CHARACTERIZATION OF ELASTOPLASTIC PROPERTIES OF ADDITIVELY MANUFACTURED SPECIMENS FROM INDENTATION DATA USING STOCHASTIC INVERSE MODELING

Published

June 17, 2024

Author(s)

Ridwan Olabiyi, Jordan Weaver, Ashif Sikandar Iquebal

Abstract

Rapid characterization of the mechanical properties and material structure of additively manufactured components via nondestructive techniques is becoming the sine-qua-non for their wider adoption. In this research, we primarily focus on estimating the elastoplastic properties of AM components from instrumented indentation measurements, often referred as the inverse indentation problem. While several empirical correlations and machine learning models have been developed for the inverse indentation problem, they either generalize poorly beyond the training datasets or do not provide a framework to accurately estimate the variability in material properties. In this work, we focus on alternate formulation of the inverse indentation problem, referred to as the stochastic inverse problem (SIP). In particular, SIP aims to estimate a distribution over the elastoplastic properties which when propagated through a forward model matches the observed force-displacement, i.e., the indentation data distribution. We implement our methodology for characterizing additive manufactured components that are subject to different heat treatments. The results demonstrate that our methodology accurately predicts the average elastoplastic characteristics, the Young's modulus (E) to within 1% and yield strength () to within 5% of the actual average values. Additionally, the recovered property distributions closely match those from standard tensile tests, as evidenced by KL divergence values approaching zero for the respective distributions. Based on the results, we are not only able to accurately estimate the mean elastoplastic properties, but also the associated variability. This allows us to distinguish the elastoplastic properties of the two groups from high throughput indentation measurements.

Proceedings Title

Proceedings of the ASME 2024 Manufacturing Science and Engineering Conference MSEC2024

Conference Dates

July 17-21, 2024

Conference Location

Knoxville, TN, US

Conference Title

MSEC 2024 Manufacturing Science and Engineering Conference

Pub Type

Conferences

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Citation

Olabiyi, R. , Weaver, J. and Iquebal, A. (2024), CHARACTERIZATION OF ELASTOPLASTIC PROPERTIES OF ADDITIVELY MANUFACTURED SPECIMENS FROM INDENTATION DATA USING STOCHASTIC INVERSE MODELING, Proceedings of the ASME 2024 Manufacturing Science and Engineering Conference MSEC2024, Knoxville, TN, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957004 (Accessed November 23, 2024)