In Situ Nondestructive Fatigue-Life Prediction of Additive Manufactured Parts by Establishing a Process-Defect-Property Relationship

The presence of process-induced internal defects (i.e., pores, microcracks, and lack-of-fusions) significantly deteriorates the structural durability of parts fabricated by additive manufacturing. However, traditional defects characterization techniques, such as X-ray CT and ultrasonic scanning, are costly and time-consuming. There is a research gap in the nondestructive evaluation of fatigue performance directly from the process signature of laser-based additive manufacturing processes. Herein, a novel two-phase 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-of-fusions) by leveraging processed thermal images. Subsequently, a fatigue-life prediction model is trained in Phase (II) by incorporating the defect characteristics extracted from Phase (I) to evaluate the fatigue performance. Estimating defect characteristics from the in situ thermal history facilitates the fatigue predicting process.

Automated summary

The study focuses on evaluating fatigue performance directly from the process signature of laser-based additive manufacturing processes, proposing a novel two-phase modeling methodology. In Phase (I), a convolutional neural network is used to detect the relative size of defects, while Phase (II) incorporates defect characteristics to build a fatigue-life prediction model. Estimating defect characteristics from the in situ thermal history facilitates the fatigue predicting process.