Optimization potentials of laser powder bed fusion: A conceptual approach

Abstract

Additive manufacturing (AM), more specifically laser powder bed fusion (LPBF), has become increasingly important for the production of complex components. Despite recent improvements, issues with process parameter optimization, multi-material approaches, CAx chain, adaption for automated mass production, automated process planning, and quality control are still major concerns. So far, despite growing interest, the technology has not yet made the leap into everyday and large-scale use. The use of artificial intelligence offers opportunities to solve many of these problems and improve LPBF technology. In this paper, these topics are addressed to give the reader a holistic overview of the potential for optimization. The individual topics are not only explained and supported with example products from various industries but also evaluated in terms of cost-effectiveness and quality improvement. By evaluating the potentials, restrictions, and recommendations, a framework is created for further investigation and practical application of optimization approaches.

Keywords

• additive manufacturing
• laser powder bed fusion
• artificial intelligence
• automatization
• process optimization
• cax process chain
• cad/cam
• caq