A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel
M. Vittorietti (Material Innovation Institute (M2i), Università degli Studi di Palermo, TU Delft - Statistics)
J. Hidalgo (Universidad de Castilla-La Mancha)
Jesús Galán-López (TU Delft - Team Erik Offerman)
Jilt Sietsma (TU Delft - Team Kevin Rossi)
G. Jongbloed (TU Delft - Delft Institute of Applied Mathematics)
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Abstract
This study proposes a new approach to determine phenomenological or physical relations between microstructure features and the mechanical behavior of metals bridging advanced statistics and materials science in a study of the effect of hard precipitates on the hardening of metal alloys. Synthetic microstructures were created using multi-level Voronoi diagrams in order to control microstructure variability and then were used as samples for virtual tensile tests in a full-field crystal plasticity solver. A data-driven model based on Functional Principal Component Analysis (FPCA) was confronted with the classical Voce law for the description of uniaxial tensile curves of synthetic AISI 420 steel microstructures consisting of a ferritic matrix and increasing volume fractions of M23C6 carbides. The parameters of the two models were interpreted in terms of carbide volume fractions and texture using linear mixed-effects models.
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