The poor surface roughness associated with additively manufactured parts can influence the surface integrity and geometric tolerances of produced components. In response to this issue, laser polishing (LP) has emerged as a potential technique for improving the surface finish and producing parts with enhanced properties. Many studies have been conducted to investigate the effect of LP on parts produced using additive manufacturing. The results showed that applying such a unique treatment can significantly enhance the overall performance of the part. In LP processes, the surface of the part is re-melted by the laser, resulting in smaller peaks and shallower valleys, which enable the development of smoother surfaces with the help of gravity and surface tension. Precise selection of laser parameters is essential to achieve optimal enhancement in the surface finish, microstructure, and mechanical properties of the treated parts. This paper aims to compile state-of-the-art knowledge in LP of additively manufactured metals and presents the optimal process parameters experimentally and modeling using artificial machine learning. The effects of laser power, the number of laser re-melting passes, and scanning speed on the final surface roughness and mechanical properties are comprehensively discussed in this work.

Fe-Mn-Al-Ni is an Fe-based shape memory alloy (SMA) featuring higher stability and low temperature dependency of superelasticity stress over a wide range of temperatures. Additive manufacturing (AM) is a promising technique for fabricating Fe-SMA with enhanced properties, which can eliminate the limitations associated with conventional fabrication and allow for the manufacture of complicated shapes with only a single-step fabrication. The current work investigates the densification behavior and fabrication window of an Fe-Mn-Al-Ni SMA using laser powder bed fusion (LPBF). Experimental optimization was performed to identify the optimum processing window parameters in terms of laser power and scanning speed to fabricate Fe-Mn-Al-Ni SMA samples. Laser remelting was also employed to improve the characteristics of Fe-Mn-Al-Ni-fabricated samples. Characterization and testing techniques were carried out to assess the densification behavior of Fe-Mn-Al-Ni to study surface roughness, density, porosity, and hardness. The findings indicated that using a laser power range of 175–200 W combined with a scanning speed of 800 mm/s within the defined processing window parameters can minimize the defects with the material and lead to decreased surface roughness, lower porosity, and higher densification.

Due to the layering nature of additive manufacturing, additively manufactured parts exhibit a unique microstructure and are more susceptible to defects. Post-processing heat treatments of additively manufactured parts have shown great promise in improving their quality and reliability. However, the previous studies presented here demonstrated that additively manufactured parts respond to heat treatments differently compared to their traditional counterparts. This demonstrates a need for models that can predict the influence of different heat treatments on the mechanical behavior of additively manufactured parts. A hybrid approach between data-driven and physically informed models was adopted to model the influence of post-processing heat treatments on the strengthening mechanisms of additively manufactured Inconel 718. This work focuses on Inconel 718 for its common use in additive manufacturing and because it is one of the most studied additively manufactured alloys which resulted in producing more data that can be used to model its behavior.

The sustainability of sheet metal parts often has multiple facets depending on the phase under consideration. The work presented in this paper focuses on cradle-to-gate Life Cycle Analysis (LCA) of the Origami-based Sheet Metal (OSM) folding process. OSM is an emerging fabrication technique that utilizes the principle of folding sheet metal parts by creating Material Discontinuities (MD) along the bend line. MD enables sheet metal folding (i.e., bending) with minimal force requirements and machinery. The anticipated reduction in force and machinery will result in a reduction in the required manufacturing energy. In addition, the OSM has less dependency on dies and shape-dedicated equipment. Hence, the cost associated with sheet metal parts development is reduced. This study attempts to establish the environmental impacts of the OSM for sheet metal parts by utilizing cradle-to-gate life cycle analysis. Environmental impacts of OSM are highlighted by comparing the OSM with the conventional stamping process. In the LCA, consumed energy and emissions are considered environmental impact indicators. Energy and emissions data are collected from published literature, machinery manuals, and available empirical models for energy consumption. A case study of a vehicle floor panel is presented as an example. Finite element analysis (FEA) is employed to achieve a more accurate energy estimation since the LCA inventory data displays a significant discrepancy. The findings of this study reveal that OSM requires less energy and produces fewer emissions than the stamping process.