Real-time Automated Weld Quality Analysis From Ultrasonic B-scan Using Deep Learning

Resistance spot welding is a widely used process for joining metals using electrically generated heat or Joule heating. It is one of the most commonly used techniques in automotive industry to weld sheet metals in order to form a car body. Although, industrial robots are used as automated spot welders in massive scale in the industries, the weld quality inspection process still requires human involvement to decide if a weld should be passed as acceptable or not. Not only it is a tedious and error- prone job, but also it costs industries lots of time and money. Therefore, making this process automated and real-time will have high significance in spot welding as well as the field of Non-destructive Testing (NDT). Research team in Institute of Diagnostic Imaging Research (IDIR) have developed technology to obtain grey-scale 2D images called ultrasonic b-scans in real-time during production in order to visualize the weld development with respect to time. They have demonstrated that by extracting and interpreting relevant patterns from these b-scans, weld quality can be determined accurately. However, current works combining conventional image and signal processing techniques are unable to extract those patterns from a wide variety of weld shapes with production-level satisfaction. Therefore, in this thesis, we propose to apply SSD, a single-shot multi-box detection based deep convolutional neural network framework for real-time embedded detection of components of cross-sectional weld shape from ultrasonic b-scans and interpret them to numeric parameters which are used as features to classify welds as good, bad or acceptable in real-time. Our proposed model has showed significant improvement in deciding weld quality compared to existing methods when tested on real industry facility

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa