Wheel flash welding optimization system and method based on digital twinning

By constructing a digital twin wheel flash welding optimization system, integrating multiphysics modeling and AI prediction, the system achieves transparent and intelligent control of the wheel flash welding process, solving the problems of process dependence on experience and low transparency in existing technologies, and improving welding quality and efficiency.

CN122065545APending Publication Date: 2026-05-19XI AN JIAOTONG UNIV +1
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Patent Information

Application Number
CN202610186169.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-09
Publication Date
2026-05-19

AI Technical Summary

Technical Problem

Existing wheel flash welding technology relies on experience, lacks real-time simulation and prediction capabilities, cannot achieve multi-physics field coupled simulation and advanced prediction, has low process transparency, and is difficult to achieve full-chain closed-loop control and online optimization.

Method used

A digital twin-based wheel flash welding optimization system was constructed, integrating a material database, multiphysics modeling, real-time data acquisition, AI prediction, multi-objective optimization, and advanced visualization modules to achieve dynamic optimization of process parameters and transparent control of the welding process.

Benefits of technology

It achieves transparency in the welding process, transforms process parameter adjustment from experience-driven to data-driven, reduces joint performance fluctuations, improves optimization efficiency, enhances quality prediction accuracy, shortens process debugging cycles, balances energy consumption and performance, and improves production efficiency and pass rate.

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Abstract

The invention discloses a wheel flash welding optimization system and method based on digital twinning, and belongs to the crossing field of intelligent manufacturing, welding processes and artificial intelligence. The system comprises a material attribute database module, a multi-source data acquisition and preprocessing module, a digital twin module integrating heat-force-microstructure and other multi-physics field models, an AI prediction and multi-objective optimization module, an advanced visualization and interaction module and a closed-loop control module. The method comprises the following steps: initializing and matching material parameters; collecting data in real time to update the twinborn model; running simulation and AI to predict the welding temperature, the structure and the performance; if the prediction does not reach the standard, starting a multi-objective optimization algorithm to generate an optimal process parameter set; and executing optimization parameters through closed-loop control and performing real-time visual feedback. According to the method, full-chain real-time mapping, transparent monitoring and intelligent optimization of technology-structure-performance in the welding process are achieved, the performance stability of joints is remarkably improved, and the efficiency and the product quality are optimized.
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