Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining
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...
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2024
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| Online Access: | http://eprints.uthm.edu.my/12557/1/J18079_1529767575b513c8f8890c870bc26797.pdf http://eprints.uthm.edu.my/12557/ https://doi.org/10.54216/FPA.150215 |
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| author | Zaina, Nurezayana Mohd Zain, Azlan A. Aziz, Mohamad Firdaus A. Mostafa, Salama Mat Deris, Ashanira Abd Warif, Nor S. Shahrom, Muhammad Ammar |
| author_facet | Zaina, Nurezayana Mohd Zain, Azlan A. Aziz, Mohamad Firdaus A. Mostafa, Salama Mat Deris, Ashanira Abd Warif, Nor S. Shahrom, Muhammad Ammar |
| author_sort | Zaina, Nurezayana |
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| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.uthm.eprints-12557 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | aspg |
| record_format | eprints |
| spelling | my.uthm.eprints-125572025-03-19T00:23:56Z http://eprints.uthm.edu.my/12557/ Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining Zaina, Nurezayana Mohd Zain, Azlan A. Aziz, Mohamad Firdaus A. Mostafa, Salama Mat Deris, Ashanira Abd Warif, Nor S. Shahrom, Muhammad Ammar TJ Mechanical engineering and machinery 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. aspg 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12557/1/J18079_1529767575b513c8f8890c870bc26797.pdf Zaina, Nurezayana and Mohd Zain, Azlan and A. Aziz, Mohamad Firdaus and A. Mostafa, Salama and Mat Deris, Ashanira and Abd Warif, Nor and S. Shahrom, Muhammad Ammar (2024) Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining. Fusion: Practice and Applications, 15 (2). pp. 165-172. https://doi.org/10.54216/FPA.150215 |
| spellingShingle | TJ Mechanical engineering and machinery Zaina, Nurezayana Mohd Zain, Azlan A. Aziz, Mohamad Firdaus A. Mostafa, Salama Mat Deris, Ashanira Abd Warif, Nor S. Shahrom, Muhammad Ammar Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining |
| title | Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining |
| title_full | Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining |
| title_fullStr | Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining |
| title_full_unstemmed | Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining |
| title_short | Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining |
| title_sort | multiple polynomial regression model for predicting surface roughness of titanium alloy in electrical discharge machining |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.uthm.edu.my/12557/1/J18079_1529767575b513c8f8890c870bc26797.pdf http://eprints.uthm.edu.my/12557/ https://doi.org/10.54216/FPA.150215 |
| url_provider | http://eprints.uthm.edu.my/ |