Analysis and prediction of the mechanical properties of cold forged Al-Li preforms using statistical and machine learning approaches

Lingam, Divnesh and Ananthanarayanan, Rajeshkannan and Jeevanantham, A.K. (2025) Analysis and prediction of the mechanical properties of cold forged Al-Li preforms using statistical and machine learning approaches. Engineering Research Express, 7 (3). NA. ISSN 2631-8695

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Abstract

This study investigates the mechanical properties of aluminum-lithium (Al-Li) preforms via statistical and machine learning (ML) methods, with a focus on optimizing processing parameters to achieve enhanced strength and conducts a comparative analysis between the best performing ML approach. Al-Li alloys are recognized for their high strength-to-weight ratio, making them highly suitable for aerospace applications where weight reduction is a critical design consideration. However, the challenge lies in refining processing conditions to enhance mechanical performance while maintaining manufacturability due to the inherent anisotropic nature of Al-Li alloys. The study employs response surface methodology (RSM) to analyze the effects of lithium content (0–2 wt%), aspect ratio (0.2–0.6), relative density (82%–86%), and lubrication conditions (none versus zinc stearate) on key mechanical properties. The results indicate that an intermediate lithium content of 1 wt% optimizes mechanical strength, including yield strength, tensile strength, and elastic modulus, whereas pure aluminum (0 wt% lithium) exhibits superior ductility and workability. Furthermore, higher aspect ratios (0.6) contribute to increased strength, while lower aspect ratios (0.2) enhance strain hardening and densification. The study also reveals that forging without lubrication enhances densification and mechanical strength due to increased internal stresses. Predictive models are also developed using machine learning approach whereby artificial neural networks (ANN) demonstrates superior predictive accuracy, achieving up to 92.97% accuracy for fractional theoretical density, owing to its capacity to capture complex nonlinear relationships. However, ANN showed susceptibility to overfitting, particularly when predicting ductility, where its performance on unseen data is less reliable. Conversely, linear regression, while a simpler approach, showed greater stability by mitigating overfitting risks and providing a robust baseline for evaluating linear dependencies. The integration of these methodologies offers a comprehensive framework for optimizing cold forging conditions, thereby contributing to the advancement of high-performance Al–Li alloy manufacturing.

Item Type:	Journal Article
Subjects:	T Technology > TJ Mechanical engineering and machinery
Divisions:	School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User:	Rajeshkannan Ananthanarayanan
Date Deposited:	14 Apr 2026 02:07
Last Modified:	14 Apr 2026 02:07
URI:	https://repository.usp.ac.fj/id/eprint/15331

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