Machine learning-driven process of alumina ceramics laser machining

Abstract Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light matter, simulation methods are extremely crucial, as they help enhance the quality by offering comprehension of inter-relationships laser processing parameters. On other hand, experimental parameter optimization recommends systematic, consequently time-consuming, investigation available space. An intelligent strategy employ machine learning (ML) techniques capture relationship picosecond parameters for finding proper combinations create desired cuts on industrial-grade alumina ceramic with deep, smooth defect-free patterns. such beam amplitude frequency, scanner passing speed number passes over surface, well vertical distance from sample used predicting depth, top width, bottom width engraved channels using ML models. Owing complex correlation parameters, it shown Neural Networks (NN) most efficient in outputs. Equipped an model captures interconnection channel dimensions, one can predict required input achieve target geometry. This significantly reduces cost effort during development phase without compromising accuracy or performance. The developed be applied wide range processes.