Citation: Aldalur, E.; Suárez, A.; Curiel, D.; Veiga, F.; Villanueva, P. Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints. Metals 2023, 13, 1532. https://doi.org/10.3390/ met13091532 Academic Editor: Wei Zhou Received: 28 July 2023 Revised: 26 August 2023 Accepted: 28 August 2023 Published: 29 August 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). metals Article Intelligent and Adaptive System for Welding Process Automation in T-Shaped Joints Eider Aldalur 1 , Alfredo Suárez 1 , David Curiel 2 , Fernando Veiga 1,2 and Pedro Villanueva 2, * 1 TECNALIA, Basque Research and Technology Alliance (BRTA), Science and Technology Park of Gipuzkoa, E20009 Donostia-San Sebastián, Spain; eider.aldalur@tecnalia.com (E.A.); alfredo.suarez@tecnalia.com (A.S.); fernando.veiga@unavarra.es (F.V.) 2 Engineering Department, Public University of Navarra, Los Pinos Building, Arrosadía Campus, E31006 Pamplona, Navarra, Spain; david.curiel@unavarra.es * Correspondence: pedro.villanueva@unavarra.es Abstract: 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. Keywords: welding; robotics; automation; thick joints 1. Introduction A welded joint is defined as the union of two or more elements, creating continuity through heat and/or pressure with or without the use of filler material. Currently, there are numerous welding processes available, such as Gas Metal Arc Welding (GMAW) with a consumable electrode, which is the wire itself [1]; Flux Cored Arc Welding (FCAW) [2]; Gas Tungsten Arc Welding (GTAW) [3]; and Submerged Arc Welding (SAW) [4], among others. Among these processes, GMAW technology is widely used and will be employed in this work. In certain industries, known as heavy industries (naval industry, oil and gas sector, energy sector, etc.), many components are large-scale mechanized and welded structures. For example, in the naval industry, the construction of large ships with lengths exceeding 24 m and internal volumes T.R.G. greater than 50 requires over 1000 h of welding. These joints can present some difficulties [5], including: (i) non-uniform and irregular pre-welded grooves, (ii) the need for certified and qualified welding operators, (iii) long deposition times, and (iv) welding positions that require special skills. Furthermore, in these types of sectors, there is often high physical demand and risk for the operator. Specifically, within the different types of joints, welding thick joints has been shown in the literature to be one of the most challenging to automate, as they require multiple layers of deposited material to fill the joint [6]. Consequently, the current practice of manufacturing Metals 2023, 13, 1532. https://doi.org/10.3390/met13091532 https://www.mdpi.com/journal/metals