Exploring crystallographic texture manipulation in stainless steels via laser powder bed fusion: insights from neutron diffraction and machine learning

Abstract

Laser powder bed fusion of metals (PBF-LB/M) is a pivotal additive manufacturing technique that enables the production of intricate components. In addition to enabling the production of complex shapes, it allows for a high degree of freedom in manipulating the microstructure. The present investigation explores the manipulation of the crystallographic texture in AISI 304L stainless steels via PBF-LB/M, due to the possibility of tailoring the secondary hardening phenomena. Neutron diffraction provides efficient texture assessment, while decision tree regression reveals the complex interplay between processing parameters and the resulting crystallographic textures. Our investigation identifies the optimal PBF-LB/M processing parameters for obtaining strong texture along the build and laser scan directions. Additionally, microstructural characterisation of selected samples reveals the complex solidification structures. By employing advanced characterisation techniques and machine learning, this work provides insights into achieving or avoiding specific crystallographic textures during PBF-LB/M processing of stainless steels or other materials.

Keywords

• PBF-LB/M
• crystallographic texture
• neutron diffraction
• machine learning
• process optimisation