Machine learning approach towards laser powder bed fusion manufactured AlSi10Mg thin tubes in laser shock peening

Congratulations to HiLASIANS from the ILA and ALD departments on another successful publication! Read the full article titled Machine learning approach towards laser powder bed fusion manufactured AlSi10Mg thin tubes in laser shock peening in the Surface Engineering journal.

The study was authored by Ondřej Stránský, Ivan Tarant, Sunil Pathak, Jan Brajer, Tomáš Mocek and Ondřej Denk, in collaboration with Libor Beránek and František Holešovský from the Czech Technical University in Prague.

The industry's demand for intricate geometries has spurred research into additive manufacturing (AM). Customising material properties, including surface roughness, integrity and porosity reduction, are the key industrial goals. This necessitates a holistic approach integrating AM, laser shock peening (LSP) and non-planar geometry considerations. In this study, machine learning and neural networks offer a novel way to create intricate, abstract models capable of discerning complex process relationships. Our focus is on leveraging the certain range of laser parameters (energy, spot area, overlap) to identify optimal residual stress, average surface roughness, and porosity values. Confirmatory experiments demonstrate close agreement, with an 8% discrepancy between modelled and actual residual stress values. This approach's viability is evident even with limited datasets, provided proper precautions are taken.

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