Abstract

This study treats over the sudden cleavage fracture occurrence, sometimes called “reverse fracture”, in drop-weight tear testing of X65 Q&T seamless pipeline steel. This phenomenon is not new and has been around since new manufacturing techniques such as thermomechanical controlled processes (TMCP) and seamless techniques have emerged over the last 40 years. Yet, there is no radical plausible solution for this problem as due to its stochastic nature makes it difficult to systematically study the cause and effect of catastrophic mechanisms. Here, certain key pieces of information are added to the existing body of results on this subject matter such as automated identification of cleavage sites via deep learning YOLO framework. In conjunction with advanced SEM/EBSD analysis and TEM description provide strong evidence of the nature of the microstructure and its connection with the abnormal fracture behavior.

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