Multi-task deep learning-empowered digital twin for functional composite materials fabricated by laser additive remanufacturing

Abstract

The absence of effective quality prediction methods for functional composite materials (FCMs) produced by laser additive remanufacturing (LARM) hampers their application due to the complex cross-scale defects, including surface cracks and thermal damage to the internal reinforcement phase. This paper presents a multi-task deep learning-empowered digital twin for predicting visible and invisible defects in the fabricating process of FCMs. The dimensions of FCM trajectory, thermal damage to the reinforcement phase, and forming cracks were predicted via a parallel multi-task deep learning model. The dynamic visualization of the digital twin is realized through cross-sectional modeling and provides an intuitive and effective perception for monitoring the process.