A review on scan strategies in laser-based metal additive manufacturing

Abstract

Metal additive manufacturing (MAM) is revolutionizing the production of metallic parts, offering significant application potential across various industries, especially in harsh environments where high material utilization efficiency is crucial. However, the extreme thermal and mechanical coupling in MAM results in a wide variation in part properties due to high energy density input and heterogeneous temperature distribution. This leads to high-temperature gradients, thermal stresses, microstructural variations, and defects that affect the serviceability of printed metallic parts. Controlling these attributes remains challenging due to the complexity of the process. In laser-based MAM processes, particularly in laser powder bed fusion (PBF-LB), the scan strategy, which includes laser parameters, scan speed, laser power, hatch spacing, layer thickness, and scan pattern, plays a critical role in defining thermal input and temperature gradients. These factors influence the microstructure, residual stress, and mechanical properties of additively manufactured parts. The rapid development of scan strategies is key to improving product efficiency and quality. This paper provides a comprehensive review of scan strategies and their effect on various attributes, with a primary focus on PBF-LB. It also discusses recent advancements in MAM, including multi-laser systems, inline parameter control, machine learning, and in-situ monitoring, highlighting their roles in producing high-quality products and driving the mainstream adoption of metal additive manufacturing.

Keywords

• Metal additive manufacturing
• Scan strategies
• Microstructure
• Residual stress
• Defects