Data-driven machine learning approach for predicting dwell fatigue life in two classes of Titanium alloys

Rahman, SA and Chandraker, A and Prakash, O and Chauhan, A (2024) Data-driven machine learning approach for predicting dwell fatigue life in two classes of Titanium alloys. In: Engineering Fracture Mechanics, 306 .

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Abstract

Predicting dwell fatigue life accurately in Titanium (Ti) alloys is vital for designing reliable components such as aero-engine discs and blades. This study proposes a data-driven machine learning (ML) approach for precisely predicting dwell fatigue life in two classes of Ti alloys: near α and α + β alloys. To build a robust model, the data-driven approach tackles this multi-dimensional problem by training a comprehensive dataset of various input features, including alloy composition, monotonic properties, and fatigue testing parameters. Advanced ensemble-based algorithms, Gradient Boosting (GBOOST) and Extreme Gradient Boosting (XGBOOST) are employed. The split-insensitive best-performing XGBOOST model achieved a high accuracy (R2) score of 89.9 , indicating impressive prediction capability. The model's robustness is further assessed using an unseen validation dataset to draw qualitative and quantitative conclusions. ML model's validation further involved comparing its predictions with actual experimental trends across relationships, such as dwell life versus dwell time and dwell-debit versus dwell time. Dwell-debit is also predicted as a function of varying stresses normalized to 0.2 yield strength, highlighting the susceptibility of investigated Ti alloys to dwell fatigue. Additionally, the SHapley Additive exPlanations (SHAP) analysis was conducted to gain deeper insights into various input features affecting dwell fatigue life. © 2024 Elsevier Ltd

Item Type:	Journal Article
Publication:	Engineering Fracture Mechanics
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to Elsevier Ltd.
Keywords:	Adaptive boosting; Aircraft engines; Fatigue testing; Forecasting; Statistical tests; Titanium alloys, Boosting algorithm; Data driven; Dwell fatigue; Dwell time; Gradient boosting; Input features; Machine learning approaches; Machine-learning; Reliable components; Titanium (alloys), Machine learning
Department/Centre:	Division of Mechanical Sciences > Materials Engineering (formerly Metallurgy)
Date Deposited:	31 Jul 2024 05:11
Last Modified:	31 Jul 2024 05:11
URI:	http://eprints.iisc.ac.in/id/eprint/85695

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