Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys

Romanov, Vyacheslav

doi:10.1007/s11837-022-05163-w

Cited by 2 publications

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“…Some of these issues (i.e., multi-modal stochastic response to stressors) can be addressed using ensemble modeling. 21 Remarkably, while successfully reinforcing the threshold rule over five convolutional cycles (initializing, three tempering cycles, and testing) CoFi accurately captured the threshold temperature for the onset of microstructural evolution during heat treatment (Appendix A). The thermal cycling model had maintained compatibility with the threshold rule preserving the virtual microstructure state at low temperatures and converged to the threshold temperature range of 74-113 • C, which is in line with metallurgical practice of using ∼125 • C as the lowest heat-treatment temperature for this type of metal alloys.…”

Section: Resultsmentioning

confidence: 87%

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Convoluted filtering for process cycle modeling

Romanov

2023

Engineering Reports

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Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process‐structure–property‐performance relationships hidden in materials data. The process‐structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process‐structure relations. In this work, a novel convoluted model‐filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe‐based alloy heat treatment cycles. The artifacts were pre‐trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

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Section: Resultsmentioning

confidence: 87%

“…Such data pattern suggests inherent heterogeneity that cannot be explained by evolution of the uniform (and deterministic) virtual microstructure state assumed in this case study. Some of these issues (i.e., multi‐modal stochastic response to stressors) can be addressed using ensemble modeling 21 …”

Section: Resultsmentioning

confidence: 99%