machining trajectory during the milling process; The milling cutter control trajectory correction module is used to calculate the curvature deviation value of the corresponding point between the real-time machining trajectory and the milling cutter feed sub-route. If the curvature deviation value is greater than the preset deviation value, the deviation data of the real-time machining trajectory and the milling cutter feed sub-route is calculated, and the milling cutter control trajectory is corrected according to the deviation data; The real-time processing trajectory acquisition module is also used to include: According to the priority of the milling cutter from high to low, the milling cutter control trajectory whose maximum curvature value is less than the curvature value of the corresponding milling cutter working surface is selected in sequence; The screened results are determined as the working trajectory of the corresponding milling cutter, and milling is performed until all the control trajectory sets of the milling cutters have completed milling, wherein the priority of the milling cutter is positively correlated with the working radius of the milling cutter.

6. The intelligent CNC machining system for mold manufacturing according to claim 5, characterized in that: The milling cutter control trajectory correction module is also used for: The milling cutter feed route is expressed as: Where t is the weight and t∈[0,1], which is used to represent the position of the point on the curve, P i is the i-th control point of the milling cutter feeding sub-path, is the number of combinations, which means the number of combinations of i elements taken from n different elements; Perform spline curve fitting on the real-time machining trajectory to obtain the real-time machining trajectory representation C(t); Calculate the difference between the milling cutter feed sub-path representation and the real-time processing trajectory representation to obtain the deviation data Calculate the deviation data for each control point P i The partial derivative of is expressed as follows: In the formula, is the number of combinations, which means the number of combinations of i-1 elements taken from n different elements; Based on the gradient descent method and the calculated partial derivatives, the control points of the milling cutter control trajectory are corrected.

7. The intelligent CNC machining system for mold manufacturing according to claim 5, characterized in that: The real-time processing trajectory acquisition module is also used for: Obtain the curvature values ​​of the two end points of the milling cutter control trajectory to obtain a curvature value pair; Based on the curvature value pairs, the K-Means algorithm is used to classify the milling cutter control trajectory into M milling cutter control trajectory sets, where M is the number of milling cutter types.

8. The intelligent CNC machining system for mold manufacturing according to claim 5, characterized in that: The real-time processing trajectory acquisition module is also used for: Integrate the selected milling cutter control trajectory set to obtain multiple trajectory segments, and obtain the coordinate points at both ends of the trajectory segments; Based on the coordinate points at both ends of the trajectory segment, a dynamic programming algorithm is used to solve the shortest total path connecting all trajectory segments to determine the working trajectory of the milling cutter.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and when the processor executes the computer instructions, the electronic device executes the intelligent CNC machining method for mold manufacturing as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the intelligent CNC machining method for mold manufacturing as described in any one of claims 1 to 4.

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