Prediction of Mechanical Properties of the 2024 Aluminum Alloy by Using Machine Learning Methods

Özkavak, Hatice Varol; İnce, Murat; Bıçaklı, Ezgi Eylem

doi:10.1007/s13369-022-07009-8

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…For example, Younis et al built an optimized neural network model to predict the fatigue crack propagation rate of aircraft aluminum alloys [1]. Varol Özkavak et al predicted the hardness and bending strength of 2024 aluminum alloy [2]. Yong-fei et al predicted the ultimate tensile strength and elongation of aviation aluminum alloys [3], and there are many researchers who predicted different properties of aluminum alloys.…”

Section: Comments (1): the Claim 'The Second Model Can Be Used As A M...mentioning

confidence: 99%

Reply to comment on ‘Composition-based aluminum alloy selection using an artificial neural network’

Fatriansyah,

Rizqillah,

Suhariadi

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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This reply is addressed to comments on our paper entitled ‘Composition-based Aluminum Alloy Selection Using an Artificial Neural Network.’ There are six main comments, and we addressed the comments carefully. This machine learning (ML) modeling is only part of the development of a broader material selection (or material screening) system. Consideration of other material properties can certainly be included through the integration of ML systems.

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Section: Comments (1): the Claim 'The Second Model Can Be Used As A M...mentioning

confidence: 99%

Reply to comment on ‘Composition-based aluminum alloy selection using an artificial neural network’

Fatriansyah,

Rizqillah,

Suhariadi

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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show abstract

“…Among the aluminum alloys, the 2000 series alloys, particularly the 2024 alloy, have high strength and excellent fatigue resistance, making them attractive for aerospace and automotive applications. However, the optimization of these properties requires the development of new alloy designs [1][2][3][4] . The aluminum alloy AA 2024 is valued for its mechanical strength characteristics, with the primary hardening mechanism attributed to the precipitation of Al 2 Cu and Al 2 CuMg.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Chemical Composition on the Oxidation Resistance of 2000 Series Aluminum Alloys using Extreme Vertices Design

Antunes,

Wainer,

Salvo

2024

Mat. Res.

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This research investigates optimizing the properties of 2024 aluminum alloy using the Extreme Vertices Design (EVD) method and linear regression. It examines the oxidation behavior of the alloy during solubilization heat treatment, specifically focusing on the effect of magnesium addition leading to a dark oxide layer. The study employs a comprehensive experimental design and regression models to estimate the specific oxidation rate constant (k). Analysis of results reveals variations in oxidation behavior among alloys and the influence of aluminum, copper, and magnesium concentrations on the oxidation rate. The regression analysis yields a comprehensive equation: k = -0.01Al -4.06Cu -15.71Mg + 4.52Al•Cu + 17.01Al•Mg + 418.5Cu:Mg -447.7Al•Cu•Mg, with statistically significant results (p < 0.05) for all terms. An increase in magnesium concentration was found to enhance the oxidation rate, implying a higher alloy susceptibility to oxidation. These findings underline the value of the EVD method and regression analysis in alloy property optimization, thus aiding in the design of aluminum alloys with improved oxidation resistance.

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Machine Learning Prediction of Aluminum Alloy Stress–Strain Curves at Variable Temperatures with Failure Analysis

Dorbane,

Harrou,

Anghel

et al. 2023

J Fail. Anal. and Preven.

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Prediction of Mechanical Properties of the 2024 Aluminum Alloy by Using Machine Learning Methods

Cited by 5 publications

References 31 publications

Reply to comment on ‘Composition-based aluminum alloy selection using an artificial neural network’

Reply to comment on ‘Composition-based aluminum alloy selection using an artificial neural network’

Exploring the Impact of Chemical Composition on the Oxidation Resistance of 2000 Series Aluminum Alloys using Extreme Vertices Design

Machine Learning Prediction of Aluminum Alloy Stress–Strain Curves at Variable Temperatures with Failure Analysis

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