High-Throughput Strategies that Encompass Experiments and Machine Learning to Predict the Mechanical Properties of Additive Manufactured Aerospace Alloys

Small Punch Test (SPT) uses a thin disk of material to predict mechanical properties. While SPT has existed for decades, it has been used largely as a qualitative evaluator of mechanical properties. Recent advances in computational modeling have enabled the extraction of uniaxial stress-strain response from the measured SPT load-displacement data. Due to small sample volumes and unidirectional testing, SPT is conducive to high-throughput automation and ideally suited to extract properties from high-cost materials. Aerospace alloys have been of recent interest to the Additive Manufacturing (AM) community due to AM’s unique ability to fabricate complex designs not possible, or extremely arduous, with conventional manufacturing. In this research, SPT, coupled with Materials Informatics and computational modeling, is used to develop relevant Process-Structure-Property relationships to decrease the cost and time of process optimization for AM aerospace alloys, namely Inconel 718, Inconel 625, and Niobium C103.