AUTOMATION OF ULTRASONIC FLAW DETECTION APPLICATIONS USING DEEP LEARNING ALGORITHMS

The Industrial Revolution-4.0 promises to integrate multiple technologies including but not limited to automation, cloud computing, robotics, and Artificial Intelligence. The non-Destructive Testing (NDT) industry has been shifting towards automation as well. For ultrasound-based NDT, these technological advancements facilitate smart systems hosting complex signal processing algorithms. Therefore, this thesis introduces the effective use of AI algorithms in challenging NDT scenarios. The first objective is to investigate and evaluate the performance of both supervised and unsupervised machine learning algorithms and optimize them for ultrasonic flaw detection utilizing Amplitude-scan (A-scan) data. Several inferences and optimization algorithms have been evaluated. It has been observed that proper choice of features for specific inference algorithms leads to accurate flaw detection. The second objective of this study is the hardware realization of the ultrasonic flaw detection algorithms on embedded systems. Support Vector Machine algorithm has been implemented on a Tegra K1 GPU platform and Supervised Machine Learning algorithms have been implemented on a Zynq FPGA for a comparative study. The third main objective is to introduce new deep learning architectures for more complex flaw detection applications including classification of flaw types and robust detection of multiple flaws in B-scan data. The proposed Deep Learning pipeline combines a novel grid-based localization architecture with meta-learning. This provides a generalized flaw detection solution wherein additional flaw types can be used for inference without retraining or changing the deep learning architecture. Results show that the proposed algorithm performs well in more complex scenarios with high clutter noise and the results are comparable with traditional CNN and achieve the goal of generality and robustness