Analysis and prediction of the mechanical properties of cold rolled Al-Li preforms using statistical and artificial neural network models

Lingam, Divnesh and Ananthanarayanan, Rajeshkannan and Jeevanantham, A.K. (2025) Analysis and prediction of the mechanical properties of cold rolled Al-Li preforms using statistical and artificial neural network models. Engineering Research Express, 7 . pp. 1-24. ISSN 2631-8695

Text - Published Version Restricted to Repository staff only Download (2MB) | Request a copy

Abstract

In this study, aluminum-lithium (Al-Li) alloys with x wt% lithium (x =0, 1 and 2) were synthesized using powder metallurgy and cold rolling, with response surface methodology (RSM) and artificial neural networks (ANN) employed for predictive modelling comparative analysis. Al-Li alloys are known for their high strength-to-weight ratio, making them suitable for aerospace applications. Yield
strength, ultimate tensile strength (UTS), ductility, and fractional theoretical density (FTD) were analyzed based on lithium content, sintering time (60, 90, and 120 min), and rolling passes (2, 4, and 6). Microstructural analysis was performed using SEM, XRD, and optical microscopy. RSM followed the I-optimal design approach, developing empirical models through variance analysis. Findings indicated optimal sintering times of 90 min for improved ductility and FTD, and 120 min for enhanced yield strength and UTS. Cold rolling induced strain hardening, with UTS peaking at 141.170 MPa, while yield strength initially increased but declined after two roll passes due to over-straining.
The ANN model, utilizing a 3-20-4 topology, exhibited strong predictive performance, achieving a determination coefficient (R2) of 0.99628. Both RSM and ANN successfully predicted Al–Li alloy properties within the investigation range. However, overfitting concerns, particularly with unseen data, affected ANN’s accuracy in predicting ductility. Overall, ANN outperformed RSMin predictive
accuracy, especially for nonlinear behavior, making it a more effective tool for modelling mechanical properties of Al-Li alloys.

Item Type:	Journal Article
Uncontrolled Keywords:	al-li, modelling, mechanical properties, rolling, ann, rsm
Subjects:	T Technology > TJ Mechanical engineering and machinery
Divisions:	School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User:	Rajeshkannan Ananthanarayanan
Date Deposited:	07 Aug 2025 02:41
Last Modified:	07 Aug 2025 02:41
URI:	https://repository.usp.ac.fj/id/eprint/15057

Actions (login required)

View Item