Virginia Uralde

Metal additive manufacturing (MAM) offers automotive manufacturers design and supply-chain options, but published demonstrations do not by themselves establish production readiness or economic advantage. This critical narrative review connects process selection, alloy behaviour, component requirements and the complete manufacturing chain. It covers laser powder bed fusion, directed energy deposition, binder jetting and metal material extrusion, distinguishing direct vehicle parts from tooling, repair and spare parts. The synthesis identifies the following three recurring constraints: fatigue performance cannot be inferred from tensile strength; sintering-based routes require control of shrinkage and local defects; and printing productivity must be separated from output of accepted finished parts. Published examples include a 10% piston mass reduction and a 17% moulding-cycle reduction, both reported by industrial sources rather than established as sector-wide effects. A requirement-led selection framework, condition-specific material benchmarks and a qualitative economic assessment connect these findings to engineering decisions. The strongest opportunities occur where functional integration, avoided tooling or asset-life extension offset qualification and post-processing costs. Evidence remains heterogeneous and the initial single-database search limits coverage; the review therefore does not claim a systematic census or a universal ranking of technologies. Adoption should be judged at the level of a qualified alloy–process–part combination against a credible conventional alternative.

A methodology for structured WAAM planning and conversational toolpath adaptation is described. The workflow comprises work-coordinate-system definition, part slicing and generation of a baseline trajectory from operator-specified process parameters. A structured JSON contract separates path planning from NC generation and enables AI-assisted translation of operator requests into bounded, inspectable trajectory transformations. Baseline geometry and selected comparison parameters remain fixed for the primary three-strategy case, while requested changes, affected regions and verification results are recorded. Independent checks assess geometric correspondence and centreline containment before human review. The primary case demonstrates bounded waving and region-dependent deposition direction; a supplementary cylindrical-bracket case is presented only as a plan-level concentric-path illustration. These computational examples assess traceability, replay and file readability; they do not establish process qualification, machine safety or physical deposition performance.

This work presents the fabrication and characterization of bimetallic walls produced by Wire Arc Additive Manufacturing (WAAM), employing Inconel 718 and stainless steel 316L arranged through both superimposed and overlapping bead strategies. The study evaluates the resulting microstructure, hardness distribution, mechanical performance, and corrosion behavior of the deposited structures. Minor microcracks and solidification-related defects were detected at the interfacial region. The microstructure is primarily dendritic, consisting of an austenitic matrix enriched with Laves phases and cubic TiN precipitates. Hardness values are higher in the Inconel zones, lower in the stainless-steel regions, and intermediate at the interface. Despite the presence of interfacial defects and brittle secondary phases, the overall mechanical strength and ductility remain comparable to those reported for WAAM-fabricated 316L stainless steel. In corrosion testing, the bimetallic specimens displayed global corrosion rates similar to those of their monolithic counterparts; however, they exhibited a significantly narrower passive range. This behavior is attributed to pronounced compositional gradients between Ni-rich and Fe-rich regions, which foster micro-galvanic coupling and intensify localized corrosion on the stainless-steel side of the interface.

Metal additive manufacturing (MAM) enables the production of complex, high-value components for sectors such as aerospace, energy, and biomedical engineering. However, its large-scale industrial adoption remains constrained by internal defects, residual stresses, distortions, microstructural variability, and the complexity of the coupled process-parameter space. This review examines how artificial intelligence (AI)—including machine learning, deep learning, and optimization algorithms—is being applied to address these challenges across the MAM workflow. A structured literature review was conducted covering studies published between 2015 and 2025, identified through searches in Scopus, Web of Science, and IEEE Xplore. The selected literature is analyzed according to key functional domains of metal additive manufacturing: design for additive manufacturing (DfAM), process modeling and simulation, in situ monitoring and control, and microstructure and property prediction. AI approaches are further categorized by learning paradigm, including supervised learning, deep learning, reinforcement learning, and hybrid physics–machine learning models. The review highlights recent advances in AI-assisted parameter optimization, defect detection, and digital-twin frameworks for process supervision. At the same time, it identifies persistent challenges, particularly the scarcity and heterogeneity of datasets, limited transferability across machines and materials, and the need for uncertainty-aware models capable of supporting validation and certification. Overall, the analysis indicates that the integration of multi-sensor monitoring with hybrid physics-informed AI models represents the most promising near-term pathway to improve process reliability, reduce trial-and-error experimentation, and accelerate industrial qualification in metal additive manufacturing.

CO₂ emissions continue to rise, along with the associated environmental risks. In response, the United Nations has been promoting the adoption of sustainable practices among businesses worldwide. In parallel, an innovative technology known as additive manufacturing (AM) has emerged over the past four decades. This technology has the potential to be more sustainable than conventional manufacturing (CM) technologies. When metals are used as the material, the process is referred to as metal additive manufacturing (mAM). AM technologies have seven process categories, which include metal mAM processes, most notably powder bed fusion (PBF), directed energy deposition (DED), binder jetting (BJT), material extrusion of metal-filled feedstock, and sheet lamination. Among these, PBF and DED are by far the most widely applied metal AM technologies in both industrial practice and academic research. The use of mAM is increasing; however, is it truly more sustainable than CM? Motivated by this question, a systematic literature review (SLR) was conducted to compare the sustainability impacts of mAM and CM across the three dimensions of sustainability: environmental, economic, and social. The evidence shows mixed sustainability outcomes, which are synthesized later in the conclusions. The sustainability comparison is influenced by factors like part redesign with topological optimization (TO), the material and energy mix used, geometric complexity, production volume per batch, and the boundaries adopted. Economic viability remains critical; companies are unlikely to adopt mAM if it proves more expensive than CM as this could threaten its competitiveness. Social impacts are the least studied dimension, and it is difficult to anticipate the changes that might occur because of such a transition.

Topology optimisation and lattice design constitute key enablers in the transition towards high-performance and resource-efficient engineering, particularly within the framework of additive manufacturing and welding-based deposition processes. The increasing integration of arc-based technologies, such as Wire Arc Additive Manufacturing, has strengthened the relevance of these methodologies by enabling the fabrication of large-scale, structurally efficient components with controlled material distribution and mechanical performance. These design strategies provide unique opportunities to achieve lightweight structures, functionally graded behaviour, and tailored properties beyond the limitations imposed by conventional manufacturing and joining techniques. The growing demand for functionally efficient components in sectors such as aerospace, biomedical, and automotive engineering continues to drive the adoption of these approaches, where both material efficiency and structural integrity under welding-induced thermal effects are critical. This chapter introduces the fundamentals of topology optimisation and functionally graded lattice architectures, describes their integration into advanced design and manufacturing workflows, including welding-based additive processes, and presents selected case studies that demonstrate their practical impact. Finally, emerging strategies based on generative design and artificial intelligence are discussed as key drivers for the automated and process-aware optimisation of future additively manufactured and welded structures.

This article presents the development and validation of a thermal model for the Wire Arc Additive Manufacturing (WAAM) process. The model is based on the Finite Element Method (FEM) and is designed to predict the thermal distribution of the substrate where the welded bead is going to be deposited. To validate the thermal model, an experimentation process was carried out using three different substrate thicknesses (8, 10 and 30 mm), while varying key process parameters that influence heat input (such as deposition rate and travel speed). The simulation results were compared with experimental data by analyzing critical thermal parameters such as the area, height, and width of the heat affected zone (HAZ), as well as the melt pool dimensions. The HAZ geometry obtained from the thermal simulation showed strong agreement with experimental observations, confirming the model’s accuracy. Then, the model was extended to simulate a broader range of travel speed and deposition rate per sample thickness. This allowed for a more comprehensive study of how these parameters affect the thermal response of the substrate. The expanded dataset generated from the simulations was used to train a sequential artificial neural network and then compared with experimentation obtained results. The ANN model demonstrated reliable predictive performance across all cases, with the lowest error observed for the 30?mm substrate thickness due to its reduce extreme temperature gradients and transient thermal fluctuations. The novelty of this work lies in the integration of a validated FEM thermal model with a sequential ANN, enabling rapid and accurate prediction of thermal fields in WAAM process. Keywords: WAAM; HAZ; melt pool; FEM; ANN

With the increasing number of applications employing additive manufacturing solutions, these deposition processes must become more autonomous, which can be helped by the application of machine learning monitoring. This study presents a fully online, low-cost framework for real-time quality control in Invar wire-arc additive manufacturing (WAAM). Synchronized current and voltage signals are transformed into spatial heatmaps and temporal Markov transition images, which are processed by an optimized ResNet-18 to classify the quality of each layer on-the-fly. Validation using cross-validation on an internal Invar dataset yields an accuracy of up to 94% under clean conditions, with inference times below 20 ms per layer, enabling deployment during natural cooling between layers. These results demonstrate the feasibility of non-intrusive signal-based anomaly detection, enabling rapid identification of weld spalls and useful for scalable and automated WAAM monitoring in industrial environments.

Additive manufacturing (AM) is transforming the production of steel components, offering unique advantages such as design freedom and the ability to create complex geometries. This review examines the corrosion behavior of various steel types, including austenitic stainless steels (SS), martensitic SS, duplex SS, low‐alloy steels, and maraging steels, produced through AM technologies. In addition, the topic of material hybridization through AM is addressed, which allows for the optimization of the properties of the base materials. While AM often generates finer grain structures, particularly in SS, which enhances corrosion resistance, it can also lead to undesirable phases, precipitates, or defects like porosity that degrade performance. Controlling AM process parameters is crucial to achieving the desired microstructure and optimizing corrosion resistance. The review highlights current knowledge, identifies challenges, and underscores the importance of standardized testing methodologies to enable better cross‐study comparisons and guide future advancements in corrosion‐resistant AM steels.