Similarity evaluation method for multi-dimensional part machining process

Abstract

The invention discloses a similarity evaluation method for a multi-dimensional part machining process, and belongs to the field of machining processes. Acquiring data of a plurality of sample parts, wherein the data of the sample parts comprises material information of the sample parts, machining process information of the sample parts and finished product parameters of the sample parts; classifying the multiple pieces of sample part data based on the material information of the sample parts to obtain the classification of each piece of sample part data; creating similarity evaluation priorities of the sample parts based on the processing process information of the sample parts; and based on the classification of the sample part data and in combination with the finished product parameters of the sample parts, performing similarity evaluation on the processing process information of the sample parts to obtain the similarity between different sample part data. The similarity evaluation method for the multi-dimensional part machining process has the beneficial effect that the similarity evaluation method for the multi-dimensional part machining process is provided for efficiently evaluating existing process knowledge.

Description

Technical Field

[0001] The present application relates to the field of machining processes, and in particular, to a similarity evaluation method for multi-dimensional part machining processes. Background Art

[0002] As traditional manufacturing develops towards digitalization, automation, and intelligence, the connection between industrialization and informatization has become increasingly close, which has strongly promoted the vigorous development of advanced manufacturing technology. At the same time, the advancement of intelligent manufacturing has also triggered a huge change in the production mode. One of them is that the dominant position of people in the production execution process is gradually replaced by intelligent, information-based, and networked machines. Under the traditional manufacturing model, manufacturing workers complete manufacturing tasks by operating related manufacturing equipment according to the content of the product process documents. In this process, the skills and experience of manufacturing workers play a fault-tolerant role in process information. Under the intelligent manufacturing model, the production execution system and related equipment have become the main recipients and executors of process information, requiring more accurate and detailed manufacturing processes to guide the correct and efficient production, which poses new challenges to the quality and efficiency of process design.

[0003] Diversified market demands have driven many manufacturing companies to change their production model from mass production to a multi-variety, small-batch production model. Companies need to master more process knowledge to meet the rapid manufacturing of new products and achieve rapid response to the market. At the same time, as the application of digital information technology in enterprises gradually matures, a large amount of production and manufacturing data is stored in enterprise databases. These data are huge in quantity, highly complex, and have a variety of storage structures. They are often difficult to use directly, resulting in the problem of "rich data and poor knowledge" in the field of process design.

[0004] Process design is the bridge between product design and manufacturing. It is one of the core businesses of intelligent manufacturing systems and a key factor affecting product quality, production costs, and development cycles. Statistical analysis shows that in new product development, only about 20% are completely new designs, while about 80% of product designs can be obtained by fully or partially reusing existing design results. This feature also exists in the process of product process design. Therefore, how to effectively collect and store messy process knowledge, how to achieve accurate matching and retrieval of similar parts, and how to effectively reuse the process routes and process knowledge of existing parts are important links in the upgrade of traditional manufacturing industries to informatization and intelligence.

[0005] Currently, there is no similarity evaluation method for multi-dimensional part machining processes that can efficiently utilize existing process knowledge. Summary of the invention

[0006] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] In order to solve the technical problems mentioned in the above background technology section, some embodiments of the present application provide a similarity evaluation method for multi-dimensional part machining processes, including:

[0008] Acquire a plurality of sample part data, wherein the sample part data includes material information of the sample parts, processing information of the sample parts, and finished product parameters of the sample parts;

[0009] Classifying a plurality of sample part data based on material information of the sample parts to obtain a classification of each sample part data;

[0010] Creating similarity evaluation priorities of sample parts based on machining process information of sample parts;

[0011] Based on the classification of sample part data and combined with the finished product parameters of the sample parts, the similarity of the processing information of the sample parts is evaluated to obtain the similarity between different sample part data;

[0012] A similarity evaluation model is created based on the classification of each sample part data, the finished product parameters of each sample part, the similarity evaluation priority of each sample part data, and the similarity between different sample part data;

[0013] Obtain data of a sample part to be tested;

[0014] Parsing the sample part data to be tested to obtain the material information to be tested and the target finished product parameters to be tested in the sample part data to be tested;

[0015] Inputting the obtained material information of the part to be tested and the target finished product parameters of the part to be tested into the similarity evaluation model, starting the similarity evaluation model, and obtaining at least one sample part data output by the similarity evaluation model;

[0016] Based on the similarity, all sample part data are sorted from high to low, and the first N sample part data and the corresponding similarity are output.

[0017] Furthermore, the multiple sample part data are classified based on the material information of the sample parts to obtain the classification of each sample part data, including:

[0018] Create multiple material databases based on different material types;

[0019] Select a sample part data;

[0020] Parsing the sample part data to obtain material information in the sample part data, wherein the material information includes material type;

[0021] Incorporate the sample part data into the corresponding material database based on the material type;

[0022] Return to the step of selecting a sample part data until each sample part data has been selected once.

[0023] Furthermore, the creating similarity evaluation priority of the sample parts based on the processing information of the sample parts includes:

[0024] Parsing the processing information of the sample parts to obtain shape information of the sample parts, size information of the sample parts, processing method information of the sample parts, machine tool processing information of the sample parts, and process type information of the sample parts in the processing information of the sample parts;

[0025] The shape information of the sample parts is taken as the first evaluation priority;

[0026] Consider the dimensional information of the sample parts as the second evaluation priority;

[0027] The information on the treatment methods of sample parts is taken as the third evaluation priority;

[0028] The machine tool processing information of the sample parts is taken as the fourth evaluation priority;

[0029] The process type information of the sample parts is taken as the fifth evaluation priority.

[0030] Furthermore, the similarity evaluation of the processing information of the sample parts is performed based on the classification of the sample part data and in combination with the finished product parameters of the sample parts to obtain the similarity between different sample part data, including:

[0031] Select a material database;

[0032] Select a sample part data in the material database;

[0033] Parsing the sample part data to obtain processing type information in the sample part data, wherein the processing type information includes lathe processing type, milling machine processing type, drilling machine processing type and planer processing type;

[0034] Defining the sample part data based on the processing type information to obtain at least one processing type definition of the sample part data;

[0035] Returning to the step of selecting a sample part data in the material database until each sample part data in the material database has been selected once;

[0036] Return to selecting a material database until each material database has been selected once.

[0037] Furthermore, the similarity evaluation of the processing information of the sample parts based on the classification of the sample part data and in combination with the finished product parameters of the sample parts to obtain the similarity between different sample part data also includes:

[0038] Select a sample part data from a material database;

[0039] Parsing the sample part data to obtain at least one processing type in the sample part data;

[0040] Select a processing type;

[0041] Using the processing type as an index, searching the material database to obtain at least one search result;

[0042] All search results are evaluated for similarity with the processing type to obtain multiple similarity evaluations;

[0043] Return to selecting a processing type until each processing type has been selected once;

[0044] Return to the step of selecting a sample part data in a material database until each sample part data in each material database has been selected once.

[0045] Furthermore, all the search results are evaluated for similarity with the processing type to obtain multiple similarity evaluations, including:

[0046] Select a search result;

[0047] Parsing the search results to obtain shape information of the sample parts, size information of the sample parts, processing method information of the sample parts, machine tool processing information of the sample parts, and process type information of the sample parts in the search results;

[0048] Parse the processing type to obtain shape information of sample parts in the processing type, size information of sample parts, processing method information of sample parts, machine tool processing information of sample parts, and process type information of sample parts;

[0049] Based on the shape information of the sample part in the processing type and the shape information of the sample part in the search result, a similarity evaluation is performed to obtain the similarity between the shape information of the sample part in the processing type and the shape information of the sample part in the search result;

[0050] Performing a similarity evaluation based on the size information in the processing type and the size information in the search result to obtain the similarity between the size information in the processing type and the size information in the search result;

[0051] Based on the processing method information in the processing type and the processing method information in the search result, a similarity evaluation is performed to obtain the similarity between the processing method information in the processing type and the processing method information in the search result;

[0052] Based on the similarity evaluation of the machine tool processing information in the processing type and the machine tool processing information in the search result, the similarity between the machine tool processing information in the processing type and the machine tool processing information in the search result is obtained;

[0053] Performing a similarity evaluation based on the process type information in the processing type and the process type information in the search result, obtaining the similarity between the process type information in the processing type and the process type information in the search result;

[0054] Return to selecting a search result until each search result has been selected once.

[0055] Furthermore, the similarity evaluation model is created based on the classification of each sample part data, the finished product parameters of each sample part, the similarity evaluation priority of each sample part data, and the similarity evaluation between different sample part data, including:

[0056] The processing type similarity evaluation formula is as shown in Formula 1;

[0057] S=A×K1+B×K2+C×K3+D×K4+E×K5

[0058] Formula 1;

[0059] 其中， S 为该加工类型与一个检索结果的相似度； A 为该加工类型中 The similarity between the shape information of the sample part of B and the shape information of the sample part in the retrieval result; 为该加工类型中的尺寸信息与该检索结果中的尺寸信息的相似度； C 为 The similarity between the processing method information in the processing type and the processing method information in the search result; D 为该加工类型中的机床加工信息与该检索结果中的机床加工信息的相似度； E 为该加工类型中的工艺类型信息与该检索结果中的工艺类型信息的相似度； K 1为该加工类型中的样本零件的形状信息与该检索结果中的样本零件的形状信息的相似度的权重； K 2为该加工类型中的尺寸信息与该检索结果中的尺寸信息的相似度的权重； K 3为该加工类型中的处理方式信息与该检索结果中的处理方式信息的相似度的权重； K 4该加工类型中的机床加工信息与该检索结果中的机床加工信息的相似度的权重； K5为该加工类型 The weight of the similarity between the process type information in the search result and the process type information in the search result.

[0060] Further, in the inputting of the obtained material information of the part to be tested and the target finished product parameters of the part to be tested into the similarity evaluation model, the similarity evaluation model is started to obtain at least one sample part data output by the similarity evaluation model, the similarity evaluation method of the multi-dimensional part machining process also includes:

[0061] Based on the material information of the part to be tested and the target finished product parameters, at least one processing type of the part to be tested is obtained;

[0062] All obtained processing types are input into the similarity evaluation model respectively.

[0063] The beneficial effect of the present application is that it provides a similarity evaluation method for multi-dimensional part machining processes that makes efficient use of existing process knowledge. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The illustrative embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application.

[0065] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.

[0066] In the attached picture:

[0067] Figure 1 A schematic flow chart of a method for similarity evaluation of multi-dimensional part machining processes provided in one embodiment of the present application. DETAILED DESCRIPTION

[0068] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0069] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0070] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0071] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0072] like Figure 1 As shown, in one embodiment of the present application, the similarity evaluation method of the multi-dimensional part machining process includes the following S100 to S900:

[0073] S100, obtaining a plurality of sample part data, wherein the sample part data includes material information of the sample parts, processing information of the sample parts, and finished product parameters of the sample parts.

[0074] S200, classifying a plurality of sample part data based on material information of the sample parts to obtain a classification of each sample part data.

[0075] Specifically, the material information includes the type of material, such as metals, plastics, etc. Further metal types include copper metal, copper alloy, aluminum metal, aluminum alloy, etc.

[0076] S300: creating a similarity evaluation priority of the sample parts based on the processing information of the sample parts.

[0077] Specifically, the processing process information includes machine tool information, processing technology cards and other information.

[0078] S400, based on the classification of the sample part data and in combination with the finished product parameters of the sample parts, the similarity of the processing information of the sample parts is evaluated to obtain the similarity between different sample part data.

[0079] S500, creating a similarity evaluation model based on the classification of each sample part data, the finished product parameters of each sample part, the similarity evaluation priority of each sample part data, and the similarity between different sample part data.

[0080] Specifically, the classification of each sample part data, the finished product parameters of each sample part, the similarity evaluation priority of each sample part data, and the similarity between different sample part data are used as training data to train the similarity evaluation model to obtain a trained similarity evaluation model.

[0081] S600, obtaining data of a sample part to be tested.

[0082] S700, parsing the sample part data to be tested, and obtaining the material information to be tested and the target finished product parameters to be tested in the sample part data to be tested.

[0083] S800, inputting the obtained material information of the part to be tested and the target finished product parameters of the part to be tested into a similarity evaluation model, starting the similarity evaluation model, and obtaining at least one sample part data output by the similarity evaluation model.

[0084] S900, sorting all sample part data from high to low based on similarity, and outputting the first N sample part data and the corresponding similarity.

[0085] In this embodiment, multiple sample part data are first obtained, each of which has a complete processing process, and the data in the complete processing process is correspondingly saved, and then the part data is classified differently based on the material information so that each sample part data has a corresponding classification, and the same classification is used as the basis for similarity evaluation, and then different similarity evaluation priorities are established based on the processing process information, and pairwise similarity comparisons are performed between multiple sample part data in the same classification, so as to obtain multiple similarities, wherein the similarity evaluation priority is used as an auxiliary element in the similarity calculation process to improve the reliability of the similarity calculation; then the similarity evaluation model is trained by using the classification of each sample part data, the finished product parameters of each sample part, the similarity evaluation priority of each sample part data, and the similarity between different sample part data as training data, so that the trained similarity evaluation model has the functions of corresponding retrieval and similarity calculation.

[0086] In one embodiment of the present application, the classification of the plurality of sample part data based on the material information of the sample part to obtain the classification of each sample part data includes the following steps S201 to S205:

[0087] S201, creating multiple material databases based on different material types.

[0088] S202, selecting a sample part data.

[0089] S203, parsing the sample part data to obtain material information in the sample part data, wherein the material information includes material type.

[0090] S204: Incorporate the sample part data into a corresponding material database based on the material type.

[0091] S205, returning to the step of selecting a sample part data, until each sample part data has been selected once.

[0092] Specifically, the material database refers to a material type database, and sample part data of the same material type are summarized in the same material database.

[0093] If the same part is composed of multiple different material combinations, the data of the different material parts of the part will be split and summarized in the corresponding material database.

[0094] In this embodiment, multiple material databases are created by first establishing different material types, and all sample data are stored in their corresponding databases accordingly. When performing similarity calculations, the corresponding material databases can be retrieved to obtain multiple sample part data with similarity evaluation values, thereby reducing the amount of data processing and improving the data processing speed.

[0095] In one embodiment of the present application, it is characterized in that the similarity evaluation priority of the sample parts is created based on the processing information of the sample parts, including the following S301 to S305:

[0096] S301, parsing the processing information of the sample part to obtain the shape information of the sample part, the size information of the sample part, the processing method information of the sample part, the machine tool processing information of the sample part and the process type information of the sample part.

[0097] S301, taking the shape information of the sample part as the first evaluation priority.

[0098] S302, taking the size information of the sample parts as the second evaluation priority.

[0099] S303: Using the processing method information of the sample parts as the third evaluation priority.

[0100] S304, taking the machine tool processing information of the sample part as the fourth evaluation priority.

[0101] S305, taking the process type information of the sample part as the fifth evaluation priority.

[0102] In this embodiment, the shape information of the sample parts, the size information of the sample parts, the processing method information of the sample parts, the machine tool processing information of the sample parts and the process type information of the sample parts in the processing process information of the sample parts are compared to obtain the influence of each factor on the similarity. The factor with a greater influence on the similarity result has a higher corresponding evaluation priority.

[0103] In one embodiment of the present application, the similarity evaluation of the processing information of the sample parts based on the classification of the sample part data and in combination with the finished product parameters of the sample parts to obtain the similarity between different sample part data includes the following S401 to S406:

[0104] S401, select a material database.

[0105] S402, selecting a sample part data in the material database.

[0106] S403, parsing the sample part data to obtain processing type information in the sample part data, wherein the processing type information includes lathe processing type, milling machine processing type, drilling machine processing type and planer processing type.

[0107] S404: Define the sample part data based on the processing type information to obtain at least one processing type definition of the sample part data.

[0108] S405, returning to the step of selecting a sample part data in the material database until each sample part data in the material database has been selected once.

[0109] S406, returning to select a material database until each material database has been selected once.

[0110] Specifically, a sample part data includes at least one processing type information, because a part must include at least one processing technology. At the same time, under normal conditions, a part often requires multiple processing technologies from a blank state to a finished product state. For example, the blank is first turned on the end face and the outer cylindrical surface by a CNC lathe. Then the outer cylindrical surface is milled by a CNC milling machine, and then the part is drilled by a drilling lathe. Therefore, there is a possibility that the same part or different parts of a part may have multiple processing types.

[0111] In this embodiment, the similarities between different CNC machining types are comparable, so the sample part data is classified again on the basis of material classification, and the machining types are further refined to make the similarity comparison reasonable and practical.

[0112] In one embodiment of the present application, the similarity evaluation of the processing information of the sample parts based on the classification of the sample part data and the finished product parameters of the sample parts is performed to obtain the similarity between different sample part data, and further includes the following S407 to S413:

[0113] S407, selecting a sample part data in a material database.

[0114] S408, parsing the sample part data to obtain at least one processing type in the sample part data.

[0115] S409, select a processing type.

[0116] S410, using the processing type as an index, searching the material database to obtain at least one search result.

[0117] S411, performing similarity evaluations on all search results and the processing type to obtain multiple similarity evaluations.

[0118] S412, returning to the step of selecting a processing type until each processing type has been selected once.

[0119] S413, returning to the step of selecting a sample part data in a material database until each sample part data in each material database has been selected once.

[0120] Specifically, a search result refers to a sample part data; a sample part data may have multiple processing types, and each processing type may have multiple search results, and the similarity between each search result and the processing type may be the same or different.

[0121] In this embodiment, the material database is searched by using a processing type as an index, and then the search results for multiple parts with the same or similar materials and the same processing type are obtained. In this case, the similarity of the processing technology has evaluation significance and value, and the sample part data and the search results are comprehensively and multi-dimensionally evaluated in combination with similarities of different priorities.

[0122] In one embodiment of the present application, all search results are evaluated for similarity with the processing type to obtain multiple similarity evaluations, including the following S411a to S411i:

[0123] S411a, select a search result.

[0124] S411b, parsing the search result to obtain shape information of the sample parts, size information of the sample parts, processing method information of the sample parts, machine tool processing information of the sample parts, and process type information of the sample parts in the search result.

[0125] S411c, analyzing the processing type to obtain shape information of the sample parts in the processing type, size information of the sample parts, processing method information of the sample parts, machine tool processing information of the sample parts, and process type information of the sample parts.

[0126] S411d, performing similarity evaluation based on the shape information of the sample part in the processing type and the shape information of the sample part in the search result, to obtain the similarity between the shape information of the sample part in the processing type and the shape information of the sample part in the search result.

[0127] S411e, performing a similarity evaluation based on the size information in the processing type and the size information in the search result, to obtain the similarity between the size information in the processing type and the size information in the search result.

[0128] S411f, performing similarity evaluation based on the processing method information in the processing type and the processing method information in the search result, to obtain the similarity between the processing method information in the processing type and the processing method information in the search result.

[0129] S411g: Perform similarity evaluation based on the machine tool processing information in the processing type and the machine tool processing information in the search result to obtain the similarity between the machine tool processing information in the processing type and the machine tool processing information in the search result.

[0130] S411h: Perform similarity evaluation based on the process type information in the processing type and the process type information in the search result to obtain the similarity between the process type information in the processing type and the process type information in the search result.

[0131] S411i, returning to the process of selecting a search result until each search result has been selected once.

[0132] In this embodiment, the shape information, size information, processing method information, machine tool processing information and process type information of sample parts of the same processing type are evaluated separately and individually with other search results to obtain the corresponding similarity. Here, the similarity evaluation is further refined to make the similarity evaluation more stable and accurate, and less susceptible to individual extreme data.

[0133] In one embodiment of the present application, the similarity evaluation model is created based on the classification of each sample part data, the finished product parameters of each sample part, the similarity evaluation priority of each sample part data, and the similarity evaluation between different sample part data, including the following S501:

[0134] S501, the processing type similarity evaluation formula is as shown in Formula 1;

[0135] S=A×K1+B×K2+C×K3+D×K4+E×K5

[0136] Formula 1;

[0137] 其中， S 为该加工类型与一个检索结果的相似度； A 为该加工类型中的样本零件的形状信息与该检索结果中的样本零件的形状信息的相似度； B 为该加工类型中的尺寸信息与该检索结果中的尺寸信息的相似度； C 为该加工类型中的处理方式信息与该检索结果中的处理方式信息的相似度； D 该加工类型中 The weight of the similarity between the machine tool processing information in the processing type and the machine tool processing information in the search result; E is the similarity between the process type information in the processing type and the process type information in the search result; K1 is the weight of the similarity between the shape information of the sample parts in the processing type and the shape information of the sample parts in the search result; K2 is the weight of the similarity between the size information in the processing type and the size information in the search result; K3 is the weight of the similarity between the processing method information in the processing type and the processing method information in the search result; K4 is the weight of the similarity between the machine tool processing information in the processing type and the machine tool processing information in the search result; K5 is the weight of the similarity between the process type information in the processing type and the process type information in the search result.

[0138] In this embodiment, the shape information of the sample parts in this processing type may refer to the shape of the blank and the shape of the part after processing, and both are compared for similarity separately; the size information in this processing type may refer to the size of the processing part, such as the size of the outer diameter, the diameter of the inner hole, or the size of the slot, etc.; the processing method information in this processing type may refer to the heat treatment process that the workpiece undergoes before or after processing, etc.; the machine tool processing information in this processing type may refer to the operating data information of the part in different machine tools used in different processing stages, such as spindle speed, feed rate, etc.; the process type information in this processing type may refer to turning, milling, drilling, planing, etc.

[0139] In one embodiment of the present application, the material information of the part to be tested and the target finished product parameters of the part to be tested are input into the similarity evaluation model, the similarity evaluation model is started, and at least one sample part data output by the similarity evaluation model is obtained. The similarity evaluation method of the multi-dimensional part machining process also includes the following S810 to 820:

[0140] S810, obtaining at least one processing type of the part to be tested based on material information of the part to be tested and target finished product parameters.

[0141] S810: Input all obtained processing types into a similarity evaluation model respectively.

[0142] Specifically, the target finished product parameters are the ultimate goal of a part processing, which include multiple aspects such as surface roughness and dimensional accuracy, and the target finished product parameters are achieved by combining different processing techniques in multiple stages.

[0143] In this embodiment, the processing completion difference of a part is divided into multiple sections, each section can correspond to a different processing type, and the similarity matching of each processing type is performed to obtain the similarity matching of a complete section of processing. Based on this, the similarity matching results of the part at different stages can be obtained.

[0144] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A similarity evaluation method for multi-dimensional part machining processes, characterized in that: The similarity evaluation method of the multi-dimensional part machining process includes: Acquire a plurality of sample part data, wherein the sample part data includes material information of the sample parts, processing information of the sample parts, and finished product parameters of the sample parts; Classifying a plurality of sample part data based on material information of the sample parts to obtain a classification of each sample part data; Creating similarity evaluation priorities of sample parts based on machining process information of sample parts; Based on the classification of sample part data and combined with the finished product parameters of the sample parts, the similarity of the processing information of the sample parts is evaluated to obtain the similarity between different sample part data; A similarity evaluation model is created based on the classification of each sample part data, the finished product parameters of each sample part, the similarity evaluation priority of each sample part data, and the similarity between different sample part data; Obtain data of a sample part to be tested; Parsing the sample part data to be tested to obtain the material information to be tested and the target finished product parameters to be tested in the sample part data to be tested; Inputting the obtained material information of the part to be tested and the target finished product parameters of the part to be tested into the similarity evaluation model, starting the similarity evaluation model, and obtaining at least one sample part data output by the similarity evaluation model; Based on the similarity, all sample part data are sorted from high to low, and the first N sample part data and the corresponding similarity are output.

2. The similarity evaluation method of multi-dimensional part machining process according to claim 1 is characterized in that: The method of classifying the plurality of sample part data based on the material information of the sample part to obtain a classification of each sample part data includes: Create multiple material databases based on different material types; Select a sample part data; Parsing the sample part data to obtain material information in the sample part data, wherein the material information includes material type; Incorporate the sample part data into the corresponding material database based on the material type; Return to the step of selecting a sample part data until each sample part data has been selected once.

3. The similarity evaluation method of multi-dimensional part machining process according to claim 2 is characterized in that: The similarity evaluation priority of the sample parts is created based on the processing information of the sample parts, including: Parsing the processing information of the sample parts to obtain shape information of the sample parts, size information of the sample parts, processing method information of the sample parts, machine tool processing information of the sample parts, and process type information of the sample parts in the processing information of the sample parts; The shape information of the sample parts is taken as the first evaluation priority; Consider the dimensional information of the sample parts as the second evaluation priority; The information on the treatment methods of sample parts is taken as the third evaluation priority; The machine tool processing information of the sample parts is taken as the fourth evaluation priority; The process type information of the sample parts is taken as the fifth evaluation priority.

4. The similarity evaluation method of multi-dimensional part machining process according to claim 3 is characterized in that: The similarity evaluation of the processing information of the sample parts is performed based on the classification of the sample part data and in combination with the finished product parameters of the sample parts, so as to obtain the similarity between different sample part data, including: Select a material database; Select a sample part data in the material database; Parsing the sample part data to obtain processing type information in the sample part data, wherein the processing type information includes lathe processing type, milling machine processing type, drilling machine processing type and planer processing type; Defining the sample part data based on the processing type information to obtain at least one processing type definition of the sample part data; Returning to the step of selecting a sample part data in the material database until each sample part data in the material database has been selected once; Return to selecting a material database until each material database has been selected once.

5. The similarity evaluation method of multi-dimensional part machining process according to claim 4 is characterized in that: The similarity evaluation of the processing information of the sample parts based on the classification of the sample part data and in combination with the finished product parameters of the sample parts to obtain the similarity between different sample part data also includes: Select a sample part data from a material database; Parsing the sample part data to obtain at least one processing type in the sample part data; Select a processing type; Using the processing type as an index, searching the material database to obtain at least one search result; All search results are evaluated for similarity with the processing type to obtain multiple similarity evaluations; Return to selecting a processing type until each processing type has been selected once; Return to the step of selecting a sample part data in a material database until each sample part data in each material database has been selected once.

6. The similarity evaluation method of multi-dimensional part machining process according to claim 5 is characterized in that: The similarity evaluation is performed on all the search results and the processing type, and multiple similarity evaluations are obtained, including: Select a search result; Parsing the search results to obtain shape information of the sample parts, size information of the sample parts, processing method information of the sample parts, machine tool processing information of the sample parts, and process type information of the sample parts in the search results; Parse the processing type to obtain shape information of sample parts in the processing type, size information of sample parts, processing method information of sample parts, machine tool processing information of sample parts, and process type information of sample parts; Based on the shape information of the sample part in the processing type and the shape information of the sample part in the search result, a similarity evaluation is performed to obtain the similarity between the shape information of the sample part in the processing type and the shape information of the sample part in the search result; Performing a similarity evaluation based on the size information in the processing type and the size information in the search result to obtain the similarity between the size information in the processing type and the size information in the search result; Based on the processing method information in the processing type and the processing method information in the search result, a similarity evaluation is performed to obtain the similarity between the processing method information in the processing type and the processing method information in the search result; Based on the similarity evaluation of the machine tool processing information in the processing type and the machine tool processing information in the search result, the similarity between the machine tool processing information in the processing type and the machine tool processing information in the search result is obtained; Performing a similarity evaluation based on the process type information in the processing type and the process type information in the search result to obtain the similarity between the process type information in the processing type and the process type information in the search result; Return to selecting a search result until each search result has been selected once.

7. The similarity evaluation method of multi-dimensional part machining process according to claim 6 is characterized in that: The similarity evaluation model is created based on the classification of each sample part data, the finished product parameters of each sample part, the similarity evaluation priority of each sample part data, and the similarity evaluation between different sample part data, including: The processing type similarity evaluation formula is as shown in Formula 1; S=A×K1+B×K2+C×K3+D×K4+E×K5 Formula 1; Among them, S is the similarity between the processing type and a search result; A is the similarity between the shape information of the sample part in the processing type and the shape information of the sample part in the search result; B is the similarity between the size information in the processing type and the size information in the search result; C is the similarity between the processing method information in the processing type and the processing method information in the search result; D is the similarity between the machine tool processing information in the processing type and the machine tool processing information in the search result; E is the similarity between the process type information in the processing type and the process type information in the search result; K1 is the weight of the similarity between the shape information of the sample part in the processing type and the shape information of the sample part in the search result; K2 is the weight of the similarity between the size information in the processing type and the size information in the search result; K3 is the weight of the similarity between the processing method information in the processing type and the processing method information in the search result; K4 is the weight of the similarity between the machine tool processing information in the processing type and the machine tool processing information in the search result; K5 is the weight of the similarity between the process type information in the processing type and the process type information in the search result.

8. The similarity evaluation method of multi-dimensional part machining process according to claim 7 is characterized in that: In the method of inputting the obtained material information of the part to be tested and the target finished product parameters of the part to be tested into the similarity evaluation model, starting the similarity evaluation model, and obtaining at least one sample part data output by the similarity evaluation model, the similarity evaluation method of the multi-dimensional part machining process also includes: Based on the material information of the part to be tested and the target finished product parameters, at least one processing type of the part to be tested is obtained; All obtained processing types are input into the similarity evaluation model respectively.

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