Hybrid optimisation studies on the microstructural properties and wear resistance of maraging steel 1.2709 parts produced by laser powder bed fusion

Abstract

Improper selection of laser powder bed fusion (LPBF) process parameters tends to result in poor quality parts which imposes limitations with respect to the mechanical performance due to process induced defects. To address this LPBF processing challenge, this study employs a hybrid optimisation technique which combines artificial neural network (ANN) and response surface methodology (RSM) models. The models were employed for predicting the microstructural properties (porosity, microhardness and amount of martensite phase composition) and mechanical characteristic (wear resistance) of LPBF manufactured maraging steel 1.2709 parts as a function of a combination of process parameters (scan speed, laser power and hatch spacing). Both ANN and RSM models had a high tracking ability. However, ANN showed better prediction accuracy than RSM. The most desirable optimal LPBF processing parameters for minimum wear volume and porosity while maintaining maximum microhardness and martensite phase composition were found at volumetric energy density (VED) of 77 J/mm3 (laser power = 165 W, scan speed = 784 mm/s and hatch spacing = 91 μm). Optimal quality properties predicted by the RSM and ANN models were consistent with confirmatory experiment results.