A digital intelligent processing and manufacturing method and device based on CAM system

Abstract

The present application relates to a method and device for digital intelligent processing and manufacturing based on a CAM system, and relates to the field of intelligent manufacturing. The method includes obtaining a component model drawn by a user, determining the correlation between the component model and each historical model in a preset database, and determining a target model whose correlation reaches a preset correlation threshold, obtaining processing information for each target model, the processing information including the tool used, the moving path of each tool used, and the cutting parameters of each tool used, determining that the component model corresponds to the same area in each target model, and generating processing information of the component model based on the processing information of the same area, and outputting the processing information of the component model. The present application has the effect of improving the efficiency and accuracy of the formulation of processing strategies for parts by processing machine tools.

Description

Technical Field

[0001] The present application relates to the field of intelligent manufacturing, and in particular to a digital intelligent processing and manufacturing method and device based on a CAM system. Background Art

[0002] With the intelligentization of industrial manufacturing, CAM (Computer Aided Manufacturing) systems are increasingly widely used in industrial manufacturing and other fields. In CNC processing of machine tools, milling machines, etc., relevant personnel usually draw the parts to be processed through industrial drawing software, and then use CAM plug-ins in industrial drawing software or other software to design the processing information of the tool on the processing machine tool, such as tool model, feed rate and other cutting parameters. However, determining the processing strategy of the processing machine tool in this way requires relevant personnel to spend a lot of time to formulate, resulting in low efficiency. Summary of the invention

[0003] In order to improve the efficiency and accuracy of formulating machining strategies for parts by machining machine tools, the present application provides a digital intelligent machining and manufacturing method and device based on a CAM system.

[0004] In the first aspect, the present application provides a digital intelligent processing and manufacturing method based on a CAM system, which adopts the following technical solutions: A digital intelligent processing and manufacturing method based on a CAM system, comprising: Get the component model drawn by the user; Determine the relevance of the component model to each historical model in a preset database, and determine a target model whose relevance reaches a preset relevance threshold; Acquire processing information of each target model, wherein the processing information includes the tools used, the moving path of each tool used, and the cutting parameters of each tool used; Determining that the component model corresponds to a same region in each target model, and generating processing information of the component model based on processing information of the same region; The processing information of the component model is output.

[0005] By adopting the above technical solution, the component model drawn by the user is obtained to facilitate the subsequent formulation of the processing information of the component model. The preset database stores the historical models that have been processed, so the correlation between the component model and each historical model is determined. The higher the correlation, the closer the historical model is to the component model, and the more suitable the processing information of the historical model is for the component model. Therefore, the target model whose correlation reaches the preset correlation threshold is determined, and the processing information of each target model is obtained. The processing information includes the tools used to process the target model, the moving path of each tool used, and the cutting parameters of each tool used, etc. The same area in the component model and each target model is determined, and then the processing information of the component model can be generated according to the relevant information of the same area. Finally, the processing information of the generated component model is output to facilitate the staff to check the generated processing information in time. Compared with the staff setting the processing strategy of the component model through the CAM plug-in or other software, it is more efficient and accurate to first determine the target model with a high correlation with the component model and use the processing information of the target model to generate the processing information of the component model.

[0006] In another possible implementation manner, determining the relevance of the component model to each historical model in a preset database includes: Determine the isometric view and three-view drawing of the component model; Calculating a first similarity between the axonometric view of the component model and the axonometric view of each historical model; Calculating a second similarity between the three views of the component model and the three views of each historical model; Performing feature recognition on the component model to obtain processing features in the component model, wherein the processing features include holes, grooves, chamfers, and fillets; determining a first quantity of each machining feature in the component model and obtaining a second quantity of each machining feature in each historical model; Determining first position information of each machining feature in the component model and second position information of each machining feature in each history model; The relevance of each history model is determined based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information.

[0007] In another possible implementation, determining the relevance of each historical model based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information includes: Determine an average similarity of each historical model based on the first similarity and the second similarity; calculating an absolute value of a difference between a first quantity and a second quantity of each processing feature, and determining a coefficient of each processing feature in each history model based on the first quantity of each processing feature; Calculate the importance score of each processing feature according to the coefficient of each processing feature and the absolute value of the difference corresponding to each processing feature; Determine a total importance score of each history model based on the importance score of each processing feature; Determine a first relevance based on the similarity average value and the importance total score; The relevance of each history model is determined based on the first relevance, the first position information, and the second position information.

[0008] In another possible implementation, the first position information includes a first center point of each processing feature in each processing feature, the second position information includes a second center point of each processing feature in each processing feature of each historical model, and determining the correlation of each historical model based on the first correlation, the first position information, and the second position information includes: Connecting the first center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in the component model, and connecting the second center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in each historical model; Determining a third center point of the distribution profile of each machining feature in the component model and a fourth center point of the distribution profile of each machining feature in each history model; Calculating a third similarity between a distribution profile of each machining feature in the component model and a distribution profile of each machining feature in each historical model, and calculating a distance between the third center point and a fourth center point of each machining feature in each historical model; determining a second relevance of each history model based on a third similarity and a distance of each processing feature; The relevance of each historical model is determined based on the first relevance and the second relevance.

[0009] In another possible implementation manner, the generating the processing information of the component model based on the processing information of the same area includes: Mapping the processing information of the same area into the component model to obtain an intersection area; Determine the same areas corresponding to the intersection areas, and use the processing information of the same area with the largest area among the same areas corresponding to the intersection areas as the processing information of the intersection areas; Determine the boundaries of all identical regions in the component model, and obtain the tool movement path of the component model by fitting the movement paths of the identical regions on both sides of the boundaries; The machining information of the component model is generated based on the tool movement path, the tools used for each identical area, and the cutting parameters of each tool used.

[0010] In another possible implementation, the method further includes: Determining all the tools used from the machining information of the component model; Obtain the total distance, average feed rate and average cutting speed of all moving paths of each tool used within a preset historical time period; determining the degree of wear of each tool used based on the total distance, the average feed rate and the average cutting speed; If there is an abnormally used tool whose wear reaches a preset wear threshold, the moving path of the abnormally used tool on the component model is marked.

[0011] In another possible implementation, the method further includes: When an instruction to start machining is detected, vibration data, temperature value, feed rate and cutting speed of each tool used are obtained; determining an amplitude average and an amplitude variance based on the vibration data, and determining a temperature average of the temperature values; The wear amount of each tool used in the current machining is determined based on the amplitude average value, the amplitude variance, the temperature average value, the feed rate and the cutting speed.

[0012] In the second aspect, the present application provides a digital intelligent processing and manufacturing device based on a CAM system, which adopts the following technical solution: A digital intelligent processing and manufacturing device based on a CAM system, comprising: A model acquisition module is used to acquire the component model drawn by the user; A model determination module, used to determine the relevance between the component model and each historical model in a preset database, and to determine a target model whose relevance reaches a preset relevance threshold; An information acquisition module, used to acquire processing information of each target model, wherein the processing information includes the tool used, the moving path of each tool used, and the cutting parameters of each tool used; A processing information generating module, used for determining that the component model corresponds to the same region in each target model, and generating processing information of the component model based on the processing information of the same region; The output module is used to output the processing information of the component model.

[0013] By adopting the above technical solution, the model acquisition module acquires the component model drawn by the user to facilitate the subsequent formulation of the processing information of the component model. The preset database stores the historical models that have been processed. Therefore, the model determination module determines the correlation between the component model and each historical model. The higher the correlation, the closer the historical model is to the component model, and the more suitable the processing information of the historical model is for the component model. Therefore, the model determination module determines the target model whose correlation reaches the preset correlation threshold. The information acquisition module acquires the processing information of each target model. The processing information includes the tools used to process the target model, the moving path of each tool used, and the cutting parameters of each tool used. The processing information generation module determines the same area in the component model and each target model, and then generates the processing information of the component model according to the relevant information of the same area. Finally, the output module outputs the processing information of the generated component model to facilitate the staff to view the generated processing information in time. Compared with the staff setting the processing strategy of the component model through the CAM plug-in or other software, it is more efficient and accurate to first determine the target model with a high correlation with the component model and use the processing information of the target model to generate the processing information of the component model.

[0014] In another possible implementation, when determining the relevance between the component model and each historical model in the preset database, the model determination module is specifically configured to: Determine the isometric view and three-view drawing of the component model; Calculating a first similarity between the axonometric view of the component model and the axonometric view of each historical model; Calculating a second similarity between the three views of the component model and the three views of each historical model; Performing feature recognition on the component model to obtain processing features in the component model, wherein the processing features include holes, grooves, chamfers, and fillets; determining a first quantity of each machining feature in the component model and obtaining a second quantity of each machining feature in each historical model; Determining first position information of each machining feature in the component model and second position information of each machining feature in each history model; The relevance of each history model is determined based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information.

[0015] In another possible implementation, when the model determination module determines the relevance of each historical model based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information, it is specifically used to: Determine an average similarity of each historical model based on the first similarity and the second similarity; calculating an absolute value of a difference between a first quantity and a second quantity of each processing feature, and determining a coefficient of each processing feature in each history model based on the first quantity of each processing feature; Calculate the importance score of each processing feature according to the coefficient of each processing feature and the absolute value of the difference corresponding to each processing feature; Determine a total importance score of each history model based on the importance score of each processing feature; Determine a first relevance based on the similarity average value and the importance total score; The relevance of each history model is determined based on the first relevance, the first position information, and the second position information.

[0016] In another possible implementation, the first position information includes a first center point of each processing feature in each processing feature, the second position information includes a second center point of each processing feature in each historical model, and the model determination module determines the correlation of each historical model based on the first correlation, the first position information, and the second position information, specifically for: Connecting the first center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in the component model, and connecting the second center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in each historical model; Determining a third center point of the distribution profile of each machining feature in the component model and a fourth center point of the distribution profile of each machining feature in each history model; Calculating a third similarity between a distribution profile of each machining feature in the component model and a distribution profile of each machining feature in each historical model, and calculating a distance between the third center point and a fourth center point of each machining feature in each historical model; determining a second relevance of each history model based on a third similarity and a distance of each processing feature; The relevance of each historical model is determined based on the first relevance and the second relevance.

[0017] In another possible implementation manner, when the processing information generating module generates the processing information of the component model based on the processing information of the same area, it is specifically configured to: Mapping the processing information of the same area into the component model to obtain an intersection area; Determine the same areas corresponding to the intersection areas, and use the processing information of the same area with the largest area among the same areas corresponding to the intersection areas as the processing information of the intersection areas; Determine the boundaries of all identical regions in the component model, and obtain the tool movement path of the component model by fitting the movement paths of the identical regions on both sides of the boundaries; The machining information of the component model is generated based on the tool movement path, the tools used for each identical area, and the cutting parameters of each tool used.

[0018] In another possible implementation, the device further includes: A tool determination module, used to determine all used tools from the processing information of the component model; A first data acquisition module is used to obtain the total distance, average feed rate and average cutting speed of all moving paths of each tool used in a preset historical time period; a wear determination module, configured to determine the wear of each tool used based on the total distance, the average feed rate and the average cutting speed; The marking module is used to mark the moving path of the abnormally used tool on the component model when there is an abnormally used tool whose wear reaches a preset wear threshold.

[0019] In another possible implementation, the device further includes: A second data acquisition module is used to acquire vibration data, temperature value, feed rate and cutting speed of each tool used when an instruction to start processing is detected; A data determination module, configured to determine an amplitude average value and an amplitude variance based on the vibration data, and determine a temperature average value of the temperature value; The wear amount calculation module is used to determine the wear amount of each tool used in the current machining based on the amplitude average value, amplitude variance, temperature average value, feed rate and cutting speed.

[0020] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and at least one is configured to: execute a digital intelligent processing and manufacturing method based on a CAM system as shown in any possible implementation of the first aspect.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, when the computer program is executed in a computer, enables the computer to execute a digital intelligent processing and manufacturing method based on a CAM system as described in any one of the first aspects.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: Acquiring the component model drawn by the user facilitates the subsequent formulation of the processing information of the component model. The preset database stores the historical models that have been processed, so the correlation between the component model and each historical model is determined. The higher the correlation, the closer the historical model is to the component model, and the more applicable the processing information of the historical model is to the component model. Therefore, the target model whose correlation reaches the preset correlation threshold is determined, and the processing information of each target model is obtained. The processing information includes the tools used to process the target model, the moving path of each tool used, and the cutting parameters of each tool used, etc. The same area in the component model and each target model is determined, and then the processing information of the component model can be generated according to the relevant information of the same area. Finally, the processing information of the generated component model is output to facilitate the staff to check the generated processing information in time. Compared with the staff setting the processing strategy of the component model through CAM plug-ins or other software, it is more efficient and accurate to first determine the target model with a high correlation with the component model and use the processing information of the target model to generate the processing information of the component model. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a digital intelligent processing and manufacturing method based on a CAM system according to an embodiment of the present application.

[0024] Figure 2 It is a structural schematic diagram of a digital intelligent processing and manufacturing device based on a CAM system according to an embodiment of the present application.

[0025] Figure 3 It is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The present application is further described in detail below in conjunction with the accompanying drawings.

[0027] After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed, but such modifications are protected by patent law as long as they are within the scope of the claims of this application.

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0030] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0031] The embodiment of the present application provides a digital intelligent processing and manufacturing method based on a CAM system, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. Figure 1 As shown, the method includes step S101, step S102, step S103, step S104 and step S105, wherein: S101, obtaining a component model drawn by a user.

[0032] For the embodiment of the present application, the component model can be drawn by the staff using industrial drawing software, such as SolidWorks, CATIA, UG, Pro / E, PowerMill, etc. After the user completes drawing the component model, the engineering file of the component model can be stored in the local storage medium in the electronic device, so that the electronic device can obtain the component model.

[0033] S102, determining the relevance between the component model and each historical model in a preset database, and determining a target model whose relevance reaches a preset relevance threshold.

[0034] For the embodiment of the present application, the preset database stores component models that have been drawn and processed in the history, namely historical models. In order to facilitate the subsequent generation of processing information of the component model, the electronic device determines the correlation between the component model and each historical model. The higher the correlation, the more similar the historical model is to the component model, and the greater the possibility that the processing information of the historical model can be applied to the component model. The preset correlation threshold is used as the dividing point whether the correlation meets the requirements. After the electronic device determines the correlation of each historical model, it compares the correlation with the preset correlation threshold, thereby determining the target model that meets the preset correlation threshold.

[0035] S103, obtaining processing information of each target model.

[0036] The processing information includes the tools used, the moving path of each tool used, and the cutting parameters of each tool used.

[0037] For the embodiment of the present application, the processing information of each historical model can also be stored in a preset database and have a mapping relationship with the historical model. After the electronic device determines the target model, it can obtain the processing information of each target model.

[0038] S104, determining that the component model corresponds to the same region in each target model, and generating processing information of the component model based on processing information of the same region.

[0039] For the embodiments of the present application, there is an identical area with the same shape or structure between each target model and the component model. Therefore, the electronic device determines the identical area in each target model, and the processing information of the identical area can also be applied to the component model. Therefore, the electronic device can generate the processing information of the component model according to the determined processing information of the identical area, that is, the processing information of the component model is generated according to the processing information of the historical model with a high degree of relevance. This is more efficient and accurate than the staff directly formulating the processing information for the drawn component model.

[0040] S105: Output processing information of the component model.

[0041] For the embodiment of the present application, after the electronic device generates the processing information of the component model, the processing information can be output for the convenience of the staff to view. Specifically, the electronic device maps the processing information to a preset template or a preset table to obtain more intuitive table information, and can mark the areas with different processing information in the component model with different colors, and mark the processing information corresponding to each area with the same color, so that the staff can more intuitively view the processing information corresponding to different areas.

[0042] In a possible implementation of the embodiment of the present application, determining the relevance of the component model with each historical model in the preset database in step S102 specifically includes step S1021 (not shown in the figure), step S1022 (not shown in the figure), step S1023 (not shown in the figure), step S1024 (not shown in the figure), step S1025 (not shown in the figure), step S1026 (not shown in the figure) and step S1027 (not shown in the figure), wherein: S1021, determine the isometric view and three-view drawing of the component model.

[0043] For the embodiment of the present application, the electronic device can open the engineering file of the component model by calling the 3D drawing software and control the 3D drawing software to adjust the component model to the axonometric view and store the axonometric view of the component model under the view. The electronic device controls the 3D drawing software to adjust the component model to the view of the front view, the left view and the top view in sequence and store the views under the three view angles, i.e., three views.

[0044] S1022: Calculate a first similarity between the axonometric drawing of the component model and the axonometric drawing of each historical model.

[0045] For the embodiment of the present application, the electronic device can determine the axonometric view of each historical model in the manner disclosed in step S1021, and then the electronic device can input the axonometric view of the component model and the axonometric view of each historical model into the trained network model for similarity calculation to obtain a first similarity. First, a neural network is used to extract features from the two axonometric views, that is, the axonometric views are converted into feature vectors with certain representativeness. Common feature extraction methods include convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), etc. Based on the extracted feature vectors, the similarity between the two axonometric views is calculated. Common similarity calculation methods include cosine similarity, Euclidean distance, Manhattan distance, etc.

[0046] S1023, calculating a second similarity between the three views of the component model and the three views of each historical model.

[0047] For the embodiment of the present application, the electronic device can determine the front view, left view and top view of each historical model in the manner disclosed in step S1021. Then, the similarity between the front view of the component model and the front view of each historical model, the similarity between the left view and the left view of each historical model, and the similarity between the top view and the top view of each historical model are determined in the manner disclosed in step S1022, that is, the second similarity includes the similarity of the front view, the similarity of the left view and the similarity of the top view.

[0048] S1024, performing feature recognition on the component model to obtain processing features in the component model.

[0049] Among them, processing features include holes, slots, chamfers, and fillets.

[0050] For the embodiment of the present application, the electronic device inputs the component model into the trained network model for feature recognition, thereby identifying processing features such as holes, grooves, chamfers, and fillets in the component model. The processing features are not limited to the above four types, and may also include processing features such as steps and threads. Specifically, the network model may be a convolutional neural network model, a recurrent neural network model, or other types of network models. In other embodiments, the electronic device may also retrieve the drawing steps or logs of the component model, etc., to extract the processing features in the component model.

[0051] S1025, determining a first quantity of each processing feature in the component model, and obtaining a second quantity of each processing feature in each historical model.

[0052] For the embodiment of the present application, the electronic device counts each processing feature in the component model to obtain a first quantity of each processing feature. Similarly, the quantity of each processing feature in each historical model can also be stored in a preset database, so that the electronic device can obtain a second quantity of each processing feature in each historical model.

[0053] S1026, determining first position information of each processing feature in the component model and second position information of each processing feature in each historical model.

[0054] For the embodiment of the present application, the electronic device can map the component model into a preset three-dimensional coordinate system, thereby determining the three-dimensional coordinates of each processing feature in each processing feature in the component model, that is, the first position information. Similarly, the electronic device can determine the three-dimensional coordinates of each processing feature in each processing feature in the historical model, that is, the second position information, by mapping the historical model into the preset three-dimensional coordinate system.

[0055] S1027 , determining the relevance of each historical model based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information.

[0056] For the embodiment of the present application, the higher the first similarity and the second similarity, the higher the correlation between the historical model and the component model, and vice versa. The quantitative relationship between the first quantity and the second quantity of each processing feature also affects the correlation between the historical model and the component model. The position difference between the first position information and the second position information of the same type of processing features also affects the correlation between the historical model and the component model. Therefore, the electronic device can determine the correlation of each historical model based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information and the second position information.

[0057] In a possible implementation of the embodiment of the present application, in step S1027, the relevance of each historical model is determined based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information, specifically including step S1 (not shown in the figure), step S2 (not shown in the figure), step S3 (not shown in the figure), step S4 (not shown in the figure), step S5 (not shown in the figure), and step S6 (not shown in the figure), wherein: S1, determining an average similarity of each historical model based on the first similarity and the second similarity.

[0058] For the embodiment of the present application, the electronic device calculates the average value of the first similarity, the similarity of the front view, the similarity of the left view and the similarity of the top view through the average value calculation formula, that is, the similarity average value. The similarity average value is used to characterize the overall level of the four similarities. The higher the similarity average value of the historical model, the more similar the historical model is to the component model, and the higher the corresponding correlation is.

[0059] S2, calculating the absolute value of the difference between the first quantity and the second quantity of each processing feature, and determining the coefficient of each processing feature in each history model based on the first quantity of each processing feature.

[0060] For the embodiment of the present application, the electronic device calculates the average difference between the first number of each processing feature of the component model and the second number of the corresponding type in each historical model. The larger the average difference, the greater the difference in the number of processing features of the same type, indicating that the correlation is lower, and the smaller the average difference, the smaller the difference in the number of processing features of the same type, and the higher the correlation. For the component model, the larger the first number of a certain processing feature, the greater the feature belongs to the main processing item of the component model, and the higher the degree of influence on the overall processing and manufacturing of the component model. Therefore, the electronic device determines the coefficient of each processing feature for the first number of each processing feature in the component model. Specifically, the electronic device can sort the types of all processing features of the component model according to the first number to obtain a sorting result. A plurality of preset coefficients can be pre-stored in the electronic device. The electronic device can sort the preset coefficients from large to small to obtain a sorting result. The electronic device maps the same position data according to the sorting result of the processing feature type and the sorting result of the preset coefficient, and finally obtains the coefficient of each processing feature. The more the first number of a processing feature, the more important the processing feature is. The smaller the absolute value of the difference of the processing feature in a historical model, the closer the historical model and the component model are in terms of the number of processing features, and the higher the correlation. Therefore, the more a certain type of processing feature is in the component model, the more important the processing feature is, and the smaller the absolute value of the difference between the number of the same type of processing feature and the historical model is, but because the type of processing feature is more important, the higher the importance score of the processing feature in the historical model that needs to be calculated, the larger the coefficient needs to be set for the processing features with more numbers. In other implementations, the staff can obtain multiple preset coefficients through calculation or experiment, and then determine the preset coefficient of influence according to the number of each processing feature in the component model.

[0061] S3, calculating the importance score of each processing feature according to the coefficient of each processing feature and the absolute value of the difference corresponding to each processing feature.

[0062] For the embodiment of the present application, the electronic device performs weighted calculation on the absolute value of the difference corresponding to each processing feature according to the coefficient of each processing feature to obtain the importance score of each processing feature in each historical model.

[0063] S4, determining a total importance score of each history model based on the importance score of each processing feature.

[0064] For the embodiment of the present application, after the electronic device determines the importance score of each processing model of each historical model, the total importance score is obtained by summing up all the importance scores.

[0065] S5: Determine a first relevance based on the average similarity value and the total importance score.

[0066] For the embodiments of the present application, the average similarity value and the total importance score are both key factors affecting the relevance of the historical models. Therefore, the staff sets corresponding coefficients for the average similarity value and the total importance score and stores them in the electronic device. After the electronic device determines the average similarity value and the total importance score, it calls the corresponding coefficients for weighted calculation to obtain the first relevance of each historical model.

[0067] S6, determining the relevance of each historical model based on the first relevance, the first position information, and the second position information.

[0068] For the embodiment of the present application, the difference between the first position information of each processing feature and the second position information of the same processing feature in the historical model also affects the relevance of each historical model. Therefore, the electronic device determines the relevance of each historical model more accurately by combining the first position information and the second position information with the first relevance.

[0069] In a possible implementation manner of the embodiment of the present application, the first position information includes the first center point of each processing feature in each processing feature, the second position information includes the second center point of each processing feature in each processing feature of each historical model, and the correlation of each historical model is determined based on the first correlation, the first position information and the second position information in step S6, which specifically includes step S61 (not shown in the figure), step S62 (not shown in the figure), step S63 (not shown in the figure), step S64 (not shown in the figure) and step S65 (not shown in the figure), wherein, S61, connecting the first center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in the component model, and connecting the second center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in each historical model.

[0070] For the embodiment of the present application, after the electronic device maps the component model into the preset three-dimensional coordinate system, the first center point of each processing feature in each processing feature can be determined, and then the electronic device connects the first center point of each processing feature to obtain the distribution profile of each processing feature. Similarly, after the electronic device maps each historical model into the preset three-dimensional coordinate system, the second center point of the processing feature in each historical model can be determined, and then the electronic device connects the second center point of each processing feature to obtain the distribution profile of each processing feature.

[0071] S62, determining a third center point of the distribution profile of each processing feature in the component model and a fourth center point of the distribution profile of each processing feature in each history model.

[0072] For the embodiment of the present application, after the electronic device determines the distribution profile of each processing feature in the component model, the electronic device calculates the average value of the x-axis, y-axis and z-axis values ​​of the three-dimensional coordinates of all first center points based on the three-dimensional coordinates of each first center point on the distribution profile through the average value calculation formula, and determines the average value of the three axes as the third center point of the distribution profile of each processing feature. The electronic device determines the third center point of the distribution profile of each processing feature on the component model in the same way. The electronic device determines the fourth center point of the distribution profile of each processing feature in each historical model in the above manner.

[0073] S63, calculating a third similarity between a distribution profile of each processing feature in the component model and a distribution profile of each processing feature in each historical model, and calculating a distance between the third center point and a fourth center point of each processing feature in each historical model.

[0074] For the embodiment of the present application, the electronic device inputs the distribution profile of each processing feature in the component model and the distribution profile of the same type in each historical model into the trained network model for similarity calculation to obtain the third similarity. Taking the distribution profile of the hole feature on the component model as an example, the electronic device calculates the third similarity between the distribution profile of the hole feature and the distribution profile of the hole feature on each historical model. The higher the third similarity, the closer the distribution characteristics of the hole feature are, the closer the corresponding processing strategy is, and the higher the correlation between the component model and the historical model. Specifically, the network model can be a convolutional neural network model, a recurrent neural network model, or other types of network models. The electronic device calculates the third similarity between other types of processing features and the same type of processing features on each historical model in the above manner. The electronic device calculates the distance between the third center point of each processing feature on the component model and the fourth center point of the distribution profile of the same type of processing feature in each historical model by the distance formula between two points. The smaller the distance, the closer the distribution center points of the same type of processing features are, the closer the distribution of the processing features are, and the closer the processing strategies are, so the higher the correlation is.

[0075] S64, determining a second relevance of each history model based on the third similarity and distance of each processing feature.

[0076] For the embodiments of the present application, in summary, the third similarity and distance are both key factors that affect the correlation height between the component model and the historical model in terms of the distribution and position of the component features, and the degree of influence is different. Therefore, the staff sets corresponding coefficients for the third similarity and distance and stores them in the electronic device. After the electronic device determines the third similarity and distance, it calls the corresponding coefficients for weighted calculation to obtain a score. The score is the correlation of a processing feature. The electronic device calculates the correlation of all types of processing features in this way, and then averages or sums the correlations of all types of processing features to obtain a second correlation with respect to each historical model.

[0077] S65: Determine the relevance of each historical model based on the first relevance and the second relevance.

[0078] For the embodiment of the present application, the first correlation and the second correlation are both key factors affecting the total correlation of each historical model. Therefore, the total correlation of each historical model can be determined by combining the first correlation and the second correlation. The electronic device can directly sum the first correlation and the second correlation to obtain the total correlation, or the staff can set the corresponding coefficients for the first correlation and the second correlation and store them in the electronic device. The electronic device uses the corresponding coefficients for the first correlation and the second correlation to perform weighted calculation to obtain the total correlation. It is more accurate for the electronic device to determine the second correlation through the distribution and position attributes of each processing feature, that is, the distribution profile and the center point of the distribution profile, and then determine the total correlation in combination with the first correlation.

[0079] In a possible implementation of the embodiment of the present application, in step S104, the processing information of the component model is generated based on the processing information of the same area, specifically including step S1041 (not shown in the figure), step S1042 (not shown in the figure), step S1043 (not shown in the figure) and step S1044 (not shown in the figure), wherein: S1041, mapping the processing information of the same area into the component model to obtain the intersection area.

[0080] For the embodiment of the present application, the electronic device performs overlapping mapping of each target model with the component model respectively, so as to determine the same area between each target model and the component model, and then the electronic device maps the same area of ​​each target model to the component model. Since there are overlapping intersections between the same areas of different target models, the electronic device can determine the intersection area where there is an intersection.

[0081] S1042, determining the same regions corresponding to the intersecting regions, and taking the processing information of the same region with the largest area among the same regions corresponding to the intersecting regions as the processing information of the intersecting regions.

[0082] For the embodiment of the present application, after the electronic device determines all the intersecting areas, it determines the same area corresponding to each intersecting area. In order to reduce data duplication and confusion, the electronic device compares the areas of the same areas that cause the intersecting areas, thereby determining the same area with the largest area, and then deletes the parts of other same areas in the intersecting areas at the intersecting areas, thereby reducing the complexity of the intersecting areas and the data source. The electronic device directly uses the processing information of the same area with the largest area as the processing information of the intersecting areas. In each intersecting area, the processing information of the same area with the largest area is retained in this way, and the processing information of other same areas is deleted, so that the number of same areas finally mapped on the component model is the least and they are all same areas with larger areas, so that the processing information data sources of the final component model are the least, reducing the complexity of processing.

[0083] S1043, determining the boundaries of all identical regions in the component model, and fitting the moving paths of the identical regions on both sides of the boundaries to obtain the tool moving path of the component model.

[0084] For the embodiment of the present application, the electronic device can obtain the boundary between all the same areas after eliminating the intersecting areas, and the electronic device determines the breakpoints of the moving path of the tool in the same area at both ends of the boundary, and connects the breakpoints to obtain a continuous transition moving path, so that the subsequent processing process is more continuous and smooth. Alternatively, the electronic device inputs all the moving paths of the same area into the trained network model for path planning or path splicing to finally obtain the tool moving path of the component model.

[0085] S1044: Generate processing information of the component model based on the tool movement path, the tools used in each same area, and the cutting parameters of each tool used.

[0086] For the embodiment of the present application, the electronic device can obtain the overall processing information of the component model based on the tool movement path, the tools used in each same area, and the cutting parameters of each tool used. This is more efficient and accurate than the staff formulating processing strategies through related CAM plug-ins or software.

[0087] In a possible implementation manner of the embodiment of the present application, the method further includes step S106 (not shown in the figure), step S107 (not shown in the figure), step S108 (not shown in the figure) and step S109 (not shown in the figure), wherein: S106, determining all the tools used from the processing information of the component model.

[0088] For the embodiment of the present application, after the electronic device determines the processing information of the component model, it can perform data extraction on the processing information to obtain all the tools required to process the component model, that is, the tools used.

[0089] S107, obtaining the total distance, average feed rate and average cutting speed of all moving paths of each tool used in a preset historical time period.

[0090] For the embodiment of the present application, the data of each tool used in each processing will be stored in the cloud server or the local storage medium in the electronic device, that is, the distance of the moving path, the feed rate and the cutting speed of each processing. The preset historical time period is set by the staff through the visual operation interface, which can be the past seven days, the past half month, etc. The electronic device obtains the data of each tool used in the processing and use in the preset historical time period, and then sums the moving distance to obtain the total distance of the moving path of each tool in the historical time period, and calculates the average feed rate of each time through the average value calculation formula to obtain the average feed rate, and calculates the average cutting speed of each time through the average value calculation formula to obtain the average cutting speed.

[0091] S108, determining the wear degree of each used tool based on the total distance, the average feed rate and the average cutting speed.

[0092] For the embodiment of the present application, the total distance of the moving path, the average feed rate and the cutting speed are all key factors affecting the wear of each tool, and the degree of influence is different. Therefore, the staff sets the corresponding coefficients for the total distance, the average feed rate and the average cutting speed and stores them in the electronic device. The longer the total distance the tool moves, the more serious the wear, the larger the average feed rate, the more serious the wear, and the larger the average cutting speed, the more serious the wear. Therefore, after the electronic device determines the total distance, the average feed rate and the average cutting speed, it calls the corresponding coefficients for weighted calculation to obtain a score, which can characterize the wear of the tool in the preset historical time period. In this way, the electronic device determines the wear of each tool used.

[0093] S109: If there is an abnormally used tool whose wear reaches a preset wear threshold, a moving path of the abnormally used tool on the component model is marked.

[0094] For the embodiment of the present application, the preset wear threshold serves as the dividing point for whether the wear is too great. Reaching the preset wear threshold indicates that the wear is too great and the tool is not suitable for further processing. The tool needs to be inspected or replaced. Therefore, the electronic device compares the wear of all tools in the processing information with the preset wear threshold to determine the abnormal tool that reaches the preset wear threshold. If there is an abnormal tool, the electronic device determines the moving path of the abnormal tool on the component model, and marks the moving path with a color or line type that is different from the moving paths of other tools, so that the staff can know the tool abnormality in time.

[0095] In a possible implementation manner of the embodiment of the present application, the method further includes step Sa (not shown in the figure), step Sb (not shown in the figure) and step Sc (not shown in the figure), wherein: Sa, when the instruction to start machining is detected, the vibration data, temperature value, feed amount and cutting speed of each tool used are acquired.

[0096] For the embodiment of the present application, the processing machine tool is connected to the electronic device through a wire, and the worker presses the button to start processing on the processing machine tool, which is regarded as triggering the instruction to start processing. The processing machine tool sends the instruction to the electronic device, so that the electronic device detects the instruction to start processing. The worker can install a vibration sensor on the base of the tool, and the vibration sensor is connected to the electronic device through a wire. An infrared temperature sensor can be set on the processing machine tool, and the infrared temperature sensor faces the tool to collect the temperature value of the tool during processing. The electronic device is connected to the infrared temperature sensor through a wire, so that the electronic device can obtain the temperature value. The feed rate and cutting speed are in the processing information determined by the electronic device, so the electronic device can obtain the feed rate and cutting speed. If the worker modifies the processing information, the processing machine tool records the actual feed rate and cutting speed of the tool, so the electronic device obtains the real feed rate and cutting speed from the processing machine tool.

[0097] Sb, determining an amplitude average and an amplitude variance based on the vibration data, and determining a temperature average of the temperature values.

[0098] For the embodiment of the present application, the electronic device extracts the amplitude from the vibration data, and then determines the amplitude average value through the average value calculation formula, and determines the amplitude variance through the variance calculation formula, and the electronic device calculates the temperature average value through the average value calculation formula. The larger the amplitude average value, the more violent the tool vibration during processing, the processing quality may be reduced, resulting in reduced processing accuracy or more burrs, and greater tool wear. The larger the amplitude variance, the more unstable the vibration of the tool during processing, the processing quality may be reduced, resulting in reduced processing accuracy or more burrs, and greater tool wear. The larger the temperature average value, the more heat the tool generates during processing, and the greater the wear on the tool.

[0099] Sc, the wear amount of each tool used in the current machining is determined based on the amplitude mean, amplitude variance, temperature mean, feed rate and cutting speed.

[0100] For the embodiment of the present application, the amplitude mean, amplitude variance, stability mean, feed rate and cutting speed are all key factors affecting the tool wear. Therefore, the staff sets the corresponding coefficients for the above five factors. After the electronic device determines the above five factors, it calls the corresponding coefficients to determine a score, which can characterize the wear of the tool in the current processing. In other embodiments, the electronic device can also determine the wear in combination with the movement path of the tool and the above five factors. The wear of the tool during each processing is determined by the above key factors, which is convenient for the staff to grasp and understand the use of the tool in a timely manner.

[0101] The above embodiment introduces a digital intelligent processing and manufacturing method based on a CAM system from the perspective of a method flow, and the following embodiment introduces a digital intelligent processing and manufacturing device based on a CAM system from the perspective of a virtual module or a virtual unit. For details, please refer to the following embodiment.

[0102] The embodiment of the present application provides a digital intelligent processing and manufacturing device 20 based on a CAM system, such as Figure 2 As shown, the digital intelligent processing and manufacturing device 20 based on the CAM system may specifically include: Model acquisition module 201, used to acquire the component model drawn by the user; A model determination module 202 is used to determine the relevance of the component model with each historical model in a preset database, and to determine a target model whose relevance reaches a preset relevance threshold; The information acquisition module 203 is used to acquire the processing information of each target model, where the processing information includes the tool used, the moving path of each tool used, and the cutting parameters of each tool used; A processing information generating module 204 is used to determine that the component model corresponds to the same region in each target model, and to generate processing information of the component model based on the processing information of the same region; The output module 205 is used to output the processing information of the component model.

[0103] The embodiment of the present application discloses a digital intelligent processing and manufacturing device 20 based on a CAM system, wherein a model acquisition module 201 acquires a component model drawn by a user to facilitate the subsequent formulation of processing information of the component model. A preset database stores processed historical models, so a model determination module 202 determines the correlation between the component model and each historical model. The higher the correlation, the closer the historical model is to the component model, and the more applicable the processing information of the historical model is to the component model. Therefore, the model determination module 202 determines a target model whose correlation reaches a preset correlation threshold, and an information acquisition module 203 acquires processing information of each target model, and the processing information includes The tools used to process the target model, the moving path of each tool used, the cutting parameters of each tool used, etc. are known. The processing information generation module 204 determines the same area in the component model and each target model, and then generates the processing information of the component model according to the relevant information of the same area. Finally, the output module 205 outputs the generated processing information of the component model to facilitate the staff to check the generated processing information in time. Compared with the staff setting the processing strategy of the component model through CAM plug-ins or other software, it is more efficient and accurate to first determine the target model with a high correlation with the component model and use the processing information of the target model to generate the processing information of the component model.

[0104] In a possible implementation of the embodiment of the present application, when determining the relevance between the component model and each historical model in the preset database, the model determination module 202 is specifically used to: Determine the isometric view and three-view drawing of the component model; calculating a first similarity between the axonometric view of the component model and the axonometric view of each history model; Calculating a second similarity between the three views of the component model and the three views of each historical model; Perform feature recognition on the component model to obtain the processing features in the component model, including holes, slots, chamfers, and fillets; determining a first quantity of each machining feature in the component model and obtaining a second quantity of each machining feature in each historical model; Determining first position information of each machining feature in the component model and second position information of each machining feature in each history model; The relevance of each history model is determined based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information.

[0105] In a possible implementation manner of the embodiment of the present application, when the model determination module 202 determines the relevance of each historical model based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information, it is specifically used to: Determine an average similarity of each historical model based on the first similarity and the second similarity; calculating an absolute value of a difference between a first quantity and a second quantity of each processing feature, and determining a coefficient of each processing feature in each history model based on the first quantity of each processing feature; Calculate the importance score of each processing feature according to the coefficient of each processing feature and the absolute value of the difference corresponding to each processing feature; Determine a total importance score of each history model based on the importance score of each processing feature; Determine a first relevance based on the similarity average and the importance total score; The relevance of each history model is determined based on the first relevance, the first position information, and the second position information.

[0106] In a possible implementation manner of the embodiment of the present application, the first position information includes a first center point of each processing feature in each processing feature, and the second position information includes a second center point of each processing feature in each processing feature of each historical model. When the model determination module 202 determines the relevance of each historical model based on the first relevance, the first position information, and the second position information, it is specifically used to: Connecting the first center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in the component model, and connecting the second center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in each historical model; determining a third center point of the distribution profile of each machining feature in the component model and a fourth center point of the distribution profile of each machining feature in each history model; Calculating a third similarity between a distribution profile of each machining feature in the component model and a distribution profile of each machining feature in each historical model, and calculating a distance between the third center point and a fourth center point of each machining feature in each historical model; determining a second relevance of each history model based on a third similarity and a distance of each processing feature; The relevance of each history model is determined based on the first relevance and the second relevance.

[0107] In a possible implementation of the embodiment of the present application, when the processing information generating module 204 generates the processing information of the component model based on the processing information of the same area, it is specifically used to: Map the processing information of the same area to the component model to obtain the intersection area; Determine the same areas corresponding to the intersection areas, and use the processing information of the same area with the largest area among the same areas corresponding to the intersection areas as the processing information of the intersection areas; Determine the boundaries of all identical regions in the component model, and obtain the tool movement path of the component model by fitting the movement paths of the identical regions on both sides of the boundaries; Machining information of the component model is generated based on the tool movement path, the tools used for each identical area, and the cutting parameters of each tool used.

[0108] In a possible implementation of the embodiment of the present application, the device 20 further includes: A tool determination module is used to determine all the used tools from the processing information of the component model; A first data acquisition module is used to obtain the total distance, average feed rate and average cutting speed of all moving paths of each tool used in a preset historical time period; a wear determination module for determining the wear of each used tool based on the total distance, the average feed rate and the average cutting speed; The marking module is used to mark the moving path of the abnormally used tool on the component model when there is an abnormally used tool whose wear reaches a preset wear threshold.

[0109] In a possible implementation of the embodiment of the present application, the device 20 further includes: A second data acquisition module is used to acquire vibration data, temperature value, feed rate and cutting speed of each tool used when an instruction to start processing is detected; A data determination module, used to determine an amplitude average value and an amplitude variance based on the vibration data, and determine a temperature average value of the temperature value; The wear amount calculation module is used to determine the wear amount of each tool used in the current machining based on the amplitude average value, the amplitude variance, the temperature average value, the feed amount and the cutting speed.

[0110] Technicians in the relevant field can clearly understand that, for the convenience and brevity of description, the specific working process of the digital intelligent processing and manufacturing device 20 based on the CAM system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0111] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3The electronic device 30 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 30 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 30 does not constitute a limitation on the embodiments of the present application.

[0112] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0113] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0114] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0115] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0116] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0117] The embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer is run on a computer, the computer can execute the corresponding content in the above method embodiment. Compared with the related art, in the embodiment of the present application, the component model drawn by the user is obtained to facilitate the subsequent formulation of the processing information of the component model, and the preset database stores the processed historical models, so the correlation between the component model and each historical model is determined, and the higher the correlation, the closer the historical model is to the component model, and the more suitable the processing information of the historical model is for the component model, so the target model whose correlation reaches the preset correlation threshold is determined, and the processing information of each target model is obtained, and the processing information includes the tool used to process the target model, the moving path of each tool used, and the cutting parameters of each tool used, etc., and the same area in the component model and each target model is determined, and then the processing information of the component model can be generated according to the relevant information of the same area, and finally the generated processing information of the component model is output to facilitate the staff to view the generated processing information in time, and compared with the staff setting the processing strategy of the component model through the CAM plug-in or other software, first determining the target model with a high correlation with the component model and using the processing information of the target model to generate the processing information of the component model is more efficient and accurate.

[0118] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0119] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A digital intelligent processing and manufacturing method based on a CAM system, characterized in that: include: Get the component model drawn by the user; Determine the relevance of the component model to each historical model in a preset database, and determine a target model whose relevance reaches a preset relevance threshold; Acquire processing information of each target model, wherein the processing information includes the tools used, the moving path of each tool used, and the cutting parameters of each tool used; Determining that the component model corresponds to a same region in each target model, and generating processing information of the component model based on processing information of the same region; The processing information of the component model is output.

2. According to claim 1, a digital intelligent processing and manufacturing method based on a CAM system is characterized in that: Determining the relevance of the component model to each historical model in a preset database includes: Determine the isometric view and three-view drawing of the component model; calculating a first similarity between the axonometric view of the component model and the axonometric view of each historical model; Calculating a second similarity between the three views of the component model and the three views of each historical model; Performing feature recognition on the component model to obtain processing features in the component model, wherein the processing features include holes, grooves, chamfers, and fillets; determining a first quantity of each machining feature in the component model and obtaining a second quantity of each machining feature in each historical model; Determining first position information of each machining feature in the component model and second position information of each machining feature in each history model; The relevance of each history model is determined based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information.

3. According to claim 2, a digital intelligent processing and manufacturing method based on a CAM system is characterized in that: The determining the relevance of each historical model based on the first similarity, the second similarity, the first quantity, the second quantity, the first position information, and the second position information includes: Determine an average similarity of each historical model based on the first similarity and the second similarity; calculating an absolute value of a difference between a first quantity and a second quantity of each processing feature, and determining a coefficient of each processing feature in each history model based on the first quantity of each processing feature; Calculate the importance score of each processing feature according to the coefficient of each processing feature and the absolute value of the difference corresponding to each processing feature; Determine a total importance score of each history model based on the importance score of each processing feature; Determine a first relevance based on the similarity average value and the importance total score; The relevance of each history model is determined based on the first relevance, the first position information, and the second position information.

4. The digital intelligent processing and manufacturing method based on the CAM system according to claim 3 is characterized in that: The first position information includes a first center point of each processing feature in each processing feature, the second position information includes a second center point of each processing feature in each processing feature of each historical model, and determining the correlation of each historical model based on the first correlation, the first position information and the second position information includes: Connecting the first center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in the component model, and connecting the second center point of each processing feature in each processing feature to obtain the distribution profile of each processing feature in each historical model; Determining a third center point of the distribution profile of each machining feature in the component model and a fourth center point of the distribution profile of each machining feature in each history model; Calculating a third similarity between a distribution profile of each machining feature in the component model and a distribution profile of each machining feature in each historical model, and calculating a distance between the third center point and a fourth center point of each machining feature in each historical model; determining a second relevance of each history model based on a third similarity and a distance of each processing feature; The relevance of each historical model is determined based on the first relevance and the second relevance.

5. The digital intelligent processing and manufacturing method based on CAM system according to claim 1 is characterized in that: The step of generating the processing information of the component model based on the processing information of the same area includes: Mapping the processing information of the same area into the component model to obtain an intersection area; Determine the same areas corresponding to the intersection areas, and use the processing information of the same area with the largest area among the same areas corresponding to the intersection areas as the processing information of the intersection areas; Determine the boundaries of all identical regions in the component model, and obtain the tool movement path of the component model by fitting the movement paths of the identical regions on both sides of the boundaries; The machining information of the component model is generated based on the tool movement path, the tools used for each identical area, and the cutting parameters of each tool used.

6. The digital intelligent processing and manufacturing method based on CAM system according to claim 1 is characterized in that: The method further comprises: Determining all the tools used from the machining information of the component model; Obtain the total distance, average feed rate and average cutting speed of all moving paths of each tool used within a preset historical time period; determining the degree of wear of each tool used based on the total distance, the average feed rate and the average cutting speed; If there is an abnormally used tool whose wear reaches a preset wear threshold, the moving path of the abnormally used tool on the component model is marked.

7. The digital intelligent processing and manufacturing method based on CAM system according to claim 1 is characterized in that: The method further comprises: When an instruction to start machining is detected, vibration data, temperature value, feed rate and cutting speed of each tool used are obtained; determining an amplitude average and an amplitude variance based on the vibration data, and determining a temperature average of the temperature values; The wear amount of each tool used in the current machining is determined based on the amplitude average value, the amplitude variance, the temperature average value, the feed rate and the cutting speed.

8. A digital intelligent processing and manufacturing device based on a CAM system, characterized in that: include: A model acquisition module is used to acquire the component model drawn by the user; A model determination module, used to determine the relevance between the component model and each historical model in a preset database, and to determine a target model whose relevance reaches a preset relevance threshold; An information acquisition module, used to acquire processing information of each target model, wherein the processing information includes the tool used, the moving path of each tool used, and the cutting parameters of each tool used; A processing information generating module, used for determining that the component model corresponds to the same region in each target model, and generating processing information of the component model based on the processing information of the same region; The output module is used to output the processing information of the component model.

9. An electronic device, characterized in that: It includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in the memory and is configured to be executed by the at least one processor, and the at least one application is used to execute a digital intelligent processing and manufacturing method based on a CAM system according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the digital intelligent processing and manufacturing method based on a CAM system as described in any one of claims 1 to 7.

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