Multisensor inspection of laser-brazed joints in the automotive industry

Funding Information: Acknowledgments: Authors acknowledge the European Regional Development Fund (ERDF), Programa Operacional Regional de Lisboa (Lisb@2020 e Portugal2020), for its financial support via the project PROBING (POCI-01-0247-FEDER-040042, Ref. 40042) and the Portuguese company INTROSYS—Integration for Robotic Systems (www.introsys.eu accessed 3 November 2021) for technical collaboration. Authors also acknowledge FCT—MCTES for its financial support via the project UIDB/00667/2020 (UNIDEMI).Automobile laser brazing remains a complex process whose results are affected by several process variables that may result in nonacceptable welds. A multisensory customized inspection system is proposed, with two distinct non-destructive techniques: the potential drop method and eddy current testing. New probes were designed, simulated, produced, and experimentally validated in automobile’s laser-brazed weld beads with artificially introduced defects. The numerical simulations allowed the development of a new four-point probe configuration in a non-conventional orthogonal shape demonstrating a superior performance in both simulation and experimental validation. The dedicated inspection system allowed the detection of porosities, cracks, and lack of bonding defects, demonstrating the redundancy and complementarity these two techniques provide.publishersversionpublishe

Sensors for Quality Control in Welding

The classical inspection methods used for detecting and finding disturbances in welding process are based on direct measurement of its parameters as arc voltage, welding current, wire feed speed, etc. Using these inspection methods implies sensors insertion around the welding process and its presence could alter the metallic transference behavior and consequently an uneven quality as well as it can increase the production cost. For reducing these implications is necessary using a non intrusive inspection method. In this paper we will show nonintrusive methods to the weld quality inspection. These methods are based on sensor fusion, the extraction of global information coming from the interrelation data given by each sensor that, for example, sensing the spectroscopy radiation emission, the acoustic sensing of the electrical arc, the infrared emissions indicating the heat content of the weld. Finally, the fusion data will be applied to a statistical control for detecting and finding welding disturbances. The results will show that sensor fusion could be used as a tool to measure indirectly the weld quality in the GMAW process

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system

New directions for inline inspection of automobile laser welds using non-destructive testing

POCI-01-0247-FEDER-040042, Ref. 40042 FCT-SFRH/BD/108168/ EXPL/EEI-EEE/0394/2021 UIDB/00667/2020An innovative pilot installation and eddy current testing (ECT) inspection system for laser-brazed joints is presented. The proposed system detects both surface and sub-surface welding defects operating autonomously and integrated with a robotized arm. Customized eddy current probes were designed and experimentally validated detecting pore defects with 0.13 mm diameter and sub-surface defects buried 1 mm deep. The integration of the system and the manufacturing process towards an Industry 4.0 quality control paradigm is also discussed.publishersversioninpres

Exploring Infrared Sensoring for Real Time Welding Defects Monitoring in GTAW

This paper presents an evaluation of an infrared sensor for monitoring the welding pool temperature in a Gas Tungsten Arc Welding (GTAW) process. The purpose of the study is to develop a real time system control. It is known that the arc welding pool temperature is related to the weld penetration depth; therefore, by monitoring the temperature, the arc pool temperature and penetration depth are also monitored. Various experiments were performed; in some of them the current was varied and the temperature changes were registered, in others, defects were induced throughout the path of the weld bead for a fixed current. These simulated defects resulted in abrupt changes in the average temperature values, thus providing an indication of the presence of a defect. The data has been registered with an acquisition card. To identify defects in the samples under infrared emissions, the timing series were analyzed through graphics and statistic methods. The selection of this technique demonstrates the potential for infrared emission as a welding monitoring parameter sensor

Development of an acoustic emission monitoring system for crack detection during arc welding

Condition monitoring techniques are employed to monitor the structural integrity of a structure or the performance of a process. They are used to evaluate the structural integrity including damage initiation and propagation in engineering components. Early damage detection, maintenance and repairs can prevent structural failures, reduce maintenance and replacement costs, and guarantee that the structure runs securely during its service life. Acoustic emission (AE) technology is one of the condition monitoring methods widely employed in the industry. AE is an attractive option for condition monitoring purposes, the number of industrial applications where is used is rising. AE signals are elastic stress waves created by the fast release of energy from local sources occurring inside of materials, e.g. crack initiating and propagating. The AE technique includes recording this phenomenon with piezoelectric sensors, which is mounted on the surface of a structure. The signals are subsequently analysed in order to extract useful information about the nature of the AE source. AE has a high sensitivity to crack propagation and able to locate AE activity sources. It is a passive approach. It listens to the elastic stress waves releasing from material and able to operate in real-time monitoring to detect both cracks initiating and propagating. In this study, the use of AE technology to detect and monitor the possible occurrence of cracking during the arc welding process has been investigated. Real-time monitoring of the automated welding operation can help increase productivity and reliability while reducing cost. Monitoring of welding processes using AE technology remains a challenge, especially in the field of real-time data analysis, since a large amount of data is generated during monitoring. Also, during the welding process, many interferences can occur, causing difficulties in the identifications of the signals related to cracking events. A significant issue in the practical use of the AE technique is the existence of independent sources of a signal other than those related to cracking. These spurious AE signals make the discovering of the signals from the cracking activity difficult. Therefore, it is essential to discriminate the signal to identify the signal source. The need for practical data analysis is related to the three main objectives of monitoring, which is where this study has focused on. Firstly, the assessment of the noise levels and the characteristics of the signal from different materials and processes, secondly, the identification of signals arising from cracking and thirdly, the study of the feasibility of online monitoring using the AE features acquired in the initial study. Experimental work was carried out under controlled laboratory conditions for the acquisition of AE signals during arc welding processing. AE signals have been used for the assessment of noise levels as well as to identify the characteristics of the signals arising from different materials and processes. The features of the AE signals arising from cracking and other possible signal sources from the welding process and environment have also collected under laboratory conditions and analysed. In addition to the above mentioned aspects of the study, two novel signal processing methods based on signal correlation have been developed for efficiently evaluating data acquired from AE sensors. The major contributions of this research can be summarised as follows. The study of noise levels and filtering of different arc welding processes and materials is one of the areas where the original contribution is identified with respect to current knowledge. Another key contribution of the present study is the developing of a model for achieving source discrimination. The crack-related signals and other signals arising from the background are compared with each other. Two methods that have the potential to be used in a real-time monitoring system have been considered based on cross-correlation and pattern recognition. The present thesis has contributed to the improvement of the effectiveness of the AE technique for the detection of the possible occurrence of cracking during arc welding

Quality Assessment of Laser Welding Dual Phase Steels

Since non-conforming parts create waste for industry, generating undesirable costs, it is necessary to set up quality plans that not only guarantee product conformity but also cut the root causes of welding defects by developing the concept of quality at origin. Due to their increasing use in automotive industry, dual phase (DP) steels have been the chosen material for this study. A quality plan for welding DP steel components by laser was developed. This plan is divided into three parts: pre-welding, during and post-welding. A quality assessment regarding mechanical properties, such as hardness, microstructure and tensile strength, was also performed. It was revealed that DP steel does not present considerable weldability problems, except for the usual softening of the heat affected zone (HAZ) and the growth of martensite in the fusion zone (FZ), and the best analysis techniques to avoid failures in these steels are finite element method (FEM), visual techniques during welding procedure and digital image correlation (DIC) for post-weld analysis.The present work was done and funded under the scope of projects UIDB/00481/2020 and UIDP/00481/2020—FCT—Fundação para a Ciencia e a Tecnologia; and CENTRO-01-0145-FEDER- 022083—Centro Portugal Regional Operational Programme (Centro2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund. LAETA/INEGI/CETRIB is acknowledge due to the support provided in all research activities.info:eu-repo/semantics/publishedVersio

Automatic trajectory determination in automated robotic welding considering weld joint symmetry

The field of inspection for welded structures is currently in a state of rapid transformation driven by a convergence of global technological, regulatory, and economic factors. This evolution is propelled by several key drivers, including the introduction of novel materials and welding processes, continuous advancements in inspection technologies, innovative approaches to weld acceptance code philosophy and certification procedures, growing demands for cost-effectiveness and production quality, and the imperative to extend the lifespan of aging structures. Foremost among the challenges faced by producers today is the imperative to meet customer demands, which entails addressing both their explicit and implicit needs. Furthermore, the integration of emerging materials and technologies necessitates the exploration of fresh solutions. These solutions aim to enhance inspection process efficiency while providing precise quantitative insights into defect identification and location. To this end, our project proposes cutting-edge technologies, some of which have yet to gain approval within the sector. Noteworthy among these innovations is the integration of vision systems into welding robots, among other solutions. This paper introduces a groundbreaking algorithm for tool path selection, leveraging profile scanning and the concept of joint symmetry. The application of symmetry principles for trajectory determination represents a pioneering approach within this expansive field

REVISIÓN DE TÉCNICAS DE SISTEMAS DE VISIÓN ARTIFICIAL PARA LA INSPECCIÓN DE PROCESOS DE SOLDADURA TIPO GMAW

El proceso de soldadura GMAW es ampliamente estudiado debido a su alta productividad y bajo costo. En este trabajo se han revisado las investigaciones orientadas a la inspección del proceso de GMAW a través de sistemas de visión artificial con el objetivo de establecer los principales elementos utilizados en estos sistemas destacando dos categorías: métodos computacionales (software y algoritmos generales), materiales y modelos matemáticos (métodos estadísticos y numéricos). Estas categorías se traslapan en el estudio y se han utilizado para evaluar el costo en términos de recursos humanos y recursos económicos. Las investigaciones revisadas se desarrollaron en la última década, con la excepción de algunas investigaciones que desempeñaron un papel principal en el desarrollo de los sistemas de inspección de los procesos GMAW. Finalmente, se han destacado los posibles campos de investigación para aquellos que intentan explorar sistemas de visión artificial para inspección de procesos GMAW.Palabras clave: GMAW, soldadura, visión artificial, inspección