Surface Roughness of Aluminum Alloy 7024 Predicted by Linear Regression and Neural Network Model in Abrasive Water Jet Machining

Authors

Baqer A. Ahmed, Department of Production Engineering and Metallurgy / University of Technology / IraqFollow
Mostafa Adel Abdullah, Department of Production Engineering and Metallurgy / University of Technology / IraqFollow
Safaa Kadhim Ghazi, Department of Production Engineering and Metallurgy / University of Technology / IraqFollow

Abstract

A prediction model using a linear regression model and Artificial Neural Network for the abrasive water jet machining of Aluminum-alloy 7024 was the main objective of this study. The abrasive water jet experiments were carried out based on the Taguchi Design. The influence of three independent variables such as pressure (200, 250, 300, and 350 MPa), feed rate (40, 60, 80, and 100 mm/min), and Gap or standoff distance (1, 2, 3, and 4 mm) as input and use Surface Roughness (Ra) were examined using analysis of variance (ANOVA) as output. The ANOVA response graphs show that pressure has the greatest impact on Ra as a function of feed rate and Gap. The regression model was sophisticated between the studied factors and the response. The confirmation tests show that the regression models are in well approval. The estimated determination of the coefficient value was (0.96). As a result, the maximum error between the obtained Artificial Neural Network (ANN) and experimental data is less than the regression model. The prediction model of ANN was found to be more accurate when compared with the regression model. The ANN results have a good acceptance between the predicted and experimental data, with a mean square error of training indices equal to (0.001).

Recommended Citation

Ahmed, Baqer A.; Abdullah, Mostafa Adel; and Ghazi, Safaa Kadhim (2024) "Surface Roughness of Aluminum Alloy 7024 Predicted by Linear Regression and Neural Network Model in Abrasive Water Jet Machining," Al-Bahir Journal for Engineering and Pure Sciences: Vol. 4: Iss. 2, Article 7.
Available at: https://doi.org/10.55810/2313-0083.1060

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