Development of machines and process for laser additive manufacturing

Abstract

Laser Directed Energy Deposition (L-DED) has emerged as a powerful additive manufacturing technique for high-performance alloys, owing to its ability to fabricate complex, near-net-shape components. However, achieving consistent geometric accuracy remains a major challenge, especially for multi-layer thin-walled structures of high-performance alloys like In718. This thesis presents a comprehensive, experimentally validated, data-driven framework for predicting and optimising geometric features—track height, width, and depth—in L-DED. A systematic experimental study was conducted using a structured design approach to understand how laser power, scan speed, and powder feed rate affect the geometry of L-DED deposits. By analysing the resulting data, clear trends emerged: laser power and powder feed rate were found to be the dominant factors influencing Track width and height, while scan speed played a more subtle role in melt pool behaviour and stability. These trends were used to develop predictive relationships that guided process optimisation. The optimised parameters enabled significant improvements in material efficiency, energy consumption, and build accuracy.