A General Probabilistic Framework Combining Experiments and Simulations to Identify the Small Crack Driving Force

Abstract : Identifying the small crack (SC) driving force of polycrystalline engineering alloys is critical to correlate the inherent microstructure variability and the intrinsic SC growth rate's scatter observed during stage I. Due to recent experimental advancements, “cycle-by-cycle” data of a SC propagating through a beta-metastable titanium alloy are available via phase and diffraction contrast tomography. To compute micromechanical fields not available from the experiment, FFT-based crystal plasticity simulations are utilized. Results of the experiment and simulations are combined in a single dataset and sampled utilizing non-local mining technique. Sampled data are analyzed into a machine learning Bayesian Network framework to identify statically relevant correlations between state variables, microstructure features, location of the crack front, and experimentally observed growth rate, in order to postulate a data-driven, non-parametric SC driving force. Results are presented with a particular focus on the correlation between well-established SC driving forces and the proposed new driving force metric.
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https://hal.archives-ouvertes.fr/hal-01580015
Contributor : Yoann Guilhem <>
Submitted on : Friday, September 1, 2017 - 12:45:01 AM
Last modification on : Thursday, June 6, 2019 - 5:24:02 PM

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  • HAL Id : hal-01580015, version 1

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Andrea Rovinelli, Michael D. Sangid, Ricardo A. Lebensohn, Wolfgang Ludwig, Yoann Guilhem, et al.. A General Probabilistic Framework Combining Experiments and Simulations to Identify the Small Crack Driving Force. TMS 2017 Conference, Feb 2017, San Diego, United States. ⟨hal-01580015⟩

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