Characterization of Elastoplastic Properties of Additively Manufactured Specimens From Indentation Data Using Stochastic Inverse Modeling Available to Purchase

Rapid characterization of mechanical properties and material structure of additively manufactured (AM) components via non-destructive techniques (NDT) is crucial for their wider adoption. However, accurate characterization of AM components using NDT remains a challenge. To this end, our work focuses on characterizing the elastoplastic properties of AM components from instrumented indentation measurements, addressing the inverse indentation problem. Previous approaches to this problem have limitations in generalization or in estimating the variability of elastoplastic properties. In this work, we explore a stochastic inverse problem (SIP) formulation, estimating a distribution over elastoplastic properties (Young’s modulus, yield strength, and strain hardening exponent) that aligns with observed indentation data. Implementing this methodology for AM components subjected to different heat treatments, we achieve predictions of the strain hardening exponent (n), Young’s modulus (E), and yield strength (σ_y) to within 1.1%, 1%, and 5% of the actual values, respectively. The recovered distributions closely match those from standard tensile tests, indicating our methodology’s accuracy in characterizing mean elastoplastic properties and their variability through high throughput indentation measurements.