Journal Article

Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel

King, Wayne E., Elwany, Alaa, Khairallah, Saad, Tapia, Gustavo, Matthews, Manyalibo

2018

Overview

Laser Powder-Bed Fusion (L-PBF) metal-based additive manufacturing (AM) is complex and not fully understood. Successful processing for one material, might not necessarily apply to a different material. This paper describes a workflow process that aims at creating a material data sheet standard that describes regimes where the process can be expected to be robust. The procedure consists of building a Gaussian process-based surrogate model of the L-PBF process that predicts melt pool depth in single-track experiments given a laser power, scan speed, and laser beam size combination. The predictions are then mapped onto a power versus scan speed diagram delimiting the conduction from the keyhole melting controlled regimes. This statistical framework is shown to be robust even for cases where experimental training data might be suboptimal in quality, if appropriate physics-based filters are applied. Additionally, it is demonstrated that a high-fidelity simulation model of L-PBF can equally be successfully used for building a surrogate model, which is beneficial since simulations are getting more efficient and are more practical to study the response of different materials, than to re-tool an AM machine for new material powder.

• Publisher: Springer London,Springer Nature B.V
• Subject: Additive manufacturing / Austenitic stainless steels / CAE) and Design / Computer simulation / Computer-Aided Engineering (CAD / Data sheets / Engineering / Gaussian process / Industrial and Production Engineering / Laser beams / Lasers / Mathematical models / Mechanical Engineering / Media Management / Melting / Original Article / Process planning / Robustness / Workflow