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Fusion: Practice and Applications
Volume 15 , Issue 2, PP: 165-172 , 2024 | Cite this article as | XML | Html |PDF

Title

Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining

  Nurezayana Zainal 1 * ,   Azlan Mohd Zain 2 ,   Mohamad Firdaus A. Aziz 3 ,   Salama A. Mostafa 4 ,   Ashanira Mat Deris 5 ,   Nor B. Abd Warif 6 ,   Muhammad Ammar S. Shahrom 7

1  Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
    (nurezayana@uthm.edu.my)

2  School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
    (azlanmz@utm.my)

3  Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
    (mdfirdaus@uthm.edu.my)

4  Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
    (salama@uthm.edu.my)

5  Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
    (ashanira@umt.edu.my)

6  Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
    (norbakiah@uthm.edu.my)

7  Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia
    (hi210017@student.uthm.edu.my)

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Doi   :   https://doi.org/10.54216/FPA.150215

Received: August 27, 2023 Revised: December 15, 2023 Accepted: April 09, 2024

Abstract :

This study investigated the experimental work of titanium alloy in the die-sinking electrical discharge (EDM) machining process to enhance surface integrity (surface roughness) by applying regression-based modeling. Furthermore, a multiple polynomial regression (MPR) model was developed to predict surface roughness responses under optimized conditions. The effects of EDM parameters, such as pulse-on time (ON), pulse-off time (OFF), peak current (IP), and servo voltage (SV), on surface roughness were studied. The experiment was conducted using a two-level full factorial design with four center points. Roughness was measured using a surface roughness tester (Formtracer SJ-301). The significant cutting parameters for surface roughness were determined using analysis of variance (ANOVA). The results showed that increasing the servo voltage significantly reduced the surface roughness by 46.48%. The developed model also predicted surface roughness values lower than those observed in the experimental data, with an R2 value of 0.608.

Keywords :

Regression; Polynomial Regression; Surface roughness; Electrical discharge machining; titanium alloy.

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Cite this Article as :
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MLA Nurezayana Zainal , Azlan Mohd Zain, Mohamad Firdaus A. Aziz , Salama A. Mostafa , Ashanira Mat Deris, Nor B. Abd Warif, Muhammad Ammar S. Shahrom. "Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining." Fusion: Practice and Applications, Vol. 15, No. 2, 2024 ,PP. 165-172 (Doi   :  https://doi.org/10.54216/FPA.150215)
APA Nurezayana Zainal , Azlan Mohd Zain, Mohamad Firdaus A. Aziz , Salama A. Mostafa , Ashanira Mat Deris, Nor B. Abd Warif, Muhammad Ammar S. Shahrom. (2024). Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining. Journal of Fusion: Practice and Applications, 15 ( 2 ), 165-172 (Doi   :  https://doi.org/10.54216/FPA.150215)
Chicago Nurezayana Zainal , Azlan Mohd Zain, Mohamad Firdaus A. Aziz , Salama A. Mostafa , Ashanira Mat Deris, Nor B. Abd Warif, Muhammad Ammar S. Shahrom. "Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining." Journal of Fusion: Practice and Applications, 15 no. 2 (2024): 165-172 (Doi   :  https://doi.org/10.54216/FPA.150215)
Harvard Nurezayana Zainal , Azlan Mohd Zain, Mohamad Firdaus A. Aziz , Salama A. Mostafa , Ashanira Mat Deris, Nor B. Abd Warif, Muhammad Ammar S. Shahrom. (2024). Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining. Journal of Fusion: Practice and Applications, 15 ( 2 ), 165-172 (Doi   :  https://doi.org/10.54216/FPA.150215)
Vancouver Nurezayana Zainal , Azlan Mohd Zain, Mohamad Firdaus A. Aziz , Salama A. Mostafa , Ashanira Mat Deris, Nor B. Abd Warif, Muhammad Ammar S. Shahrom. Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining. Journal of Fusion: Practice and Applications, (2024); 15 ( 2 ): 165-172 (Doi   :  https://doi.org/10.54216/FPA.150215)
IEEE Nurezayana Zainal, Azlan Mohd Zain, Mohamad Firdaus A. Aziz, Salama A. Mostafa, Ashanira Mat Deris, Nor B. Abd Warif, Muhammad Ammar S. Shahrom, Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining, Journal of Fusion: Practice and Applications, Vol. 15 , No. 2 , (2024) : 165-172 (Doi   :  https://doi.org/10.54216/FPA.150215)
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