Cutter intelligent optimization device and method, electronic equipment and storage medium

Abstract

The invention relates to an intelligent tool optimization device and method, electronic equipment and a storage medium. The intelligent tool optimization method comprises the steps that the intelligent tool optimization device comprises an input module used for inputting to-be-machined process data and comprising a structured part process module and a numerical control machining chart module; the processing module is used for processing knowledge reasoning and processing parameter recommendation, and comprises a processing template reasoning module used for reasoning a part processing procedure method and flow; the tool reasoning module is used for reasoning tool types and tool parameters; the cutting parameter recommendation module is used for reasoning a processing method and cutting parameters; and the output module is used for tool selection and machining parameter endowing and comprises a machining template selection module, a tool selection module and a cutting parameter endowing module. According to the intelligent tool optimization device, cutting parameters of numerical control machining can be optimized and selected, the machining quality and efficiency of parts are improved, and the manufacturing cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital process design, in particular to a device and method for intelligent selection of cutting tools based on model features, an electronic device and a storage medium. BACKGROUND

[0002] Process design is the link between production and design, and its design results directly determine the quality and efficiency of enterprise production. Cutting tool selection is a very critical link in the machining process, which not only directly relates to the surface quality of the machined workpiece, but also involves the entire machining cost.

[0003] When selecting a cutting tool, the selected cutting tool should not only have the required machining capability, but also be the optimal cutting tool under certain optimization objectives. Therefore, cutting tool selection is a complex optimization process. In the traditional process design process, process designers select cutting tools based on machining experience, which has problems such as low design efficiency and poor accuracy, and unreasonable machining parameters directly lead to low machining quality of parts and increased manufacturing cost.

[0004] Therefore, it is urgent to develop a more intelligent cutting tool selection method to automatically recommend the optimal machining cutting tool to process designers to improve design efficiency and accuracy. SUMMARY

[0005] The technical problem to be solved by the present application is that in the traditional process design process, process designers select cutting tools based on machining experience, which has problems such as low design efficiency and poor accuracy, and unreasonable machining parameters directly lead to low machining quality of parts and increased manufacturing cost.

[0006] To solve the above technical problems, according to one aspect of the present application, a device for intelligent selection of cutting tools is provided, which comprises: an input module for inputting process data to be machined, wherein the input module comprises: a structured part process module for constructing structured process data of parts; a numerical control chart module for guiding refinement of numerical control machining methods and parameter data; a processing module for machining knowledge reasoning and machining parameter recommendation, wherein the processing module comprises: a machining template reasoning module for reasoning part machining process method and flow; a cutting tool reasoning module for reasoning cutting tool type and cutting tool parameter; a cutting parameter recommendation module for reasoning machining method and cutting parameter; an output module for cutting tool selection and machining parameter assignment, wherein the output module comprises: a machining template selection module for selecting part machining templates; a cutting tool selection module for selecting machining cutting tools; and a cutting parameter assignment module for assigning corresponding process operations to cutting parameters.

[0007] According to the embodiment of the present application, the tool intelligence optimization device is preferably implemented based on Teamcenter In-class application; the processing module can customize the processing parameters including tool material, part material, cutting method, tool diameter, tool length, feed speed, spindle speed, cutting depth, and step distance according to the part type processing technology characteristics of the diesel engine key structural parts.

[0008] According to the embodiment of the present application, the tool reasoning module can include a tool library, and the tool library is provided with optional tool information including tool type, tool code, tool manufacturer, tool specification, tool model, tool or blade material, tool diameter, tool edge number, tool edge length, tool total length, cooling method, and tool life, wherein the tool type includes drill, reamer, milling cutter, turning tool, blade, tap, die, parting tool, tool accessory, planer, and boring tool.

[0009] According to the embodiment of the present application, the cutting parameter recommendation module can include a cutting parameter library, and the cutting parameter library structure is sorted according to the existing process type, part type, processing material, and tool type, all cutting information required for optimization of parameters is perfected, and the cutting parameter library meeting the requirements of NX CAM numerical control programming is established; the cutting parameter library includes data parameters of spindle speed, cutting speed, feed per tooth fz, feed speed, cutting depth, cutting width / row distance, and tool moving speed, the data parameters in the cutting parameter library are combined with the tool library information to facilitate quick calling and management of verified cutting processing data, and the cutting processing data is applied to the associated tool path operation; the cutting parameter library can optimize the processing parameter information according to the part type, material, and tool related information, and assign the processing parameter information to the process data to be processed.

[0010] According to the second aspect of the present application, a tool intelligence optimization method is provided, and the method includes the following steps: data input, the system reads process data, process information, and process model; numerical control processing chart, according to the chart information, determines equipment, material, tool, and feature, and completes classification attribute information definition; template calling, the system calls the processing template based on the classification attribute information matching; tool calling, the recommended tool is called based on knowledge reasoning; cutting parameter assignment, the recommended optimal cutting parameter is obtained based on knowledge reasoning; tool path generation, the tool path is compiled based on the template, processing geometry, and cutting parameter; post-processing, the corresponding machine tool post is called through the processing equipment, and the NC digital control program is output; document output and approval, the numerical control processing document is output, and the approval process is completed.

[0011] According to the embodiment of the present application, when the numerical control machining icon is processed, the machining parameters including the tool material, the part material, the cutting mode, the tool diameter, the tool length, the feed speed, the spindle speed, the cutting depth and the step distance can be customized according to the part type and the machining process characteristics of the key structural part of the diesel engine.

[0012] According to the embodiment of the present application, the tool calling can be based on a tool library, and the tool library can set optional tool information including the tool type, the tool material, the tool manufacturer, the tool specification, the tool model, the tool or blade material, the tool diameter, the tool edge number, the tool edge length, the tool total length, the cooling mode and the tool life, wherein the tool type includes the drill, the reamer, the milling cutter, the turning tool, the blade, the tap, the die, the cutting tool, the tool accessory, the planer, and the boring tool.

[0013] According to the embodiment of the present application, when the cutting parameter is assigned, the cutting parameter selection can be based on the cutting parameter library, the cutting parameter library structure can be sorted according to the existing process type, the part type, the machining material and the tool type, all the cutting information required for the optimized parameters can be improved, the cutting parameter library meeting the requirements of the NX CAM numerical control programming can be established, the data parameters including the spindle speed, the cutting speed, the feed per tooth fz, the feed speed, the cutting depth, the cutting width / row distance and the tool moving speed are included in the cutting parameter library, the data parameters in the cutting parameter library are combined with the tool library information, so that the verified cutting machining data can be called and managed conveniently and quickly, and the verified cutting machining data can be applied to the associated tool trajectory operation, and the machining parameter information can be optimized according to the part type, the material and the tool related information, and the machining parameter information can be assigned to the process data to be used for machining.

[0014] According to the third aspect of the present application, an electronic device is provided, which includes a memory, a processor and a tool intelligent optimization program stored in the memory and executable on the processor, and when the tool intelligent optimization program is executed by the processor, the steps of the tool intelligent optimization method are implemented.

[0015] According to the fourth aspect of the present application, a computer storage medium is provided, wherein the tool intelligent optimization program is stored in the computer storage medium, and when the tool intelligent optimization program is executed by the processor, the steps of the tool intelligent optimization method are implemented.

[0016] Compared with the prior art, the technical scheme provided by the embodiment of the present application can at least achieve the following beneficial effects:

[0017] According to the tool intelligent optimization device, method, electronic device and storage medium of the present application, the enterprise machining experience can be integrated, the efficient and high-quality machining data can be refined, and the enterprise manufacturing knowledge base can be improved.

[0018] The tool intelligent selection device, method, electronic device and storage medium can improve the selection of tools in numerical control programming and optimization of tool libraries in enterprises, improve efficiency and reduce tool costs.

[0019] The tool intelligent selection device, method, electronic device and storage medium can optimize the selection of cutting parameters in numerical control machining, improve the machining quality and efficiency of parts, and reduce manufacturing costs. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments will be briefly introduced below. Obviously, the drawings described below only relate to some of the embodiments of the present application, but not limit the present application.

[0021] Figure 1 is a device block diagram illustrating the tool intelligent selection according to the embodiments of the present application;

[0022] Figure 2 is a method flow chart illustrating the tool intelligent selection according to the embodiments of the present application. DETAILED DESCRIPTION

[0023] In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] Unless otherwise defined, the technical terms or scientific terms used herein should be understood as the usual meanings understood by those skilled in the art to which the present application belongs. The "first", "second" and similar words used in the patent application description and claims of the present application do not represent any order, quantity or importance, but are used to distinguish different components. Similarly, "one" or "a" and similar words do not represent a quantity limit, but represent the existence of at least one.

[0025] Figure 1 is a device block diagram illustrating the tool intelligent selection according to the embodiments of the present application.

[0026] As shown in Figure 1 , the tool intelligent selection device comprises an input module, a processing module and an output module.

[0027] The input module is used for inputting process data to be processed, wherein the input module comprises: a structured part process module, used for constructing structured process data of parts; and a numerical control machining chart module, used for guiding refinement of numerical control machining methods and parameter data.

[0028] The processing module is used for processing knowledge reasoning and machining parameter recommendation, wherein the processing module comprises: a machining template reasoning module, used for reasoning machining process methods and processes of parts; a tool reasoning module, used for reasoning tool types and tool parameters; and a cutting parameter recommendation module, used for reasoning machining methods and cutting parameters.

[0029] The output module is used for tool selection and machining parameter assignment, wherein the output module comprises: a machining template selection module, used for selecting machining templates of parts; a tool selection module, used for selecting machining tools; and a cutting parameter assignment module, used for assigning corresponding process operations with cutting parameters.

[0030] The device, method, electronic equipment and storage medium for intelligent tool optimization according to the application can optimize selection of cutting parameters for numerical control machining, improve machining quality and efficiency of parts, and reduce manufacturing cost.

[0031] According to one or some embodiments of the application, the processing module is based on Teamcenter In-class application, and the processing module customizes machining parameters including tool material, part material, cutting method, tool diameter, tool length, feed speed, spindle speed, cutting depth and step distance according to part type machining process characteristics of key structural parts of diesel engines.

[0032] According to one or some embodiments of the application, the tool reasoning module comprises a tool library, and the tool library sets optional tool information including tool type, tool code, tool manufacturer, tool specification, tool brand, tool or blade material, tool diameter, tool edge number, tool edge length, tool total length, cooling method and tool life, wherein the tool type includes drill, reamer, milling cutter, turning tool, blade, tap, die, parting tool, tool auxiliary part, planer tool and boring tool.

[0033] According to one or some embodiments of the present application, the cutting parameter recommendation module comprises a cutting parameter library, according to the existing process type, part type, machining material, tool type, the cutting parameter library structure is sorted out, all cutting information required for perfecting the preferred parameters is sorted out, and the cutting parameter library meeting the requirements of NX CAM numerical control programming is established; the cutting parameter library comprises data parameters of spindle speed, cutting speed, feed per tooth fz, feed speed, cutting depth, cutting width / row pitch, and tool moving speed, the data parameters in the cutting parameter library are combined with tool library information, so that the verified cutting machining data is called and managed conveniently and quickly, and is applied to the associated tool path operation; the cutting parameter library can be used to select the machining parameter information according to the part type, material, and tool related information, and the machining parameter information is assigned to the process data to be used for machining.

[0034] Figure 2 It is a method flow chart of tool intelligent optimization according to an embodiment of the present application.

[0035] As shown in Figure 2 , the method of tool intelligent optimization comprises the following steps:

[0036] Data input, the system reads process data, process model, and process information;

[0037] Numerical control machining chart, according to chart information, determine equipment, material, tool and feature, complete classification attribute information definition;

[0038] Template calling, the system matches and calls the machining template based on the classification attribute information;

[0039] Tool calling, based on knowledge reasoning, call recommended tool;

[0040] Cutting parameter assignment, based on knowledge reasoning, obtain recommended preferred cutting parameter;

[0041] Generate tool path, complete tool path programming based on template, machining geometry, and cutting parameter;

[0042] Post-processing, call corresponding machine tool post-processing through machining equipment, and output NC digital control program;

[0043] Document output and approval, output numerical control machining document, and complete approval process.

[0044] According to one or some embodiments of the present application, when the numerical control machining icon is displayed, according to the part type machining process characteristics of the key structural part of the diesel engine, the machining parameters including tool material, part material, cutting method, tool diameter, tool length, feed speed, spindle speed, cutting depth, and step pitch are customized.

[0045] According to one or some embodiments of the present application, wherein the tool call is based on a tool library, the tool library setting includes optional tool information of tool type, tool code, tool manufacturer, tool specification, tool model, tool or blade material, tool diameter, tool edge number, tool edge length, tool total length, cooling method, tool life, wherein the tool type includes drill, reamer, milling cutter, turning tool, blade, tap, die, parting tool, tool accessory, planer, boring tool.

[0046] The tool intelligent optimization device, method, electronic equipment and storage medium according to the present application can improve the selection of tools in numerical control programming and the optimization of enterprise tool library, improve efficiency and reduce tool cost.

[0047] According to one or some embodiments of the present application, when the cutting parameter is assigned, the cutting parameter selection is based on a cutting parameter library, the cutting parameter library structure is sorted according to the existing process type, part type, machining material and tool type, all cutting information required for optimized parameters is perfected, and a cutting parameter library meeting the requirements of NX CAM numerical control programming is established; the cutting parameter library includes data parameters of spindle speed, cutting speed, feed per tooth fz, feed speed, cutting depth, cutting width / row spacing and tool moving speed, the data parameters in the cutting parameter library are combined with the tool library information to facilitate the calling and management of verified cutting machining data, and the cutting machining data is applied to the associated tool path operation; the cutting parameter library can select and assign machining parameter information to the process data to be used for machining according to the part type, material and tool related information.

[0048] The tool intelligent optimization device, method, electronic equipment and storage medium according to the present application can integrate enterprise machining experience, refine efficient and high-quality machining data, and perfect enterprise manufacturing knowledge base.

[0049] In specific implementation, the tool intelligent optimization method of the present application is used for database creation and management, and is realized based on In-class in Teamcenter. In-class in Teamcenter is a powerful data classification module, which realizes the classification management of product data and process resources in Teamcenter. In In-class, the complex product data is grouped (Group) first based on the classification idea, and the group can be inherited in multiple layers; then the group is subdivided into abstract classes (AbstractClass); the abstract class can inherit multiple subclasses (SubClass); only the subclass can be objectified, that is, each subclass corresponds to multiple data objects. The group, abstract class, subclass and object constitute the product data structure tree of the entire enterprise.

[0050] Teamcenter In-class application object-oriented method, data classification, hierarchical organization and management, analysis of data information structure, research its structure, relationship, describe the dynamic changes of data, the establishment of object-oriented data classification structure, so that a very complex information structure becomes simple and clear, conducive to the next process manufacturing resource organization and data management.

[0051] Classification management involves related concepts include:

[0052] Group (group), the collection of related classes, the highest level of classification, such as: with map procurement parts library.

[0053] Abstract class (abstract class), used to merge the common attributes of storage class, classification instance cannot be stored in the abstract class.

[0054] Storage class (storage class), defined by the parent class inherits the properties and storage class itself special attribute set, can form the definition of specific parts and other information.

[0055] View (view), associated with storage class or abstract class, used to customize class attribute access rights for specific users or user groups. Different users or groups can display different attributes or hide some attributes.

[0056] Attribute, the main basis for query function, attribute is inherent characteristics, used to describe and identify a certain object in a group of objects.

[0057] Classification layer (Hierarchy), define the classification level of enterprise and the attributes, access rights, images of specific classes.

[0058] Attribute dictionary (Dictionary), define all the required attribute information of the enterprise, provide the basis for adding attributes of specific classes.

[0059] Create attribute dictionary: attributes are inherent characteristics with uniqueness, used to describe and identify a certain feature in a group of objects. For the attributes that need to inherit from the knowledge resource Item version main attribute table, configure the reference attribute at the same time of creating the attribute dictionary to achieve.

[0060] Create classification attributes, requirements are as follows: 1) attribute name must be accurate, cannot cause ambiguity, try to avoid the same name attribute item duplication, for similar attribute names, should try to unify the attribute name, if necessary, add notes in the "default comment" column. 2) When creating classification attributes, attributes of the same period and purpose should be pre-defined with an attribute ID range to define attribute ID without conflict, such as resource library, attribute ID can be assigned within 4000-5000.

[0061] Create classification, the following requirements are required when creating classification in Teamcenter classification management module: 1) Create the tree structure of the repository, the classification that needs to store the knowledge resource instance is set as the storage class, and the parent class is set as the abstract class. The name of the classification ID uses English abbreviations or custom characters that are easy to identify, while taking into account the parent classification ID name, to facilitate the maintenance and use of the repository. 2) For multi-level classification, after combining all types and quantities, reasonable classification should be performed during the warehousing work. The parent framework should be built first, then the sub-classification is established, and then the ICO (classification object) is imported. 3) The creation of classification is generally completed in batches, and the subsequent use and maintenance generally do not modify the classification of the large class.

[0062] Configure classification, configure the classification attribute according to the classification table repository, and each classification child node inherits the attribute of the classification. If different subclasses have different attributes, you can separately allocate the required classification attributes to the corresponding subclass. Generally speaking, the abstract class usually defines the shared attributes of the storage class, and all subclasses of the abstract class will inherit the attribute items of the abstract class. The storage class is defined by the combination of the inherited attributes of the parent class and the attributes specific to the storage class.

[0063] Add attributes to classification, in addition, if the classification attribute and the attribute expression in the knowledge resource component master attribute table and the knowledge resource component version master attribute table are consistent, the classification attribute should be configured as a reference attribute, so as to directly reference the attribute value in the component, so as to ensure that the values displayed in the component and the classification are consistent.

[0064] Create classification view, Teamcenter defines classification view including: User View, Role View, ProjectView, Group View, Default View, etc. The creation and definition of classification view can be performed for each classification node. According to Table 2, the order of the classification attribute of the repository is defined. When configuring the classification view, according to the relevance of the attributes, the display and display order of each attribute are reasonably adjusted. If necessary, use layout markers to layout the classification attributes of each classification node, so as to facilitate viewing in the classification.

[0065] Database construction, maintenance and retrieval, taking the creation of the repository as an example, the operation model of resource database construction and resource entry, addition, deletion, modification and query is introduced.

[0066] Data collation, according to the machining process characteristics of the parts type of the key structural parts of the diesel engine, customize the machining parameters including: tool material, part material, cutting method, tool diameter, tool length, feed speed, spindle speed, cutting depth, step distance, etc.

[0067] Tool attributes and geometric parameters, according to the actual business and tool types, characteristics of tool definition tool attributes and geometric parameters.

[0068] Cutting parameter library data parameters, cutting parameter optimization needs to combine tool library information, which can facilitate the call and management of verified cutting data, and apply it to the associated tool trajectory operation. Using the cutting parameter optimization library, the processing parameter information can be optimized according to the part type, material, tool and other related information, and assigned to the corresponding process data.

[0069] When the process is prepared, the process engineer first selects the corresponding process step according to the information of the numerical control process chart under the numerical control, and the number information is an important classification information in the classification attribute. The table below is a web format system readable form information. After classification according to the form attribute information, the TC optimizes the parameters and assigns them to NX.

[0070] According to the existing process type, part type, machining material, tool type, sort out the cutting parameter library structure, perfect all the cutting information required for parameter optimization, and establish a cutting parameter library that meets the needs of NX CAM numerical control programming.

[0071] Data management, in the parameter information selection, the part attribute information of the tool used by the optimized parameters needs to be extracted, and the parameter attribute used in the extraction process needs to be assigned and queried. When querying data calls the tool library, it is simplified to tool type, specification, material, manufacturer and other information, and other tool attributes directly map the existing tool library information.

[0072] Data query and statistical technology, research on the data query and statistical technology of intelligent tool optimization business system based on PDM system, realize the query and statistical management of intelligent tool optimization application, statistical tooling state, realize the input condition query browsing POM system tool information, and support output custom report, accumulate tool application experience, facilitate optimization of tool selection.

[0073] Intelligent tool optimization data management, research on intelligent tool optimization data management technology in intelligent tool optimization classification resource library in PDM system, optimize the way of importing tool pictures, Office documents, PDF, UG models and other local PDM system classification library, and support simulation call.

[0074] By developing intelligent tool optimization data import function, batch importing special numerical control tool related data, including pictures, office documents, PDF, UG models, etc., to PDM system. First, collect data into Excel file, and import it to PDM system and classification library through program.

[0075] On the basis of the current intelligent tool preferred classification resource library, an intelligent tool preferred library is constructed, and when an MBD process is compiled, the intelligent tool preferred library is preferentially pushed.

[0076] Process design selection, according to part, process, process information query corresponding process content, process resource and parameter information, user selection, support process content editing, process device reselection assignment and parameter information modification. In the process of three-dimensional process design, according to the characteristics of the processed features, the required tool and machining parameter are automatically recommended, and the rapid optimization of the tool is realized.

[0077] Select a row of process information, click "match process resource" to enter the process resource matching and selection interface, as shown in the following figure, the selected row of process resource is expanded according to type, which can be matched with TC resource library, and after successful matching, click "application" to temporarily store the item information of the resource, which is convenient for subsequent assignment of process resource.

[0078] NC programming cutting parameter selection, cutting parameter library is uniformly managed in Teamcenter, and suitable machining cutting parameters such as feed speed, spindle speed, cutting depth, line spacing and other machining parameters are recommended by querying and screening the classified information of machining parts, materials, tools, tool marks, features, etc., and the cutting parameters are assigned to the corresponding specific program group and operation process in NXCAM programming template.

[0079] The NX CAM programming environment and the cutting parameter library integration capability are realized, the data definition of the product can be inherited. In NX CAM programming, the cutting parameter library data can be automatically called to realize the acquisition and reuse of NC machining programming knowledge. The repeated input of machining parameters is reduced, and the programming efficiency and the standardization of NC programming are improved.

[0080] Cutting parameter optimization, NX environment calling cutting parameter optimization interface, the user can modify or call from the resource library.

[0081] According to another aspect of the present application, a tool intelligent optimization device is provided, comprising a memory, a processor and a tool intelligent optimization program stored in the memory and executable on the processor, and when the tool intelligent optimization program is executed by the processor, the steps of the tool intelligent optimization method are realized.

[0082] According to the present application, a computer storage medium is further provided.

[0083] The tool intelligent optimization program is stored on the computer storage medium, and when the tool intelligent optimization program is executed by the processor, the steps of the tool intelligent optimization method are realized.

[0084] The method implemented when the tool intelligent selection program running on the processor is executed can refer to the embodiments of the tool intelligent selection method of the present application, and will not be described here again.

[0085] The present application also provides a computer program product.

[0086] The computer program product of the present application comprises a tool intelligent selection program, which, when executed by a processor, implements the steps of the tool intelligent selection method as described above.

[0087] The method implemented when the tool intelligent selection program running on the processor is executed can refer to the embodiments of the tool intelligent selection method of the present application, and will not be described here again.

[0088] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software and a necessary general hardware platform, and of course, they can also be implemented by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, or an optical disk) as described above, and includes a number of instructions for making a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the embodiments of the present application.

[0089] The above description is only exemplary embodiments of the present application, and is not intended to limit the protection scope of the present application, which is defined by the appended claims.

Claims

1. A device for intelligent selection of cutting tools, wherein, include: The input module is used to input the process data to be processed. The input module includes: a structured parts process module, which is used to construct structured process data for parts; and a CNC machining chart module, which is used to guide the refinement of CNC machining methods and parameter data. The processing module is used for processing knowledge reasoning and processing parameter recommendation. The processing module includes: a processing template reasoning module, used to reason about the processing procedures and processes of parts; a tool reasoning module, used to reason about tool type and tool parameters; and a cutting parameter recommendation module, used to reason about processing methods and cutting parameters. The output module is used for tool selection and machining parameter assignment. The output module includes: a machining template selection module for selecting part machining templates; a tool selection module for selecting machining tools; and a cutting parameter assignment module for assigning cutting parameters to the corresponding operation.

2. The intelligent tool selection device as described in claim 1, wherein, Based on the Teamcenter In-class application, the processing module customizes machining parameters according to the part types and machining process characteristics of key structural components of the diesel engine, including: tool material, part material, cutting method, tool diameter, tool length, feed rate, spindle speed, depth of cut, and step distance.

3. The intelligent tool selection device as described in claim 1, wherein, The tool inference module includes a tool library, which is configured with selectable tool information such as tool type, tool code, tool manufacturer, tool specifications, tool grade, tool or insert material, tool diameter, number of cutting edges, cutting edge length, total tool length, cooling method, and tool life. The types of cutting tools include drills, reamers, milling cutters, turning tools, inserts, taps, dies, cutting tools, tool accessories, planers, and boring tools.

4. The intelligent tool selection device as described in claim 1, wherein, The cutting parameter recommendation module includes a cutting parameter library. Based on existing process types, part types, machining materials, and tool types, the library structure is streamlined, and all cutting information required for optimal parameters is improved to establish a cutting parameter library that meets the needs of NX CAM CNC programming. The cutting parameter library includes data parameters such as spindle speed, cutting speed, feed per tooth (fz), feed rate, depth of cut, width of cut / row spacing, and tool movement speed. The data parameters in the cutting parameter library are combined with tool library information to facilitate quick and easy access and management of verified cutting data and its application to associated toolpath operations. Using the cutting parameter library, machining parameter information can be optimized based on the part type, material, and tool-related information and assigned to the process data to be processed.

5. A method for intelligent selection of cutting tools, wherein, Includes the following steps: Data input: The system reads process data, process models, and process information; CNC machining charts are used to determine equipment, materials, cutting tools, and features, and to define classification attribute information. Template invocation: The system retrieves processing templates based on category attribute information. Tool selection is based on knowledge reasoning, which calls recommended tools. The cutting parameters are assigned based on knowledge reasoning to obtain recommended optimal cutting parameters; Generate toolpaths by creating toolpaths based on templates, machining geometry, and cutting parameters; Post-processing involves calling the corresponding machine tool post-processor through the processing equipment to output the NC digital control program. Document output and approval: Output CNC machining documents and complete the approval process.

6. The method for intelligent tool selection as described in claim 5, wherein, When machining icons using CNC machining, machining parameters are customized based on the part type and machining process characteristics of key structural components of the diesel engine, including: tool material, part material, cutting method, tool diameter, tool length, feed rate, spindle speed, depth of cut, and step distance.

7. The intelligent tool selection device as described in claim 5, wherein, Tool recall is based on a tool library, which includes selectable tool information such as tool type, tool code, tool manufacturer, tool specifications, tool grade, tool or insert material, tool diameter, number of cutting edges, cutting edge length, total tool length, cooling method, and tool life. The types of cutting tools include drills, reamers, milling cutters, turning tools, inserts, taps, dies, cutting tools, tool accessories, planers, and boring tools.

8. The intelligent tool selection device as described in claim 5, wherein, When assigning cutting parameters, the selection of cutting parameters is based on the cutting parameter library. The library structure is streamlined according to existing process types, part types, machining materials, and tool types. All cutting information required for optimal parameter selection is improved, establishing a cutting parameter library that meets the needs of NX CAM CNC programming. The cutting parameter library includes data parameters such as spindle speed, cutting speed, feed per tooth (fz), feed rate, depth of cut, width of cut / row spacing, and tool movement speed. These data parameters are combined with tool library information to facilitate convenient and quick access and management of verified cutting data, which is then applied to associated toolpath operations. Using the cutting parameter library, machining parameter information can be optimized and assigned to the process data to be processed based on information related to part type, material, and tool.

9. An electronic device, comprising: A memory, a processor, and a tool intelligent selection program stored in the memory and executable on the processor, wherein the tool intelligent selection program, when executed by the processor, implements the steps of the tool intelligent selection method as described in any one of claims 5 to 8.

10. A computer storage medium, wherein, The computer storage medium stores a tool intelligent selection program, which, when executed by a processor, implements the steps of the tool intelligent selection method as described in any one of claims 5 to 8.

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