Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing.

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML-related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM-related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM-related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM-related research areas.

Binder Jetting Additive Manufacturing of High Porosity 316L Stainless Steel Metal Foams.

High porosity (40% to 60%) 316L stainless steel containing well-interconnected open-cell porous structures with pore openness index of 0.87 to 1 were successfully fabricated by binder jetting and subsequent sintering processes coupled with a powder space holder technique. Mono-sized (30 µm) and 30% (by volume) spherically shaped poly(methyl methacrylate) (PMMA) powder was used as the space holder material. The effects of processing conditions such as: (1) binder saturation rates (55%, 100% and 150%), and (2) isothermal sintering temperatures (1000 ○C to 1200 ○C) on the porosity of 316L stainless steel parts were studied. By varying the processing conditions, porosity of 40% to 45% were achieved. To further increase the porosity values of 316L stainless steel parts, 30 vol. % (or 6 wt. %) of PMMA space holder particles were added to the 3D printing feedstock and porosity values of 57% to 61% were achieved. Mercury porosimetry results indicated pore sizes less than 40 µm for all the binder jetting processed 316L stainless steel parts. Anisotropy in linear shrinkage after the sintering process was observed for the SS316L parts with the largest linear shrinkage in the Z direction. The Young's modulus and compression properties of 316L stainless steel parts decreased with increasing porosity and low Young's modulus values in the range of 2 GPa to 29 GPa were able to be achieved. The parts fabricated by using pure 316L stainless steel feedstock sintered at 1200 ○C with porosity of ~40% exhibited the maximum overall compressive properties with 0.2% compressive yield strength of 52.7 MPa, ultimate compressive strength of 520 MPa, fracture strain of 36.4%, and energy absorption of 116.7 MJ/m3, respectively. The Young's modulus and compression properties of the binder jetting processed 316L stainless steel parts were found to be on par with that of the conventionally processed porous 316L stainless steel parts and even surpassed those having similar porosities, and matched to that of the cancellous bone types.