Predicting crack nucleation in commercially pure titanium using orientation imaging microscopy and machine learning

Jain, JV and Barnwal, VK and Kumar Saxena, A and Nair, PB and Yazar, KU and Suwas, S (2025) Predicting crack nucleation in commercially pure titanium using orientation imaging microscopy and machine learning. In: Materials Letters, 379 .

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Abstract

Due to the prohibitively long experimental and simulation times, dwell fatigue (DF) failure prediction in titanium and its alloys is a challenging task. Since most of these failures have a microstructural level origin, this work focusses on utilizing minimal experiments and machine learning for predicting failure initiation points in a given microstructure. Failure initiation points in commercially pure titanium were identified using interrupted tensile and DF tests. Orientation imaging data was used to train a Random Forest model to calculate the relative importance of various grain orientation-based features to crack nucleation. Subsequently a predictive model for identifying locations that are likely to form a DF crack in a microstructure is developed. © 2024 Elsevier B.V.

Item Type:	Journal Article
Publication:	Materials Letters
Publisher:	Elsevier B.V.
Additional Information:	The copyright for this article belongs to the publishers.
Keywords:	Titanium alloys, Commercially pure titanium; Cracks nucleation; Dwell fatigue; Failure initiation; Fatigue failures; Imaging machines; Machine-learning; Orientation imaging microscopy; Simulation time; Titania, Fatigue crack
Department/Centre:	Division of Mechanical Sciences > Materials Engineering (formerly Metallurgy)
Date Deposited:	29 Nov 2024 09:18
Last Modified:	29 Nov 2024 09:18
URI:	http://eprints.iisc.ac.in/id/eprint/86872

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