  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.
