Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN)

This research focuses on prediction on tool wear and surface roughness of Waspaloy under different machining conditions by using artificial neural network (ANN). Cutting speed and feed rate were the input nodes while tool wear and surface roughness were the output nodes. ANN with various number of n...

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Main Author:	Gan, Chin Ket
Format:	Thesis
Language:	English English
Published:	2021
Subjects:	T Technology (General) TJ Mechanical engineering and machinery
Online Access:	http://eprints.utem.edu.my/id/eprint/26952/1/Prediction%20of%20tool%20wear%20and%20surface%20roughness%20of%20waspaloy%20by%20using%20Artificial%20Neural%20Network%20%28ANN%29.pdf http://eprints.utem.edu.my/id/eprint/26952/2/Prediction%20of%20tool%20wear%20and%20surface%20roughness%20of%20waspaloy%20by%20using%20Artificial%20Neural%20Network%20%28ANN%29.pdf
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spelling	my-utem-ep.269522023-09-14T14:42:53Z Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN) 2021 Gan, Chin Ket T Technology (General) TJ Mechanical engineering and machinery This research focuses on prediction on tool wear and surface roughness of Waspaloy under different machining conditions by using artificial neural network (ANN). Cutting speed and feed rate were the input nodes while tool wear and surface roughness were the output nodes. ANN with various number of neurons, namely 1, 5 and 10 in the hidden layer were created by using MATLAB R2021a. The machining performances between the optimal neural network structure (lowest mean squared error, MSE, mean absolute error, MAE, and mean absolute percentage error, MAPE) and the experimental result were compared. The predicted value by ANN agrees well with the experimental results for tool wear and surface roughness, except in 2-1-2 model. There are three predicted values of tool wear (7th, 8th, and 9th run) in MQL condition and one predicted value in surface roughness (6th run) in wet condition are far away from the experiment result. The error percentage generated is 30.00 %, 32.23 %, 22.78 %, and 22.32 %, respectively. 2-10-2 neural network has shown the lowest MSE, MAE, and MAPE and has been selected as the optimal model. By comparing the dry, wet, and MQL, the lowest tool wear (0.12 mm) located at 8th (MQL) and 9th (MQL) runs while the lowest surface roughness (0.22 μm) located at 9th (MQL) run. MQL is preferable to use in machining to decrease the tool wear and surface roughness especially in machining hard-to-machine materials. 2021 Thesis http://eprints.utem.edu.my/id/eprint/26952/ http://eprints.utem.edu.my/id/eprint/26952/1/Prediction%20of%20tool%20wear%20and%20surface%20roughness%20of%20waspaloy%20by%20using%20Artificial%20Neural%20Network%20%28ANN%29.pdf text en public http://eprints.utem.edu.my/id/eprint/26952/2/Prediction%20of%20tool%20wear%20and%20surface%20roughness%20of%20waspaloy%20by%20using%20Artificial%20Neural%20Network%20%28ANN%29.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=121717 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Manufacturing Engineering Liew, Pay Jun
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Liew, Pay Jun
topic	T Technology (General) TJ Mechanical engineering and machinery
spellingShingle	T Technology (General) TJ Mechanical engineering and machinery Gan, Chin Ket Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN)
description	This research focuses on prediction on tool wear and surface roughness of Waspaloy under different machining conditions by using artificial neural network (ANN). Cutting speed and feed rate were the input nodes while tool wear and surface roughness were the output nodes. ANN with various number of neurons, namely 1, 5 and 10 in the hidden layer were created by using MATLAB R2021a. The machining performances between the optimal neural network structure (lowest mean squared error, MSE, mean absolute error, MAE, and mean absolute percentage error, MAPE) and the experimental result were compared. The predicted value by ANN agrees well with the experimental results for tool wear and surface roughness, except in 2-1-2 model. There are three predicted values of tool wear (7th, 8th, and 9th run) in MQL condition and one predicted value in surface roughness (6th run) in wet condition are far away from the experiment result. The error percentage generated is 30.00 %, 32.23 %, 22.78 %, and 22.32 %, respectively. 2-10-2 neural network has shown the lowest MSE, MAE, and MAPE and has been selected as the optimal model. By comparing the dry, wet, and MQL, the lowest tool wear (0.12 mm) located at 8th (MQL) and 9th (MQL) runs while the lowest surface roughness (0.22 μm) located at 9th (MQL) run. MQL is preferable to use in machining to decrease the tool wear and surface roughness especially in machining hard-to-machine materials.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Gan, Chin Ket
author_facet	Gan, Chin Ket
author_sort	Gan, Chin Ket
title	Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN)
title_short	Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN)
title_full	Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN)
title_fullStr	Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN)
title_full_unstemmed	Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN)
title_sort	prediction of tool wear and surface roughness of waspaloy by using artificial neural network (ann)
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Manufacturing Engineering
publishDate	2021
url	http://eprints.utem.edu.my/id/eprint/26952/1/Prediction%20of%20tool%20wear%20and%20surface%20roughness%20of%20waspaloy%20by%20using%20Artificial%20Neural%20Network%20%28ANN%29.pdf http://eprints.utem.edu.my/id/eprint/26952/2/Prediction%20of%20tool%20wear%20and%20surface%20roughness%20of%20waspaloy%20by%20using%20Artificial%20Neural%20Network%20%28ANN%29.pdf
_version_	1783728757934129152

Prediction of tool wear and surface roughness of waspaloy by using Artificial Neural Network (ANN)

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