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Additive manufacturing is a valid solution to build complex geometries, including lightweight structures. Among these, gyroids offer a viable concept for bone tissue application, although many preliminary trials would be required to validate the design before actual implantation. In this frame, this study is aimed at presenting the background and the steps to build a numerical simulation to extract the mechanical behaviour of the structure, thus reducing the experimental effort. The results of the simulation are compared to the actual outcome resulting from quasi-static compressive tests and the effectiveness of the model is measured with reference to similar studies presented in the literature about other lightweight structures.

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., Competing Interests: The authors declare no conflict of interest.

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.

With the development of additive manufacturing, the building of new categories of lightweight structures such as random foams have been offered. Nevertheless, given the complexity of the required parts, macroscopic defects may result or the process may even fail. Therefore, proper actions must be taken at the design stage. In this paper, a method of design for additive manufacturing (DfAM) to build metal random foam structures is proposed. Namely, a procedure is suggested to generate a structure that has interconnected porosity. This procedure is based on the aimed fractional density and several technical requirements, and then the geometry is optimized and meshed. To validate the algorithm, a test article consisting of a metal cylinder with spherical random pores ranging from 1 to 6 mm in diameter with a resulting fractional density of 40 ± 2% has been conceived and manufactured by means of laser powder bed fusion (LPBF). On the basis of the outcome of the manufacturing process, crucial information has been gathered to update the algorithm.

The modeling of laser-based processes is increasingly addressed in a competitive environment for two main reasons: Preventing a trial-and-error approach to set the optimum processing conditions and non-destructive real-time control. In this frame, a thermal model for laser heating in the form of non-penetrative bead-on-plate welds of aluminum alloy 2024 is proposed in this paper. A super-Gaussian profile is considered for the transverse optical intensity and a number of laws for temperature-dependent material properties have been included aiming to improve the reliability of the model. The output of the simulation in terms of both thermal evolution of the parent metal and geometry of the fusion zone is validated in comparison with the actual response: namely, a two-color pyrometer is used to infer the thermal history on the exposed surface around the scanning path, whereas the shape and size of the fusion zone are assessed in the transverse cross-section. With an average error of 3% and 4%, the model is capable of predicting the peak temperature and the depth of the fusion zone upon laser heating, respectively. The model is intended to offer a comprehensive description of phenomena in laser heating in preparation for a further model for repairing via additive manufacturing.

Laser welding of titanium alloys is attracting increasing interest as an alternative to traditional joining techniques for industrial applications, with particular reference to the aerospace sector, where welded assemblies allow for the reduction of the buy-to-fly ratio, compared to other traditional mechanical joining techniques. In this research work, an investigation on laser welding of Ti⁻6Al⁻4V alloy plates is carried out through an experimental testing campaign, under different process conditions, in order to perform a characterization of the produced weld bead geometry, with the final aim of developing a cognitive methodology able to support decision-making about the selection of the suitable laser welding process parameters. The methodology is based on the employment of artificial neural networks able to identify correlations between the laser welding process parameters, with particular reference to the laser power, welding speed and defocusing distance, and the weld bead geometric features, on the basis of the collected experimental data.

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., Competing Interests: The authors declare no conflict of interest.

In the framework of Additive Manufacturing of metals, Directed Energy Deposition of steel powder over flat surfaces and edges has been investigated in this paper. The aims are the repair and overhaul of actual, worn-out, high price sensitive metal components. A full-factorial experimental plan has been arranged, the results have been discussed in terms of geometry, microhardness and thermal affection as functions of the main governing parameters, laser power, scanning speed and mass flow rate; dilution and catching efficiency have been evaluated as well to compare quality and effectiveness of the process under conditions of both flat and edge depositions. Convincing results are presented to give grounds for shifting the process to actual applications: namely, no cracks or pores have been found in random cross-sections of the samples in the processing window. Interestingly an effect of the scanning conditions has been proven on the resulting hardness in the fusion zone; therefore, the mechanical characteristics are expected to depend on the processing parameters., Competing Interests: The authors declare no conflict of interest.

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.

The paper investigates the fabrication of Selective Laser Melting (SLM) titanium alloy Ti6Al4V micro-lattice structures for the production of lightweight components. Specifically, the pillar textile unit cell is used as base lattice structure and alternative lattice topologies including reinforcing vertical bars are also considered. Detailed characterizations of dimensional accuracy, surface roughness, and micro-hardness are performed. In addition, compression tests are carried out in order to evaluate the mechanical strength and the energy absorbed per unit mass of the lattice truss specimens made by SLM. The built structures have a relative density ranging between 0.2234 and 0.5822. An optimization procedure is implemented via the method of Taguchi to identify the optimal geometric configuration which maximizes peak strength and energy absorbed per unit mass.