Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness
Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase...
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Online Access: | http://eprints.utm.my/id/eprint/84775/1/SafianSharif2019_OptimumPerformanceofGreenMachiningonThin.pdf http://eprints.utm.my/id/eprint/84775/ https://dx.doi.org/10.11113/jt.v81.13443 |
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my.utm.847752020-02-27T04:58:37Z http://eprints.utm.my/id/eprint/84775/ Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness Yanis, Muhammad Mohruni, Amrifan Saladin Sharif, Safian Yani, Irsyadi TJ Mechanical engineering and machinery Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 μm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC. Penerbit UTM Press 2019 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/84775/1/SafianSharif2019_OptimumPerformanceofGreenMachiningonThin.pdf Yanis, Muhammad and Mohruni, Amrifan Saladin and Sharif, Safian and Yani, Irsyadi (2019) Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness. Jurnal Teknologi, 81 (6). pp. 51-60. ISSN 2180-3722 https://dx.doi.org/10.11113/jt.v81.13443 DOI:10.11113/jt.v81.13443 |
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TJ Mechanical engineering and machinery Yanis, Muhammad Mohruni, Amrifan Saladin Sharif, Safian Yani, Irsyadi Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness |
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Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 μm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC. |
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Article |
author |
Yanis, Muhammad Mohruni, Amrifan Saladin Sharif, Safian Yani, Irsyadi |
author_facet |
Yanis, Muhammad Mohruni, Amrifan Saladin Sharif, Safian Yani, Irsyadi |
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Yanis, Muhammad |
title |
Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness |
title_short |
Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness |
title_full |
Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness |
title_fullStr |
Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness |
title_full_unstemmed |
Optimum performance of green machining on thin walled Ti6AL4V using RSM and ANN in terms of cutting force and surface roughness |
title_sort |
optimum performance of green machining on thin walled ti6al4v using rsm and ann in terms of cutting force and surface roughness |
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Penerbit UTM Press |
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2019 |
url |
http://eprints.utm.my/id/eprint/84775/1/SafianSharif2019_OptimumPerformanceofGreenMachiningonThin.pdf http://eprints.utm.my/id/eprint/84775/ https://dx.doi.org/10.11113/jt.v81.13443 |
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