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However, traditional defects characterization techniques, such as X\u2010ray CT and ultrasonic scanning, are costly and time\u2010consuming. There is a research gap in the nondestructive evaluation of fatigue performance directly from the process signature of laser\u2010based additive manufacturing processes. Herein, a novel two\u2010phase modeling methodology is proposed for fatigue life prediction based on in situ monitoring of thermal history. Phase (I) includes a convolutional neural network designed to detect the relative size of the defects (i.e., small gas pores and large lack\u2010of\u2010fusions) by leveraging processed thermal images. Subsequently, a fatigue\u2010life prediction model is trained in Phase (II) by incorporating the defect characteristics extracted from Phase (I) to evaluate the fatigue performance. 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