Optimization of the Effect of Laser Power Bed Fusion 3D Printing during the Milling Process Using Hybrid Artificial Neural Networks with Particle Swarm Optimization and Genetic Algorithms

Abstract

Additive manufacturing (AM) is gaining popularity as it can produce near-net geometries and work with difficult-to-manufacture materials, such as stainless steel 316L. However, due to the low surface quality of AM parts, machining and other finishing methods are required. Laser powder bed fusion (LPBF) components can be difficult to finish as the surface roughness (Sa) can vary greatly depending on the part’s orientation, even when using the same machining parameters. This paper explored the effects of finishing (milling) SS 316L LPBF components in a variety of part orientations. The effect of layer thickness (LT) variation in LPBF-made components was also studied. LPBF parts of 30, 60, 80, and 100 μm layer thicknesses were created to analyze the effect of the LT on the final milling process. Additionally, the effect of cutting speed during the milling process on the surface roughness of the SS 316L LPBF component was investigated, along with the orientations and layer thicknesses of the LPBF components. The results revealed that the machined surface undergoes significant orientation and layer thickness changes. The investigations employed a factorial design, and analysis of variance (ANOVA) was used to analyze the results. In addition, an artificial neural network (ANN) model was combined with particle swarm optimization (denoted as ANN-PSO) and the genetic algorithm (denoted as ANN-GA) to determine the optimal process conditions for machining an SS 316L LPBF part. When milled along (Direction B) an orientation with a cutting speed of 80 m/min, the LPBF component produced, with a layer thickness of 60 μm, achieves the lowest surface roughness. For instance, the Sa of a milled LPBF part can be as low as 0.133 μm, compared to 7.54 μm for an as-fabricated LPBF part. The optimal surface roughness was 0.155 μm for ANN-GA and 0.137 μm for ANN-PSO, whereas the minimal surface roughness was experimentally determined to be 0.133 μm. Therefore, the surface quality of both hybrid algorithms has improved, making them more efficient.