Uralde Jiménez, Virginia
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Uralde Jiménez
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Virginia
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Ingeniería
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Publication Open Access Advancements and methodologies in directed energy deposition (DED-Arc) manufacturing: design strategies, material hybridization, process optimization and artificial intelligence(IntechOpen, 2024-09-27) Uralde Jiménez, Virginia; Suárez, Alfredo; Veiga Suárez, Fernando; Villanueva Roldán, Pedro; Ballesteros Egüés, Tomás; Ingeniería; Ingeniaritza; Institute of Smart Cities - ISCThis 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.Publication Open Access AI-driven predictive modeling of homogeneous bead geometry for WAAM processes(Springer, 2025-07-15) Fernández Zabalza, Aitor; Rodríguez Díaz, Álvaro; Veiga Suárez, Fernando; Suárez, Alfredo; Uralde Jiménez, Virginia; Ballesteros Egüés, Tomás; Alfaro López, José Ramón; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaWith 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.