Automated Defect Recognition for Welds Using Simulation Assisted TFM Imaging with Artificial Intelligence

In this paper, Artificial Intelligence (AI) algorithms are employed for first, automating the process of creating a large synthetic Total Focusing Method (TFM) imaging dataset using a small set of Finite Element (FE) simulation datasets, and second for the automated defect-recognition (ADR) in butt-welds. In this paper, six types of imaging datasets are created with three approaches. In the first approach, weld TFM images are constructed using ultrasonic A-scan signals obtained from Full Matrix Capture (FMC) performed using FE analysis on models with weld defects (porosity and slag). The second approach generates near real-time weld TFM images by implementing fast deep convolution generative adversarial networks (DCGAN). This second technique permits simulations that are several orders faster when compared to the FE method. In the third approach, noise is extracted from FMC-TFM experimental measurements using the sliding kernel approach, and this noise is supplemented to individual simulated datasets for creating near to realistic scenarios. The first dataset is created using the first approach. The second dataset is created using the second approach, and the third hybrid dataset is a combination of FE and DCGAN weld TFM imaging. The fourth dataset is noise supplemented to FE based dataset. The fifth dataset is generated by adding noise to DCGAN images. The sixth hybrid dataset with noise is a combination of FE and DCGAN weld TFM noise images. AI plays a significant role in object detection and classification through robust feature extraction, reducing human intervention. In this work, for automated weld defect recognition, a convolutional neural network (CNN) is trained using six types of simulation-assisted weld TFM imaging datasets, which improves the reliability and efficiency of welds quality assurance. The mAP value is 85% for the ADR model trained using the hybrid weld TFM dataset with noise. The model prediction on classification on the hybrid dataset for porosity is 0.86 F1-score, and for slag is 0.80 F1-score.