|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||A Physics-based Correlation Study of Hot Cracking Phenomenon in the Processes of Additive Manufacturing
||Guannan Tang, Anthony Rollett
|On-Site Speaker (Planned)
The occurrence of hot cracking in the additive manufacturing process involves a variety of factors from different aspects. This leaves the effort trying to quantify hot cracking phenomena often lacking generality. Thus, unifying models that take account of processing parameters, thermodynamics, and mechanical properties remain a big gap in modeling the hot cracking phenomenon. Our current study intends to evaluate variables in different aspects but related to the hot cracking phenomenon. The topmost relevant variables will be identified and correlated with the occurrence of hot cracking through machine learning algorithms. To this end, synchrotron-based high-speed techniques together with a melt pool simulation model will be used to generate the training data. The end goal is to build up a unifying model that can predict hot cracking susceptibility based on information at different scales and aspects.