Integrated die-casting intelligent control optimization method, device and equipment and storage medium

By combining real-time data acquisition and edge computing with digital twin technology and machine learning, the problems of insufficient data utilization and lagging quality control in traditional integrated die-casting plants have been solved, realizing real-time optimization and efficient production of the die-casting process.

CN121776444APending Publication Date: 2026-04-03DONGFENG ELECTRONICS TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-22
Publication Date
2026-04-03

AI Technical Summary

Technical Problem

Traditional integrated die-casting plants suffer from problems such as insufficient data utilization, lagging quality control, delayed system response, and reliance on experience for process optimization, resulting in low transparency in the production process, waste of resources, and poor production efficiency.

Method used

By employing real-time data acquisition, edge computing, digital twin technology, and machine learning, and through multi-source data fusion and quality prediction models, the die-casting process can be controlled in real time and optimized for process parameters. Reinforcement learning is then used for adaptive adjustment.

Benefits of technology

It achieves millisecond-level real-time control, reduces scrap rate and resource waste, improves production efficiency and data processing efficiency, and reduces reliance on highly skilled workers.

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Abstract

The invention discloses an integrated die-casting intelligent control optimization method, device, equipment and medium, and relates to the technical field of industrial intelligent manufacturing, the method comprises the following steps: collecting die-casting work related data in real time, and identifying a starting point and an ending point of an injection cycle based on a pressure change threshold to obtain the injection cycle; key characteristic parameters of each injection period are extracted based on the collected die-casting work related data, and the key characteristic parameters are input into a pre-constructed quality prediction model to predict a product quality value of the current injection period; and comparing the product quality value obtained by prediction with a preset threshold value to judge whether to perform process parameter optimization or not. Control optimization of integrated die casting can be effectively achieved, and the real-time control requirement of the die casting process is met.
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