Quality inspection and process optimization of stamping parts based on artificial intelligence technology

The collision of the “physical world” (represented by manufacturing equipment) and the “digital world” (represented by technologies such as artificial intelligence, sensors, etc.) has spawned a huge shift in manufacturing, and the fusion of the two worlds will be the next round of economic development. Inject new kinetic energy. New technologies represented by artificial intelligence are having a huge impact on production and operation processes such as production processes, production models and supply chain systems. The application value of artificial intelligence technology in the diagnosis of the manufacturing process is gradually becoming more prominent, especially in the quality inspection and process optimization of stamping parts, which is playing an incomparable advantage of artificial intelligence.

The collision of the “physical world” (represented by manufacturing equipment) and the “digital world” (represented by technologies such as artificial intelligence, sensors, etc.) has spawned a huge shift in manufacturing, and the fusion of the two worlds will be the next round of economic development. Inject new kinetic energy. New technologies represented by artificial intelligence are having a huge impact on production and operation processes such as production processes, production models and supply chain systems. The application value of artificial intelligence technology in the diagnosis of the manufacturing process is gradually becoming more prominent, especially in the quality inspection and process optimization of stamping parts, which is playing an incomparable advantage of artificial intelligence. In short, artificial intelligence-related technologies can replace the human eye to complete the identification, measurement, positioning, judgment and other functions of stamping parts. Not only that, artificial intelligence also has the ability to “learn”, and can accurately predict through sample accumulation and model training optimization. The risk of cracking of stamping parts, so as to achieve precise control and optimization of stamping product quality. The following are the application cases of artificial intelligence technology in the stamping workshop of automobile manufacturing.

Background of the project

As a very important plastic processing method in machinery manufacturing, stamping is widely used in automotive, aerospace, electrical and other industrial fields. As we all know, most of the covering parts and structural parts of the automobile body are sheet stamping parts, and the level of stamping technology and stamping quality are very important to automobile manufacturing enterprises.

There are three stamping production lines in the stamping workshop of the production base of an automobile manufacturing enterprise, mainly producing side panels, fenders, doors, hoods and other passenger car body coverings with large contours and spatially curved shapes. In the stamping production process, some side walls are prone to local cracking during the stretching process, which requires repeated parameter adjustment and trial production; at the end of the production line, a large number of quality inspectors are required to manually detect the surface defects of stamping parts.

Issues and Challenges

1. The existing inspection method for the end of the stamping production line is manual inspection. It is necessary to quickly sort out the stamping parts with surface defects such as cracks, scratches, slip lines, and concave-convex hulls within a limited production takt time. The inspection standard Inconsistent, unstable, and difficult to quantify and store quality inspection data effectively, it is not conducive to the collection of enterprise data resources, analysis and traceability of quality problems.

2. During the trial production process of stamping production, there are many factors that affect the local cracking of the side wall during the stretching process, such as equipment parameters, mold state, sheet performance, etc. The method of adjusting parameters and repeated trial production has certain blindness and cost. large and inefficient.

3. There are many influencing factors and different data forms, and they are distributed in different business systems in the workshop. There are both real-time equipment data and unstructured image data. The requirements for data collection, management and storage are extremely high.

solution

Based on the above situation, Merrill Lynch built a big data platform for enterprises to realize the integration, storage and unified management and control of equipment, molds, materials, manufacturing process data, and quality inspection data in the stamping workshop of the factory. The intelligent detection technology based on machine vision realizes the prediction of side wall punching and cracking and the intelligent identification of surface defects of product parts.

◎According to the processing parameters of stamping equipment, sheet parameters, mold performance parameters and maintenance records, etc., through data mining machine learning algorithms, establish an intelligent prediction model of stamping process. Accurately predict the cracking risk of stamping parts through sample accumulation and model training and tuning. Finally, the correlation between the influencing factors of the manufacturing process is determined, and the combined control strategy of the production process parameters is formulated to provide support for the process optimization and quality control of the stamping manufacturing process.

◎Intelligent identification and detection of stamping parts defects based on machine vision, based on the existing conditions of the production line, design an image acquisition system, through real-time image acquisition and intelligent analysis, quickly identify whether there are surface defects in stamping parts, and automatically record all inspection images and process data. Stored in a big data platform. Through the correlation between quality inspection data, production process parameters, and product design parameters, and with the help of big data analysis technology, a closed-loop connection for the analysis and management of stamping product quality problems is formed to achieve precise control and optimization of stamping product quality.

Value

1. By predicting the cracking risk of stamping parts, the design efficiency of processing parameters of stamping parts for new models of the enterprise is improved, and the number of trial productions and trial production costs are reduced.

2. Through rapid and intelligent detection of surface defects of stamping parts, the stability and reliability of production line detection are improved, and the labor intensity and labor cost of quality inspection workers are reduced. At the same time, product quality inspection data is effectively stored, providing important data support for closed-loop quality analysis and traceability.

3. Exploring a feasible demonstration path for the promotion of intelligent manufacturing transformation of enterprises, and accumulated valuable experience for the promotion and application of industrial big data, artificial intelligence and other technologies in peer enterprises.

Applicable industries

Industries such as automobile manufacturing, aerospace, and home appliance production have stamping and spraying processes and require high product surface quality.