Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

In the frame of additive manufacturing of metals, laser powder-bed fusion is investigated in this paper as an advanced industrial prototyping tool to manufacture Inconel 718 turbine blades at a predesign stage before flow production. Expediting of the evaluation of any upgrade to the part is aimed. To this purpose, possible anisotropy of manufacturing is preliminarily investigated via tensile testing at room and elevated temperature as a function of the sloping angle with the building plate; the normalized strength is given and compared with similar studies in the literature. Positioning and proper supporting in manufacturing are discussed; the parts are further investigated to assess their compliance with the intended nominal geometry.

Laser powder bed fusion (LPBF) can fabricate products with tailored mechanical and surface properties. In fact, surface texture, roughness, pore size, the resulting fractional density, and microhardness highly depend on the processing conditions, which are very difficult to deal with. Therefore, this paper aims at investigating the relevance of the volumetric energy density (VED) that is a concise index of some governing factors with a potential operational use. This paper proves the fact that the observed experimental variation in the surface roughness, number and size of pores, the fractional density, and Vickers hardness can be explained in terms of VED that can help the investigator in dealing with several process parameters at once.

Optimization of processing parameters and exposure strategies is usually performed in additive manufacturing to set up the process; nevertheless, standards for roughness may not be evenly matched on a single complex part, since surface features depend on the building direction of the part. This paper aims to evaluate post processing treating via laser surface modification by means of scanning optics and beam wobbling to process metal parts resulting from selective laser melting of stainless steel in order to improve surface topography. The results are discussed in terms of roughness, geometry of the fusion zone in the cross-section, microstructural modification, and microhardness so as to assess the effects of laser post processing. The benefits of beam wobbling over linear scanning processing are shown, as heat effects in the base metal are proven to be lower.

One of the main current challenges in the field of additive manufacturing and directed energy deposition of metals, is the need for simulation tools to prevent or reduce the need to adopt a trial-and-error approach to find the optimum processing conditions. A valuable help is offered by numerical simulation, although setting-up and validating a reliable model is challenging, due to many issues related to the laser source, the interaction with the feeding metal, the evolution of the material properties and the boundary conditions. Indeed, many attempts have been reported in the literature, although some issues are usually simplified or neglected. Therefore, this paper is aimed at building a comprehensive numerical model for the process of laser-assisted deposition. Namely: the geometry of the deposited metal is investigated in advance and the most effective reference shape is found to feed the simulation as a function of the governing factors for single- and multi-track, multi-layer deposition; then, a non-stationary thermal model is proposed and the underlying hypotheses to simulate the addition of metal are discussed step-by-step. Validation is eventually conducted, based on experimental evidence. Aluminum alloy 2024 is chosen as feeding metal and substrate.