In order to thoroughly implement the decision-making and deployment of the Party Central Committee and the State Council, the Municipal Party Committee and the Municipal Government on building a manufacturing power, a quality power, and promoting high-quality development, promote the implementation of excellent quality projects in the manufacturing industry, and realize high-quality products, enterprise modernization, and high-end industries, the Municipal Economic and Information Commission continues to organize and carry out "quality benchmarking" activities throughout the city. We will introduce and share the advanced cases of "quality benchmarking" in Shanghai in 2023-2024, promote the promotion of advanced quality management methods and practices, promote enterprises to establish advanced quality management systems, improve the quality and efficiency and core competitiveness of the manufacturing industry, and accelerate the process of new industrialization.
As the largest manufacturer of automotive transmission systems in China, Shanghai GKF Transmission System Co., Ltd. is deeply engaged in the research and development of transmission system products, and has the largest automotive transmission system product research and development and test base in Asia. Focusing on SAIC's "New Four Modernizations" strategy, the company practices the "Quality First" code of conduct, continues to carry out quality improvement work, and focuses on the three core application scenarios of equipment predictive maintenance, accurate traceability of product parameters and factory management cockpit, driven by digital innovation, to comprehensively improve the management level of production and manufacturing process.
Digital Twin Workshop Application Scenario 1:
Predictive maintenance of equipment
By clarifying the scope of key equipment that affects the process of manufacturing, collecting key data to characterize the status of key equipment, making equipment predictive maintenance recommendations through machine learning and artificial intelligence algorithms, and manually reviewing them, at the same time, in the process, continuously optimizing data quality and algorithm models, improving the accuracy of models, and providing a strong guarantee for the stable operation of equipment.
Equipment predictive maintenance display interface & predictive model
Digital Twin Workshop Application Scenario 2:
Precise and traceable product parameters
Based on the processing process models of different products, an OPC acquisition system was constructed to collect various parameters in the processing process in real time. Through RFID technology, the unique binding and real-time recording of each process data are realized. Once a non-conforming process is found, the system will immediately trigger an alarm and display the abnormal process information. At the same time, the background conducts in-depth analysis and comparison of the processing parameters of good and bad products, establishes a mathematical model of processing parameters and product quality, predicts the quality of subsequent batches of products, and provides scientific guidance for product quality improvement.
Traceability tree diagram of product processing parameter information
Digital Twin Workshop Application Scenario 3:
The factory manages the cockpit
By integrating the data collected by multiple subsystems into the plant management cockpit, a multi-level plant operation data display architecture is formed. The head of the factory department, the head of the department and the on-site management personnel can grasp the production status of the factory in real time, respond quickly to quality problems and give guidance. This initiative breaks down information silos and effectively improves the efficiency of problem solving and the accuracy of decision-making.
Multi-level plant operation data display architecture
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