Predictive modeling approaches in laser-based material processing

Abstract

Predictive modeling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new materials, processes, and structures. In the current study, previously unexplored predictive modeling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser processing on material structures. The focus is centered on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser processing on a range of materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser's input variables and the observed material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser-material interaction. As a consequence, we augmented the adjusted simulated data to the experiment and substantially improved the predictive performance due to the availability of an increased number of sampling points. In parallel, an information-theoretic metric, which identifies and quantifies the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology toward reducing material design, testing, and production cost via the replacement of expensive trial-and-error based manufacturing procedures with a precise pre-fabrication predictive tool.