Open Access
Wall fabrication by direct energy deposition (DED) combining mild steel (ER70) and stainless steel (SS 316L): microstructure and mechanical properties
(MDPI, 2022) Uralde Jiménez, Virginia; Suárez, Alfredo; Aldalur, Eider; Veiga Suárez, Fernando; Ballesteros Egüés, Tomás; Ingeniería; Ingeniaritza
Direct energy deposition is gaining much visibility in research as one of the most adaptable additive manufacturing technologies for industry due to its ease of application and high deposition rates. The possibility of combining these materials to obtain parts with variable mechanical properties is an important task to be studied. The combination of two types of steel, mild steel ER70-6 and stainless steel SS 316L, for the fabrication of a wall by direct energy deposition was studied for this paper. The separate fabrication of these two materials was studied for the microstructurally flawless fabrication of bimetallic walls. As a result of the application of superimposed and overlapped strategies, two walls were fabricated and the microstructure, mechanical properties and hardness of the resulting walls are analyzed. The walls obtained with both strategies present dissimilar regions; the hardness where the most present material is ER70-6 is around 380 HV, and for SS 316L, it is around 180 HV. The average values of ultimate tensile strength (UTS) are 869 and 628 MPa, yield strength (YS) are 584 and 389 MPa and elongation at break are 20% and 36%, respectively, in the cases where we have more ER70-6 in the sample than SS 316L. This indicates an important relationship between the distribution of the materials and their mechanical behavior.
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
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
Symmetry and its application in metal additive manufacturing (MAM)
(MDPI, 2022) Uralde Jiménez, Virginia; Veiga Suárez, Fernando; Aldalur, Eider; Suárez, Alfredo; Ballesteros Egüés, Tomás; Ingeniería; Ingeniaritza
Additive manufacturing (AM) is proving to be a promising new and economical technique for the manufacture of metal parts. This technique basically consists of depositing material in a more or less precise way until a solid is built. This stage of material deposition allows the acquisition of a part with a quasi-final geometry (considered a Near Net Shape process) with a very high raw material utilization rate. There is a wide variety of different manufacturing techniques for the production of components in metallic materials. Although significant research work has been carried out in recent years, resulting in the wide dissemination of results and presentation of reviews on the subject, this paper seeks to cover the applications of symmetry, and its techniques and principles, to the additive manufacturing of metals.
Open Access
Corrosion behavior of additively manufactured steels: a comprehensive review
(Wiley, 2025-03-21) Villabona Gorri, Eneko; Veiga Suárez, Fernando; Rivero Fuente, Pedro J.; Uralde Jiménez, Virginia; Suárez, Alfredo; Ingeniería; Ingeniaritza; Institute for Advanced Materials and Mathematics - INAMAT2; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
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.
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.
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
Application of symmetric neural networks for bead geometry determination in wire and arc additive manufacturing (WAAM)
(MDPI, 2025-02-21) Fernández Zabalza, Aitor; Veiga Suárez, Fernando; Suárez, Alfredo; Uralde Jiménez, Virginia; Sandúa Fernández, Xabier; Alfaro López, José Ramón; Ingeniería; Ingeniaritza; Institute for Advanced Materials and Mathematics - INAMAT2
The accurate prediction of weld bead geometry is crucial for ensuring the quality and consistency of wire and arc additive manufacturing (WAAM), a specific form of directed energy deposition (DED) that utilizes arc welding. Despite advancements in process control, predicting the shape and dimensions of weld beads remains challenging due to the complex interactions between process parameters and material behavior. This paper addresses this challenge by exploring the application of symmetrical neural networks to enhance the accuracy and reliability of geometric predictions in WAAM. By leveraging advanced machine learning techniques and incorporating the inherent symmetry of the welding process, the proposed models aim to precisely forecast weld bead geometry. The use of neuronal networks and experimental validation demonstrate the potential of symmetrical neural networks to improve prediction precision, contributing to more consistent and optimized WAAM outcomes.