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Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm

Surface finish and temperature rise are the crucial machining outcomes since it determines the quality of the machining and the tool life. During machining operations, choosing optimal machining parameters is critical since it affects the machining outcome. In this work, Multi-Objective Genetic Algo...

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Main Authors: Mimi Muzlina, Mukri, Nor Atiqah, Zolpakar, Pathak, Sunil
Format: Article
Language:English
Published: Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. 2023
Subjects:
TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
Online Access:http://umpir.ump.edu.my/id/eprint/39350/1/Optimization%20of%20machining%20parameters%20in%20turning%20for%20different%20hardness%20using%20multi-objective%20genetic%20algorithm.pdf
http://umpir.ump.edu.my/id/eprint/39350/
https://doi.org/10.24191/jmeche.v20i3.23899
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spelling my.ump.umpir.393502023-11-22T03:28:45Z http://umpir.ump.edu.my/id/eprint/39350/ Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm Mimi Muzlina, Mukri Nor Atiqah, Zolpakar Pathak, Sunil TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Surface finish and temperature rise are the crucial machining outcomes since it determines the quality of the machining and the tool life. During machining operations, choosing optimal machining parameters is critical since it affects the machining outcome. In this work, Multi-Objective Genetic Algorithm (MOGA) optimization is used to find the combination of machining parameters at different levels of hardness of 20, 36, and 43 to obtain minimum surface roughness and minimum cutting temperature in turning operation. Cutting depth, cutting speed, and feed rate are the machining variables that are used in the process of optimization. From the results, it shows that the minimum temperature rise is 243.333 ℃ with a surface roughness of 1.975 µm during machining of 20 hardness. It also observed that the hardness of the material significantly affects the surface roughness and temperature rise. The outcome shows that as the hardness of the material is increasing the temperature is increasing while the surface roughness is decreasing. This research also revealed that using a MOGA to optimize multi-objective replies produces positive outcomes. Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. 2023 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39350/1/Optimization%20of%20machining%20parameters%20in%20turning%20for%20different%20hardness%20using%20multi-objective%20genetic%20algorithm.pdf Mimi Muzlina, Mukri and Nor Atiqah, Zolpakar and Pathak, Sunil (2023) Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm. Journal of Mechanical Engineering, 20 (3). pp. 25-48. ISSN 1823-5514. (Published) https://doi.org/10.24191/jmeche.v20i3.23899 10.24191/jmeche.v20i3.23899
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
spellingShingle TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
Mimi Muzlina, Mukri
Nor Atiqah, Zolpakar
Pathak, Sunil
Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm
description Surface finish and temperature rise are the crucial machining outcomes since it determines the quality of the machining and the tool life. During machining operations, choosing optimal machining parameters is critical since it affects the machining outcome. In this work, Multi-Objective Genetic Algorithm (MOGA) optimization is used to find the combination of machining parameters at different levels of hardness of 20, 36, and 43 to obtain minimum surface roughness and minimum cutting temperature in turning operation. Cutting depth, cutting speed, and feed rate are the machining variables that are used in the process of optimization. From the results, it shows that the minimum temperature rise is 243.333 ℃ with a surface roughness of 1.975 µm during machining of 20 hardness. It also observed that the hardness of the material significantly affects the surface roughness and temperature rise. The outcome shows that as the hardness of the material is increasing the temperature is increasing while the surface roughness is decreasing. This research also revealed that using a MOGA to optimize multi-objective replies produces positive outcomes.
format Article
author Mimi Muzlina, Mukri
Nor Atiqah, Zolpakar
Pathak, Sunil
author_facet Mimi Muzlina, Mukri
Nor Atiqah, Zolpakar
Pathak, Sunil
author_sort Mimi Muzlina, Mukri
title Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm
title_short Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm
title_full Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm
title_fullStr Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm
title_full_unstemmed Optimization of Machining Parameters in Turning for Different Hardness using Multi-Objective Genetic Algorithm
title_sort optimization of machining parameters in turning for different hardness using multi-objective genetic algorithm
publisher Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM), Malaysia.
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/39350/1/Optimization%20of%20machining%20parameters%20in%20turning%20for%20different%20hardness%20using%20multi-objective%20genetic%20algorithm.pdf
http://umpir.ump.edu.my/id/eprint/39350/
https://doi.org/10.24191/jmeche.v20i3.23899
_version_ 1822923861347270656
score 13.235362

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