Abstract
Cleanliness is a major concern for steel manufacturers. Therefore, they constantly strive to modify and reduce non-metallic inclusions in the final product. Performance and quality of final steel sheet is strongly influenced by composition, morphology, type, size and distribution of inclusions in steel sheet. The aim of current work is to critically evaluate the versatility of a new data science enabled approach for establishing objective, high fidelity, structure-property correlations that are needed to facilitate optimal design of the processing path to realize enhanced performance of the final product.
A 2-D finite element based micro-mechanical model was developed to simulate, the effect of various spatial configurations and geometries of hard and soft inclusions in a steel matrix system, on the final properties of processed sheet. From each microscale simulation macroscale parameters such as yield strength, effective hardening rate, localization propensity, and plasticity index, were extracted. A large number of microstructures were evaluated using the micro-mechanical model. A reduced-order representation was extracted for the selected ensemble of microstructures using principal components of their 2-point statistics. These objective measures of the microstructure were then linked with the macroscale parameters listed above using regression methods. The extracted structure-property correlations are presented on this paper.
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© 2013 TMS (The Minerals, Metals & Materials Society)
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Gupta, A., Cecen, A., Goyal, S., Singh, A.K., Kalidindi, S.R. (2013). Multiscale Model for Non-Metallic Inclusions/Steel Composite System Using Data Science Enabled Structure-Property Linkages. In: Li, M., Campbell, C., Thornton, K., Holm, E., Gumbsch, P. (eds) Proceedings of the 2nd World Congress on Integrated Computational Materials Engineering (ICME). Springer, Cham. https://doi.org/10.1007/978-3-319-48194-4_9
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DOI: https://doi.org/10.1007/978-3-319-48194-4_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48585-0
Online ISBN: 978-3-319-48194-4
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