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 - ISC
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.
Open Access
Analysis of the machining process of short carbon fiber-reinforced polyamide additive manufactured parts
(Elsevier, 2024) Suárez, Alfredo; Veiga Suárez, Fernando; Penalva Oscoz, Mariluz; Ramiro, Pedro; Ballesteros Egüés, Tomás; Ingeniería; Ingeniaritza
In recent years, additive manufacturing technologies have revolutionized the production of parts, particularly in the aeronautical sector. This new manufacturing paradigm has created significant challenges and opportunities for researchers in materials and manufacturing processes. One important aspect is the development of optimal strategies for finishing-oriented machining of parts produced through additive manufacturing. This article focuses on the analysis of reinforced polyamide materials and the integration of large-scale additive manufacturing using Big Area Additive Manufacturing (BAAM) technology, along with robotic systems for subtractive machining. The aim is to explore the potential of integrating additive and subtractive processes to produce high-quality, large-scale components. The study examines the production and subsequent machining of reinforced polyamide parts using BAAM technology, showcasing the advantages and promising results observed. By combining additive manufacturing with subtractive machining, this research contributes to the ongoing advancements in the field of manufacturing, particularly in relation to reinforced polyamide materials. The findings presented in this article shed light on the potential of integrating additive and subtractive processes in the manufacturing industry, paving the way for more efficient and high-quality production methods.
Open Access
Novel sensorized additive manufacturing-based enlighted tooling concepts for aeronautical parts
(Springer Nature, 2024-07-31) Uralde Jiménez, Virginia; Veiga Suárez, Fernando; Suárez, Alfredo; López, Alberto; Goenaga, Igor; Ballesteros Egüés, Tomás; Ingeniería; Ingeniaritza; Institute of Smart Cities - ISC
This paper presents lightweight tooling concepts based on additive manufacturing, with the aim of developing advanced tooling systems as well as installing sensors for real-time monitoring and control during the anchoring and manufacturing of aeronautical parts. Leveraging additive manufacturing techniques in the production of tooling yields benefits in manufacturing flexibility and material usage. These concepts transform traditional tooling systems into active, intelligent tools, improving the manufacturing process and part quality. Integrated sensors measure variables such as displacement, humidity and temperature allowing data analysis and correlation with process quality variables such as accuracy errors, tolerances achieved and surface finish. In addition to sensor integration, additive manufacturing by directed energy arc and wire deposition (DED-arc) has been selected for part manufacturing. The research includes the mechanical characterisation of the material and the microstructure of the material once manufactured by DED-arc. Design for additive manufacturing" principles guide the design process to effectively exploit the capabilities of DED-arc. These turrets, equipped with sensors, allow real-time monitoring and control of turret deformation during clamping and manufacturing of aeronautical parts. As a first step, deformation monitoring is carried out within the defined tolerance of ± 0.15, which allows a control point to be established in the turret. Future analysis of the sensor data will allow correlations with process quality variables to be established. Remarkably, the optimised version of the turret after applying DED technology weighed only 2.2 kg, significantly lighter than the original 6 kg version. Additive manufacturing and the use of lightweight structures for fixture fabrication, followed by the addition of sensors, provide valuable information and control, improving process efficiency and part quality. This research contributes to the development of intelligent and efficient tool systems for aeronautical applications.
Open Access
Application-oriented data analytics in large-scale metal sheet bending
(MDPI, 2023) Penalva Oscoz, Mariluz; Martín, Ander; Martínez, Víctor; Veiga Suárez, Fernando; Gil del Val, Alain; Ballesteros Egüés, Tomás; Favieres Ruiz, Cristina; Ingeniería; Ingeniaritza
The sheet-metal-forming process is crucial in manufacturing various products, including pipes, cans, and containers. Despite its significance, controlling this complex process is challenging and may lead to defects and inefficiencies. This study introduces a novel approach to monitor the sheet-metal-forming process, specifically focusing on the rolling of cans in the oil-and-gas sector. The methodology employed in this work involves the application of temporal-signal-processing and artificial-intelligence (AI) techniques for monitoring and optimizing the manufacturing process. Temporal-signal-processing techniques, such as Markov transition fields (MTFs), are utilized to transform time series data into images, enabling the identification of patterns and anomalies. synamic time warping (DTW) aligns time series data, accommodating variations in speed or timing across different rolling processes. K-medoids clustering identifies representative points, characterizing distinct phases of the rolling process. The results not only demonstrate the effectiveness of this framework in monitoring the rolling process but also lay the foundation for the practical application of these methodologies. This allows operators to work with a simpler characterization source, facilitating a more straightforward interpretation of the manufacturing process.
Open Access
Benefits of aeronautical preform manufacturing through arc-directed energy deposition manufacturing
(MDPI, 2023) Suárez, Alfredo; Ramiro, Pedro; Veiga Suárez, Fernando; Ballesteros Egüés, Tomás; Villanueva Roldán, Pedro; Ingeniería; Ingeniaritza
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.
Open Access
Symmetry analysis in wire arc direct energy deposition for overlapping and oscillatory strategies in mild steel
(MDPI, 2023) Uralde Jiménez, Virginia; Veiga Suárez, Fernando; Suárez, Alfredo; Aldalur, Eider; Ballesteros Egüés, Tomás; Ingeniería; Ingeniaritza
The field of additive manufacturing has experienced a surge in popularity over recent decades, particularly as a viable alternative to traditional metal part production. Directed energy deposition (DED) is one of the most promising additive technologies, characterized by its high deposition rate, with wire arc additive manufacturing (WAAM) being a prominent example. Despite its advantages, DED is known to produce parts with suboptimal surface quality and geometric accuracy, which has been a major obstacle to its widespread adoption. This is due, in part, to a lack of understanding of the complex geometries produced by the additive layer. To address this challenge, researchers have focused on characterizing the geometry of the additive layer, particularly the outer part of the bead. This paper specifically investigates the geometrical characteristics and symmetry of walls produced by comparing two different techniques: an oscillated strategy and overlapping beads.
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 Publikoa
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.