Nowadays, the application of novel soft-computing methods to new industrial processes is often limited by the actual capacity of the industry to assimilate state-of-the-art computational methods. The selection of optimal parameters for efficient operation is very challenging in microscale manufacturing processes, because of intrinsic nonlinear behavior and reduced dimensions. In this paper, a decision-making system for selecting optimal parameters in micromilling operations is designed and implemented using simple and efficient soft-computing techniques. The procedure primarily consists of four steps: an experimental characterization; the modeling of cutting force and surface roughness by means of a multilayer perceptron; multiobjective optimization using the cross-entropy method, taking into account productivity and surface quality; and a decision-making procedure for selecting the most appropriate parameters using a fuzzy inference system. Finally, two different alloys for micromilling processes are considered, in order to evaluate the proposed system: a titanium-based alloy and a tungsten-copper alloy. The experimental study demonstrated the effectiveness of the proposed solution for automated decision-making, based on simple soft-computing methods, and its successful application to a real-life industrial challenge.

Laser-based powder bed fusion (L-PBF) technology stands out for its ability to create complex, high-performance parts, optimizing design freedom and material efficiency. Despite technical and financial challenges, it is attractive to industries where performance, weight reduction, and customization are critical. In L-PBF, relative density (RD) is a key factor that directly impacts the mechanical properties and overall quality of printed parts. However, predicting RD is a complex and costly task due to the numerous factors involved. This study addresses this need by creating a large-scale dataset for RD prediction in L-PBF, consisting of 1579 samples of commercial alloys from the literature. It includes printing conditions and other crucial inputs like protective atmosphere, powder size distribution, and part geometry. This dataset offers a valuable resource for researchers to benchmark their results, better understand key factors influencing RD, and validate models or explore new machine-learning approaches tailored to L-PBF.

The potential of laser-based powder bed fusion (L-PBF) technology for producing functional components relies on its capability of maintaining or even improving the mechanical properties of the processed material. This improvement is associated with the microstructure resulting from the high thermal gradient and fast cooling rate. However, this microstructural advantage may be counterbalanced by the lack of full density, which could be tolerated to a certain degree for applications such as biomedical implants and medical equipment. In this study, medical-grade 316L stainless steel specimens with porosities ranging from 1.7 to 9.1% were additively manufactured by L-PBF using different combinations of laser power and scanning speeds. Tribological properties were evaluated by pin-on-disc testing in dry conditions against a silicon nitride test body and analyzed in the context of microstructural characterization by optical and electron microscopy. The results reveal that higher porosity allows for a diminishing wear rate, which is explained by the capacity of the pores to retain wear debris related with the three-body abrasion. This research provides practical insights into the design of medical wear-resistant components, thereby enhancing our understanding of the potential of L-PBF in the fields of materials science and biomedical engineering.

Additive manufacturing has evolved from a rapid prototyping technology to a technology with the ability to produce highly complex parts with superior mechanical properties than those obtained conventionally. The processing of metallic powders by means of a laser makes it possible to process any type of alloy and even metal matrix composites. The present work analyzes the tensile and compressive response of 316L stainless steel processed by laser-based powder bed fusion. The resulting microstructure was evaluated by optical microscopy. Regarding the mechanical properties, the yield strength, ultimate tensile strength, percentage of elongation before breakage, compressive strength and microhardness were determined. The results show that the microstructure is constituted by stacked micro molten pools, within which cellular sub-grains are formed due to the high thermal gradient and solidification rate. The compressive strength (1511.88 ± 9.22 MPa) is higher than the tensile strength (634.80 ± 11.62 MPa). This difference is mainly associated with strain hardening and the presence of residual stresses. The initial microhardness was 206.24 ± 11.96 HV; after the compression test, the hardness increased by 23%.

Cu-11Al-5Ni-4Fe wt% alloy is processed by additive manufacturing using the laser powder bed fusion (LPBF) technique in two building orientations (90° and 0° to the building platform) to determine which laser parameters are more critical to obtain better mechanical properties. The resulting printed material is characterized microstructurally and mechanically by XRD, optical microscope, and under compressive stress. The results indicate that the alloy achieved good densification and mechanical properties similar to an as-cast counterpart. The as-built microstructure comprises acicular martensite and other thermodynamic equilibrium phases, while exothermic and endothermic curves show a poor response to temperature induce phase transformation at ~350 °C. The mechanical results show that it is possible to reach a compressive strength of 1300 MPa, up to 20% strain, and better mechanical properties in those samples fabricated in a vertical direction (90°) than the horizontal. The volumetric energy density also affects the samples’ maximum strength and superficial roughness.