Due to the intense concentration of heat in a reduced area when Gas Metal Arc Welding (GMAW) is used to join mechanical components, the regions near the weld bead are subjected to severe thermal cycles. Firstly, the region close to the weld bead that is heated tends to be in compression and, when it cools, tends to be in tension. According to Pilipenko, the material is exposed to elastic compression and, then, reaching the yield limit, undergoes plastic deformation with the appearance of residual stresses followed by elastic-plastic unloading. This could be considered as a strain-stress cycle. This paper applies plastic-strain-range memorization based on time-independent cyclic plasticity theory for butt joints with single V-groove Finite Element (FE) models that were manufactured by GMAW. The theory combines both the isotropic hardening and the nonlinear kinematic hardening rule (Chaboche model) to reproduce the behavior of cyclic plasticity and thus to obtain the residual stresses in welded joint FE models. As a practical example, the proposed theory is validated by three welded joint specimens that were manufactured with different input parameters of speed, voltage, and current. An agreement between the residual stresses obtained by the FE model proposed and those obtained experimentally by the hole drilling method at different depths demonstrates that the proposed theory could be valid for modelling the residual stresses in welded joints when cyclic plasticity is considered over the range of speed, voltage, and current studied.

The robotic welding manufacturing of metal parts is a very important process, especially in heavy industries such as shipbuilding, oil and gas, automotive, and aerospace. There is a great variety of different techniques for manufacturing by robotic welding, and the welding operations are always in a constant process of evolution, as any advance can be significant to avoid defects during the welding process. Although a great deal of research work has been carried out in recent years, thanks to which results and reviews have been presented on this subject, the main aim of this publication is to define and review works that show the advances in the main inspection, modeling, monitoring, and automated operations during the welding process to avoid, or predictively identify, any possible defect in order to obtain an optimum degree of quality in the welding.

The automation of welding processes requires the use of automated systems and equipment, in many cases industrial robotic systems, to carry out welding processes that previously required human intervention. Automation in the industry offers numerous advantages, such as increased efficiency and productivity, cost reduction, improved product quality, increased flexibility and safety, and greater adaptability of companies to market changes. The field of welding automation is currently undergoing a period of profound change due to a combination of technological, regulatory, and economic factors worldwide. Nowadays, the most relevant aspect of the welding industry is meeting customer requirements by satisfying their needs. To achieve this, the automation of the welding process through sensors and control algorithms ensures the quality of the parts and prevents errors, such as porosity, unfused areas, deformations, and excessive heat. This paper proposes an intelligent and adaptive system based on the measurement of welding joints using laser scanning and the subsequent analysis of the obtained point cloud to adapt welding trajectories. This study focuses on the optimization of T-joints under specific welding conditions and is intended as an initial implementation of the algorithm, thus establishing a basis to be worked on further for a broader welding application.

The paper introduces an innovative aerospace component production approach employing Wire Arc Additive Manufacturing (WAAM) technology to fabricate near-finished preforms from Ti₆Al₄V titanium. Tensile tests on WAAM Ti₆Al₄V workpieces demonstrated reliable mechanical properties, albeit with identified anisotropic behavior in horizontal samples, underscoring the need for optimization. This alternative manufacturing strategy addresses the challenges associated with machining forged preforms, marked by a high Buy To Fly (BTF) ratio (>10), leading to material wastage, prolonged machining durations, elevated tool expenses, and heightened waste and energy consumption. Additionally, logistical and storage costs are increased due to extended delivery timelines, exacerbated by supply issues related to the current unstable situation. The utilization of WAAM significantly mitigates initial BTF, preform costs, waste production, machining durations, and associated expenditures, while notably reducing lead times from months to mere hours. The novelty in this study lies in the application of Wire Arc Additive Manufacturing (WAAM) technology for the fabrication of titanium aircraft components. This approach includes a unique height compensation strategy and the implementation of various deposition strategies, such as single-seam, overlapping, and oscillating.

This chapter explores the latest advancements and methodologies in directed energy deposition (DED-arc) manufacturing. The introduction sets the stage for understanding the significance of these developments in the context of modern manufacturing needs. The discussion includes design strategies for DED-arc, emphasizing topological optimization, functional design, and generative design, alongside the application of artificial intelligence (AI) in enhancing design processes. Innovative approaches to material hybridization are detailed, focusing on both multilayer and in situ techniques for combining different materials to optimize component performance. The paper also covers slicing and pathing, examining slicing strategies, the use of lattice structures, and the implementation of 2D and 3D patterns to improve manufacturing efficiency and product quality. The conclusion summarizes key findings, discusses their implications for the additive manufacturing industry, and suggests potential future research directions in DED-arc technology, highlighting the emerging trends and innovations that are shaping the field.