Smart Design and Manufacturing the Welded Q350 Steel Frames via Lifecycle Management Strategy of Digital Twin

Abstract

Artificial intelligent aided design and manufacturing have been recognized as one kind of robust data-driven and data-intensive technologies in the integrated computational material engineering (ICME) era. Motivated by the dramatical developments of the services of China Railway High-speed series for more than a decade, it is essential to reveal the foundations of lifecycle management of those trains under environmental conditions. Here, the smart design and manufacturing of welded Q350 steel frames of CR200J series are introduced, presenting the capability and opportunity of ICME in weight reduction and lifecycle management at a cost-effective approach. In order to address the required fatigue life time enduring more than 9×10⁶ km, the response of optimized frames to the static and the dynamic loads are comprehensively investigated. It is highlighted that the maximum residual stress of the optimized welded frame is reduced to 69 MPa from 477 MPa of previous existing one. Based on the measured stress and acceleration from the railways, the fatigue life of modified frame under various loading modes could fulfil the requirements of the lifecycle management. Moreover, our recent developed intelligent quality control strategy of welding process mediated by machine learning is also introduced, envisioning its application in the intelligent welding.

Keywords

fatigue / intelligent manufacturing / integrated computational materials engineering (ICME) / digital twin / machine learning