The aim is to discuss the ultrasound technique applied to the inspection of resistance spot welding, and to demonstrate the advantages of this technique over the conventionally standardized destructive tests. For this, two experimental procedures were organized, the first using the a-scan (single element transducer) ultrasound technique, in which the effects of the indentation of the spot weld were studied, one of the parameters that can be easily detected by this technique. The second experiment seeks to demonstrate the detection capacity of the ultrasonic b-scan technique (matrix transducer), in which the methodology was applied to seek the correlation between the results obtained by the equipment with the results found in the peel test and also in the macrographic results conventionally known. With these experiments, it was possible to prove the reliability and reproducibility of this technique, showing an increase in precision when related to the normalized known tests, besides the quantitative evaluation that can be made, allowing the statistical collection of data.

Stocco, DaniloCaracterização de solda por resistência a ponto através de avaliações não destrutivas / D. Stocco. --São Paulo, 2010. 110 p. Dissertação (Mestrado) -

No abstract

Adaptive resistance spot welding systems typically rely on real-time analysis of dynamic resistance curves and other indirect measurements to estimate weld progress and guide adaptive weld control algorithms. Though efficient, these approaches are not always reliable, and consequently there is a need for improved feedback systems to drive adaptive welding algorithms. As an alternative, an advanced in-line integrated ultrasonic monitoring system is proposed, with real-time weld process characterization driven by artificial intelligence (AI) to create actionable feedback for the weld controller. Such a system would require real-time ultrasonic data interpretation, and for this a solution using deep learning was investigated. The proposed solution monitors the ultrasonic data for key process events and estimates the vertical size of the weld nugget proportional to the stack size throughout the welding process. This study shows that adaptive welding using ultrasonic process monitoring backed by AI-based data interpretation has immense potential. This research highlights the importance of nondestructive evaluation (NDE) in the zero-defect manufacturing paradigm.

Though manufacturing is becoming increasingly technologically advanced, statistical destructive and nondestructive evaluation (NDE) are still the dominant methods for quality control and Industry/NDE 4.0 is still not fully realized. Ultrasonic inspection systems are increasingly used, but there is still a need for fast, automated, and accurate data interpretation. To this end, the IDIR has developed an approach for ultrasonic B-scan interpretation using deep learning (DL) which is a form of artificial intelligence (AI) using deep artificial neural networks to automatically learn from data. DL forms the state of the art in many problem domains in e.g. natural language processing and computer vision, hence it has become increasingly, and often successfully, applied in NDE. Our aim was to investigate a DL approach to automatic characterization of ultrasonic B-scans. We experimented on ultrasonic B-scans from resistance spot welding (RSW) because we could rapidly generate a large dataset of samples using this process. We developed and labelled a dataset of ultrasonic B-scans from RSWs of varying parameterizations, along with important metadata (e.g. sheet thicknesses, weld time, etc.), and subsequently trained DL models for object detection on the labelled samples. The resultant AI system conducts a morphological analysis of the weld geometry after the weld is completed. Using an object detection approach, we created models that exhibit high detection rates with extremely low false positive rates, while accurately measuring the position of the nugget within the welded stack. Our work shows the applicability of DL in real-time NDE data interpretation. Such AI-based systems can be combined with ultrasonic NDE to comprehensively, accurately, and practically instantly characterize 100% of parts without human intervention, representing a major step toward Industry/NDE 4.0 and zero-defect RSW.