In situ acoustic emission monitoring of defect formation in additively manufactured refractory alloy components

This work proposes to use AE for in-situ monitoring of refractory alloy additive manufacturing, with a focus on laser engineered net shaping (LENS), a type of laser directed energy deposition (LDED) process. While AE signals are being collected during printing of refractory alloy components, pores and cracking will be intentionally introduced. The melt pool will be monitored via optical tomography. To confirm the size and location of defects, completed parts will be inspected via XCT and then sectioned for optical microscopy. I will leverage machine learning (ML) to examine the relationships between features extracted from AE signals and ground truth data that characterizes defects to develop an ML model that can be applied in real time. The new model will use only the acoustic emission signals as input to allow for in-situ monitoring. Using both in-situ (optical tomography) and ex-situ (XCT and optical microscopy) ground truth data will combat the high false-positive rate of defect detection that is a common challenge of AE monitoring by considering the possibility of defect healing on subsequent passes. The proposed work responds to TA12.4.2.2: Model- Based Operations and TA12.4.2.3: Additive Manufacturing by integrating smart sensors and analysis and decision tools into the LDED process. Refractory alloys are used in propulsion structures, leading edge materials, reactor fuels for nuclear thermal propulsion, and in other high-temperature in-space structures. Thus, the proposed work also aligns directly with TA12.1.4.2 High/Ultra-High Temperature Material Technology Candidate.

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