TY - GEN
T1 - CHARACTERIZATION OF ELASTOPLASTIC PROPERTIES OF ADDITIVELY MANUFACTURED SPECIMENS FROM INDENTATION DATA USING STOCHASTIC INVERSE MODELING
AU - Olabiyi, Ridwan
AU - Weaver, Jordan
AU - Iquebal, Ashif
N1 - Publisher Copyright:
© 2024 by National Institute of Standards and Technology (NIST).
PY - 2024
Y1 - 2024
N2 - 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 (σy) 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.
AB - 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 (σy) 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.
KW - Additive manufacturing
KW - Inverse problem
KW - Non-destructive testing
KW - Rapid characterization
UR - http://www.scopus.com/inward/record.url?scp=85203720553&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85203720553&partnerID=8YFLogxK
U2 - 10.1115/MSEC2024-125452
DO - 10.1115/MSEC2024-125452
M3 - Conference contribution
AN - SCOPUS:85203720553
T3 - Proceedings of ASME 2024 19th International Manufacturing Science and Engineering Conference, MSEC 2024
BT - Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2024 19th International Manufacturing Science and Engineering Conference, MSEC 2024
Y2 - 17 June 2024 through 21 June 2024
ER -