A closed-loop implementation method for digital transformation of thermal processing discrete manufacturing processes

Abstract

The present invention relates to a closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process. The method divides process data, process execution data, and process quality data that affect production quality in a thermal processing process into differential subset databases based on key process parameters, and determines the subset databases into three categories: "key process parameter subset", "key process execution compliance rate subset", and "key process evaluation deviation rate subset". A digital transformation plan is built with the three subsets as the core, and data precipitation is performed based on the subset division. Finally, full life cycle management and control are performed based on the product to achieve digital transformation of discrete manufacturing processes, and nonlinear quality management in the field of thermal processing discrete manufacturing can be achieved. Database management based on the three types of data subsets can be used for data use without the need for demolition and reconstruction.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal processing digitalization, and in particular to a closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process. Background Art

[0002] As a discrete manufacturing industry, the equipment manufacturing industry is a key direction of intelligent manufacturing. Its products are discrete and highly customized. Due to the diversified market demand, the complexity of the manufacturing process, and the highly discrete production model, enterprises are facing pain points and difficulties in resource allocation, production model, R&D innovation, and service transformation.

[0003] The typical characteristics of discrete extreme manufacturing industries are: 1. Discrete and highly customized products make it difficult to achieve continuous and large-scale production; 2. R&D and design involve multiple disciplines such as metallurgy, casting, forging, and heat, making it difficult to achieve coordination; 3. The business process is complex, and there are breakpoints and bottlenecks in the data chain, making it impossible to achieve efficient coordination of process, production, quality and other businesses and effective sharing of data.

[0004] Most of the existing discrete digital management systems are centered on software. They build management systems through the integration and construction of multiple systems to achieve full life cycle data management and process management, but lack effective management solutions at the data level.

[0005] In order to promote the intelligent upgrade and high-quality development of the manufacturing industry, create new momentum for the intelligent manufacturing industry, explore and build a new model of intelligent manufacturing for discrete equipment, form replicable and popularizable core technology products and overall solutions, output and promote them to vertical industries, and enable the digital transformation of customers and industries, the present invention proposes a new solution for the digital transformation of thermal processing discrete manufacturing processes. Summary of the invention

[0006] In view of the defects of the prior art, the object of the present invention is to provide a closed-loop implementation method for digital transformation of thermal processing discrete manufacturing processes.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] A closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process comprises the following steps: Step 1: Divide the process data, process execution data, and process quality data that affect production quality in the discrete manufacturing process of hot working into a differential subset database, and determine the subset database into three categories: "key process parameter subset", "key process execution compliance rate subset", and "key process evaluation deviation rate subset"; Step 2: The digital transformation plan is built with three subsets as the core, and data is accumulated according to the subset division to form "key process parameter subset", "key process execution compliance rate subset" and "key process evaluation deviation rate subset"; Step 3: Based on the product, conduct full life cycle management and control to realize the digital transformation of discrete extreme manufacturing processes.

[0009] Furthermore, step three is as follows: based on the "key process parameter subset", the enterprise's production equipment is digitally transformed to collect as much real-time production data as possible, and compared with the process standard data in real time. Through the "key process execution compliance rate subset", real-time management and correction of production process non-conformities are carried out to reduce quality problems caused by inadequate process execution. After production is completed, production quality data is collected, and the deviation between process and quality is evaluated by relying on the "key process evaluation deviation rate subset", the rationality of the process is evaluated, and the data is deposited in the underlying database to provide data support for the next process formulation.

[0010] Furthermore, the key process parameter subset needs to determine the key process parameters that affect production quality for specific product types, and clarify the number of key process parameters and parameter value setting issues.

[0011] Furthermore, the process parameters are bottom-level root data, not post-processing data, and the values ​​of key process parameters are determined in the process design stage.

[0012] Furthermore, for the key process execution compliance rate subset, it is necessary to collect as much data as possible, collect various types of data and conduct online process monitoring, and rely on products and equipment to control the deviation between key process parameter execution data and process data, eliminate the impact of operational deviations on process execution, and achieve real-time measurement, control and correction.

[0013] Furthermore, when controlling the deviation between the key process parameter execution data and the process data, the process online monitoring will compare the production execution data with the key process parameters in real time, monitor the degree of deviation of the process execution data in real time according to the preset range of process requirements, and promptly remind production operators to correct non-conformities to reduce deviations in the production process.

[0014] Furthermore, as the equipment tasks are executed during the production process, the compliance rate of key process parameters of the production process is statistically analyzed, the process execution status of the equipment in operation is fed back, and the efficiency of data collection and transmission as well as the validity of the data are evaluated.

[0015] Furthermore, the key process evaluation deviation rate subset, targeting the typical characteristics of discrete manufacturing, characterizes the rationality of the process design through two-layer quality control to achieve a comprehensive evaluation of global judgment and local correction. The two layers of quality control are product final quality deviation feedback and product production process deviation feedback.

[0016] The advantages and positive effects of the present invention are:

[0017] 1. The present invention focuses on key process parameters, conducts data statistics and full life cycle management of process parameters, process parameter production execution, and process parameter quality evaluation, realizes the digital transformation of thermal processing discrete manufacturing processes, and achieves a nonlinear quality management model in the field of thermal processing discrete manufacturing.

[0018] 2. The method provided by the present invention can be used to digitally transform the existing systems of various enterprises, and the data can be used by managing the database according to the three types of data subsets without the need for demolition and reconstruction.

[0019] 3. The digital transformation solution of the present invention is based on data. Since the data itself originates in the enterprise, compared with the traditional software-centric management method, the digital transformation provided by the present invention is closer to the actual application scenarios of the manufacturing industry, has higher utilization value, and is easier for process personnel to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the closed-loop implementation method architecture of the present invention. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] like Figure 1 As shown, in order to solve the typical problems of the thermal processing discrete manufacturing industry, the present invention proposes a closed-loop implementation method for digital transformation of thermal processing discrete manufacturing process, including the following steps:

[0023] Step 1: Divide the process data, process execution data, and process quality data that affect production quality into differential subset databases, and determine the subset database into three categories: "key process parameter subset", "key process execution compliance rate subset", and "key process evaluation deviation rate subset".

[0024] Step 2: The digital transformation plan is built with three subsets as the core. Various systems (systems used by each enterprise) are divided into subsets to accumulate data, forming "key process parameter subset", "key process execution compliance rate subset" and "key process evaluation deviation rate subset".

[0025] Step 3: Finally, based on the product, conduct full life cycle management and control to achieve digital transformation of discrete extreme manufacturing processes. Specifically:

[0026] Based on the "key process parameter subset", the company's production equipment is digitally transformed to ensure that real-time production data can be collected as much as possible and compared with process standard data in real time. Through the "key process execution compliance rate subset", real-time management and correction of production process non-conformities can be carried out to reduce quality problems caused by inadequate process execution. After production is completed, production quality data is collected, and the deviation between process and quality is evaluated based on the "key process evaluation deviation rate subset", the rationality of the process is evaluated, and the data is deposited in the underlying database to provide data support for the next process formulation.

[0027] Among them, the key process parameter subset needs to determine the key process parameters that affect production quality for specific product types. This parameter is the underlying root data, not the post-processing data. The key process parameter subset needs to clarify the number of key process parameters and the parameter value setting issues.

[0028] Key process execution compliance rate subset: collect as much data as possible to establish a sound data base, and realize the collection of traditionally difficult-to-collect data through technical transformation. Rely on products and equipment to control the deviation between key process parameter execution data and process data, eliminate the impact of operational deviations on process execution, and achieve real-time measurement, control and correction.

[0029] Key process evaluation deviation rate subset: Through two levels of quality control, namely product final quality deviation feedback (category one) and product production process deviation feedback (category two), the rationality of process design is characterized for the typical characteristics of discrete extreme manufacturing, and a comprehensive evaluation of global judgment and local correction is achieved.

[0030] Product final quality deviation feedback (Type I): Type I quality deviation number is result-based quality feedback, such as UT ultrasonic flaw detection, MT magnetic particle flaw detection, etc.

[0031] Product production process deviation feedback (category 2): Category 2 quality deviation numbers are process-based quality feedback, such as alloying in the smelting process, repair and cleaning in the casting process, etc.

[0032] The present invention is described in detail below in conjunction with a specific embodiment of the production of gear rings.

[0033] Category 1 quality deviation: Based on the statistical analysis of historical production data of gear rings, it is determined that the category 1 quality deviations in the manufacturing process of gear rings mainly include: out-of-tolerance size, loose shrinkage holes, black skin and lack of meat, pores and sand holes, burrs and pits, cracks, slag inclusions, unqualified hardness, unqualified tensile properties, and unqualified impact properties, a total of 10 categories.

[0034] Category II quality deviation: Based on the statistical analysis of historical data on gear ring production, it is determined that there are four categories of Category II quality deviations that occur in the gear ring production process: smelting alloying rework, cast steel blank rework and repair, post-casting heat treatment rework, and performance heat treatment rework.

[0035] Key process parameters: By analyzing the quality deviation types and distribution of similar products, and referring to the key points of the gear ring production process in the standard, there are 33 key process parameters for the entire gear ring production process.

[0036] Key process parameters: Details are shown in the table. The values ​​of key process parameters will be determined during the process design stage.

[0037] Table 1 Key processes and key process parameters

[0038] Compliance rate of key process parameters: Through the system data collection function and measurement and control system, various types of data are collected and online process monitoring is performed. Online process monitoring compares production execution data with key process parameters in real time. According to the preset interval of process requirements, the deviation degree of process execution is monitored in real time, and production operators are reminded to correct non-conforming items in time to reduce production process deviation. The compliance rate of key process parameters in the production process is statistically analyzed according to the execution of equipment tasks during the production process, and the process execution of the running equipment is fed back, and the efficiency of data collection and transmission and the validity of data are evaluated.

[0039] Key process parameter evaluation deviation rate: The system counts the first-class quality deviation and second-class quality deviation data of the entire process of ring gear product manufacturing, and outputs the key process parameter evaluation deviation rate.

[0040] After production is completed, the system accumulates data based on subset division to form three types of databases: "key process parameter subset number", "key process execution compliance rate subset", and "key process evaluation deviation rate subset".

[0041] The present invention focuses on key process parameters, and through statistics on process design, process execution compliance rate and process evaluation deviation rate of the production process, the final data is precipitated to form three types of subset databases, and the digital transformation of discrete manufacturing processes is realized through database management.

[0042] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process, characterized in that: The following steps are involved: Step 1: Divide the process data, process execution data, and process quality data that affect production quality in the hot processing discrete manufacturing process into different subset databases, and determine the subset database into three categories: "key process parameter subset", "key process execution compliance rate subset", and "key process evaluation deviation rate subset"; Step 2: The digital transformation plan is built around three subsets, and data is precipitated based on the subset division to form "key process parameter subset", "key process execution compliance rate subset", and "key process evaluation deviation rate subset"; Step 3: Based on the product, conduct full life cycle management and control to realize the digital transformation of discrete extreme manufacturing processes.

2. A closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process according to claim 1, characterized in that: Step three is as follows: Based on the "key process parameter subset", the company's production equipment is digitally transformed to ensure that real-time production data can be collected as much as possible and compared with process standard data in real time. Through the "key process execution compliance rate subset", real-time management and correction of production process non-conformities can be carried out to reduce quality problems caused by inadequate process execution. After production is completed, production quality data is collected, and the deviation between process and quality is evaluated based on the "key process evaluation deviation rate subset", the rationality of the process is evaluated, and the data is deposited in the underlying database to provide data support for the next process formulation.

3. The closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process according to claim 1 is characterized in that: The key process parameter subset needs to determine the key process parameters that affect production quality for specific product types, and clarify the number of key process parameters and parameter value setting issues.

4. A closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process according to claim 3, characterized in that: The process parameters are bottom-level root data, not post-processing data, and the values ​​of key process parameters are determined in the process design stage.

5. The closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process according to claim 1 is characterized in that: For the key process execution compliance rate subset, it is necessary to collect as much data as possible, collect various types of data and conduct online process monitoring, and rely on products and equipment to control the deviation between key process parameter execution data and process data, eliminate the impact of operational deviations on process execution, and achieve real-time measurement, control and correction.

6. A closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process according to claim 5, characterized in that: When controlling the deviation between key process parameter execution data and process data, the process online monitoring compares the production execution data with the key process parameters in real time. According to the preset range of process requirements, it monitors the degree of deviation of the process execution data in real time, and promptly reminds production operators to correct non-conformities to reduce deviations in the production process.

7. A closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process according to claim 5, characterized in that: During the production process, as the equipment performs its tasks, the compliance rate of key process parameters of the production process is collected and statistically analyzed to provide feedback on the process execution of the equipment in operation, and to evaluate the efficiency of data collection and transmission as well as the validity of the data.

8. The closed-loop implementation method for digital transformation of a thermal processing discrete manufacturing process according to claim 1 is characterized in that: The key process evaluation deviation rate subset, targeting the typical characteristics of discrete manufacturing, characterizes the rationality of process design through two-layer quality control to achieve a comprehensive evaluation of global judgment and local correction. The two layers of quality control are product final quality deviation feedback and product production process deviation feedback.

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