Predictive modeling approaches in laser-based material processing

TL;DR

This paper explores the use of statistical and machine learning models to predict the effects of laser processing on materials, aiming to enhance manufacturing efficiency and reduce costs.

Contribution

It introduces a novel integration of experimental and simulation data to improve predictive accuracy in laser-based material processing.

Findings

Predictive models accurately map laser inputs to material structures.

Augmenting experimental data with simulations enhances model performance.

High uncertainty regions identified around transition boundaries.

Abstract

Predictive modelling 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 modelling 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 centred 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 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 experimental and substantially improved the predictive performance, due to the availability of increased number of sampling points. In parallel, a metric to identify and quantify 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 towards reducing material design, testing and production cost via the replacement of expensive trial-and-error based manufacturing procedure with a precise pre-fabrication predictive tool.