Destructive process knowledge decision-making library system applied to manufacturing industry

Abstract

The invention provides a rational process knowledge decision-making library system applied to a manufacturing industry, which can decompose process characteristics from top to bottom and convert a product manufacturing process from experience knowledge into a knowledge packet to realize reuse. A condition decision library, a process characteristic template library, a system variable library, a processing parameter library, a manufacturing resource library and a manufacturing standard library are respectively constructed, and structured data storage is performed on lean production experiences such as production and manufacturing process schemes, processing flow procedures, manufacturing standards, technical requirements, processing templates, processing parameters, manufacturing resources and the like; through product characteristic matching and big data calculation of the system, automation of product process design is realized, a process route and process step parameters are automatically generated according to product characteristics, and model selection and parameterization creation of manufacturing resources are completed, so that the system has the output capability of a complete manufacturing process scheme, the product process design does not depend on artificial experience any more, and the production efficiency is improved. And the product process stability and the quality consistency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field related to manufacturing processes, and in particular to a reasoning process knowledge decision base system applied to the manufacturing industry. Background Art

[0002] With the rapid development of the manufacturing industry, the current manufacturing industry generally has the problem of multi-variety, small batch, and high discrete manufacturing. The design method of product manufacturing process relying on manual experience in the traditional manufacturing model cannot cope with the market demand of short delivery time, difficult process, high precision, many varieties, small batch, and fast iteration. It is particularly prominent in the process design link. Because the rules are difficult to summarize and the standards are difficult to unify, it is difficult to reuse knowledge and the results are difficult to be consistent, which greatly affects the maximization of process development and production efficiency. In addition, core technology and manufacturing experience are limited by professional technicians, and precision machining technology relies heavily on manual experience, resulting in differences in personnel experience and ability to form knowledge islands. Enterprises face tremendous pressure in digital manufacturing models such as process design, quality control, personnel training, and lean production. The traditional process design model that relies on manual experience cannot cope with the development needs of the high discrete and customized market.

[0003] In view of the above-mentioned defects, there is an urgent need to design a reasoning process knowledge decision-making library system for the manufacturing industry, which can store the experience knowledge of highly skilled technical personnel in a structured manner and realize intelligent process design technology with autonomous decision-making through the system. Summary of the invention

[0004] In order to solve the problems mentioned above, the present invention proposes a reasoning process knowledge decision base system applied to the manufacturing industry, which provides intelligent solutions and digital lean manufacturing models for process design work.

[0005] A reasoning process knowledge decision base system applied to the manufacturing industry, characterized in that: the system comprises: the system from bottom to top comprises: a standard layer, a data layer, an application layer, and an iteration layer; The standard layer is used to standardize manufacturing process knowledge and resource data; The data layer is used to classify and store the standardized knowledge in the standard layer and has maintainability and scalability; The application layer is used to realize the automatic generation of the results of product manufacturing process design by simulating the conditions and result decisions of human brain thinking; The iteration layer is used to achieve rapid improvement and maintenance of the system by integrating the data types that need to be maintained in the system.

[0006] Furthermore, the system also includes several sub-libraries: a condition decision library, a process feature template library, a system variable library, a processing parameter library, a manufacturing resource library, and a manufacturing standard library.

[0007] Furthermore, the conditional decision library is used to set the process plan and parameter logic matching method according to the manufacturing characteristics of the product; the conditional decision library has the process plan combined with different working conditions and product characteristics to configure conditions, and relies on the logical configuration of the tree structure to achieve autonomous decision-making and generate results.

[0008] Furthermore, the process feature template library is used to classify and summarize the features of products in the manufacturing industry, store processing features with manufacturability in the form of three-dimensional models, and realize automatic retrieval and search functions through the feature ID.

[0009] Furthermore, the system variable library is used to store all decision factors and attribute variables.

[0010] Furthermore, the processing parameter library is used to combine different equipment resources and processing characteristics, integrate the processing parameter results into the process results during the autonomous decision-making process of the process plan, and calculate the optimal processing parameters.

[0011] Furthermore, the manufacturing resource library is used to structure the storage of equipment performance parameters, operating speed, processing accuracy, equipment travel, accuracy compensation method, and equipment structure, automatically obtain the performance parameters of the equipment in the process of generating a process plan, and finally output the results based on the capabilities of the equipment resources.

[0012] Furthermore, the manufacturing standard library is used to store the processing step description, step technical requirements, step name, precision compensation method, and part clamping and alignment in a standardized manner, and automatically matches the corresponding manufacturing standard according to the type of manufacturing step after generating the process results.

[0013] The beneficial effects of the present invention are: 1. The present invention can decompose process features from top to bottom through the architectural design of the inferential process knowledge decision library system, and convert the product manufacturing process from empirical knowledge into knowledge packages for reuse; and respectively construct a conditional decision library, a process feature template library, a system variable library, a processing parameter library, a manufacturing resource library, and a manufacturing standard library, and store structured data of lean production experience such as production and manufacturing process plans, processing procedures, manufacturing standards, technical requirements, processing templates, processing parameters, and manufacturing resources around the design business of product manufacturing processes; through the system's product characteristic matching and big data calculation, the automation of product process design is realized, and the process route and process step parameters are automatically generated according to the product characteristics, and the selection and parameterized creation of manufacturing resources are completed, so that the system has the ability to output a complete manufacturing process plan, so that the product process design no longer relies on manual experience, and the product process stability and quality consistency are improved.

[0014] 2. The present invention is applicable to the manufacturing process design business in the highly discrete manufacturing industry. It can effectively solve the problem of knowledge islands formed by differences in personnel experience and ability, as the core technology and manufacturing experience are limited by professional and technical personnel in the discrete manufacturing industry, and the precision machining technology is highly dependent on manual experience. When responding to market demands with a wide variety of products, small batches, and short delivery times, the present invention can enable enterprises to quickly and efficiently complete process design. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is the system architecture diagram of the reasoning process knowledge decision library.

[0016] Figure 2 It is the logic diagram of the conditional decision library.

[0017] Figure 3 This is the structure diagram of the process feature template library.

[0018] Figure 4 This is a classification diagram of the system variable library.

[0019] Figure 5 This is the logic diagram of the processing parameter library.

[0020] Figure 6 A matrix table for manufacturing resource libraries.

[0021] Figure 7 Classification diagram for manufacturing standard library. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the embodiments.

[0023] The following examples are used to illustrate the present invention, but they cannot be used to limit the scope of protection of the present invention. The conditions in the examples can be further adjusted according to specific conditions. Simple improvements to the method of the present invention under the premise of the concept of the present invention belong to the scope of protection claimed in the present invention.

[0024] like Figure 1 As shown, a reasoning process knowledge decision library system applied to the manufacturing industry includes: the system is respectively: standard layer, data layer, application layer, iteration layer from bottom to top. The standard layer can realize the standardization of manufacturing process knowledge and resource data. The data layer can classify and store the knowledge of the standard layer and have maintainability and scalability. In the application layer, it is necessary to build logic to realize the conditions and result decisions that simulate human brain thinking, and use the system to realize the automatic generation of the results of product manufacturing process design. In view of the maintainability and improvement of system knowledge, the interface of the iteration layer is used to integrate the types of maintenance data to achieve rapid improvement and maintenance of system solutions.

[0025] The system also includes several sub-libraries: a condition decision library, a process feature template library, a system variable library, a processing parameter library, a manufacturing resource library, and a manufacturing standard library.

[0026] The conditional decision library, because the processing schemes and process parameters of products in the manufacturing industry are logical, how to set the process scheme and parameter logical matching method according to the manufacturing characteristics of the product is presented in the conditional decision library. It is necessary to have the process scheme combined with different working conditions and product characteristics for conditional configuration, relying on the logical configuration of the tree structure, so as to achieve autonomous decision-making and generate results. Figure 2 As shown, variable values ​​such as hole diameter and depth are extracted by identifying feature types.

[0027] The process feature template library is constructed by classifying and summarizing the features of products in the manufacturing industry, storing the processing features with manufacturability in a three-dimensional model, and realizing automatic retrieval and search functions through the feature ID. Figure 3 As shown, template classification is formulated according to the feature conditions, such as hole, surface, groove and other templates.

[0028] like Figure 4 As shown, the system variable library plays a key role in the system decision-making process for all classifications and attributes under the system architecture of the knowledge base, so the variable library has the storage of all decision factors and attribute variables.

[0029] The processing parameter library is constructed by combining different equipment resources and processing characteristics because the resources and equipment used in product processing are different. The processing parameter results are integrated into the process results during the autonomous decision-making process of the process plan, so as to calculate the optimal processing parameters using the processing parameter library. Figure 5 As shown, the machining parameters determine the corresponding specific cutting parameter values ​​by obtaining the cutting conditions of the current tool.

[0030] The manufacturing resource library is constructed in the form of a matrix, and is structured around equipment performance parameters, operating speed, processing accuracy, equipment travel, accuracy compensation method, equipment structure, etc., because the results generated after the process autonomous decision are oriented to resources such as manufacturing equipment, and the results are also affected by the performance of the equipment. The performance parameters of the equipment, operating speed, processing accuracy, equipment travel, accuracy compensation method, equipment structure, etc. can be automatically obtained through the manufacturing resource library during the process of generating the process plan, and the results are finally output based on the capabilities of the equipment resources. Figure 6 As shown, it stores the resource information used in processing and manufacturing, such as tools, tooling, and equipment.

[0031] The manufacturing standard library is used to supplement data around the process results. The manufacturing standard library is constructed with processing step descriptions, step technical requirements, step names, precision compensation methods, part clamping and alignment, etc. for standardized storage. After the process results are generated, the corresponding manufacturing standards are automatically matched according to the type of manufacturing step. Figure 7 As shown, the technical requirement description corresponding to the processing and manufacturing is stored to guide the operations at the production site.

[0032] In order to achieve the requirements of autonomous decision-making and result output of process solutions, the classification of product processing and manufacturing features, such as product geometric features, tool geometric features, and process step parameter attributes, is realized through the system variable library. Then, the conditional decision library is used to simulate the human thinking process to generate a solution framework, and the processing stations are sorted to find the optimal path to minimize the time of the processing process. The matrix of the manufacturing resource library is used to select resources (equipment, tooling, tools, etc.) with maximum processing efficiency and optimal processing quality. Finally, through this system, experience is solidified, and the system replaces manual experience in the process of product process design decision-making, effectively eliminating quality problems caused by human factors, enabling people with different skill levels to complete work of the same difficulty level, and achieving quality standardization, thereby reducing costs and increasing the market competitiveness of products.

[0033] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A reasoning process knowledge decision base system applied to the manufacturing industry, characterized by: The system includes: the system from bottom to top includes: standard layer, data layer, application layer, iteration layer; The standard layer is used to standardize manufacturing process knowledge and resource data; The data layer is used to classify and store the standardized knowledge in the standard layer and has maintainability and scalability; The application layer is used to realize the automatic generation of the results of product manufacturing process design by simulating the conditions and result decisions of human brain thinking; The iteration layer is used to achieve rapid improvement and maintenance of the system by integrating the data types that need to be maintained in the system.

2. The inference process knowledge decision base system applied to the manufacturing industry according to claim 1 is characterized by: The system also includes several sub-libraries: a condition decision library, a process feature template library, a system variable library, a processing parameter library, a manufacturing resource library, and a manufacturing standard library.

3. The inference process knowledge decision base system applied to the manufacturing industry according to claim 2 is characterized by: The conditional decision library is used to set the process plan and parameter logic matching method according to the manufacturing characteristics of the product; The conditional decision library has a process plan that combines different working conditions and product characteristics to configure conditions, and relies on the logical configuration of the tree structure to achieve autonomous decision-making and generate results.

4. The inference process knowledge decision base system applied to the manufacturing industry according to claim 2 is characterized by: The process feature template library is used to classify and summarize the features of products in the manufacturing industry, store processing features with manufacturability in the form of three-dimensional models, and realize automatic retrieval and search functions through feature IDs.

5. The inference process knowledge decision base system applied to the manufacturing industry according to claim 2 is characterized by: The system variable library is used to store all decision factors and attribute variables.

6. The reasoning process knowledge decision base system applied to the manufacturing industry according to claim 2 is characterized by: The processing parameter library is used to combine different equipment resources and processing characteristics, integrate the processing parameter results into the process results during the autonomous decision-making process of the process plan, and calculate the optimal processing parameters.

7. The inference process knowledge decision base system applied to the manufacturing industry according to claim 2 is characterized by: The manufacturing resource library is used to store equipment performance parameters, operating speed, processing accuracy, equipment travel, accuracy compensation method, and equipment structure in a structured manner, automatically obtain equipment performance parameters during the process of generating a process plan, and ultimately output results based on the capabilities of equipment resources.

8. The reasoning process knowledge decision base system applied to the manufacturing industry according to claim 2 is characterized by: The manufacturing standard library is used to store processing step descriptions, step technical requirements, step names, precision compensation methods, and part clamping and alignment in a standardized manner, and automatically matches corresponding manufacturing standards according to the type of manufacturing step after generating process results.

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