Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models
Tool life of the cutting tools is considered as one of the factors which has effects on machining costs and the quality of machined parts. The topic of tool life prediction has been an interesting and important research topic attracting the attention of a wide number of researchers in this particula...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universiti Kebangsaan Malaysia
2013
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| Online Access: | http://journalarticle.ukm.my/6683/1/07_Salah_Al-Zubaidi.pdf |
| _version_ | 1796930354497454080 |
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| author | Salah al-Zubaidi, Jaharah A. Ghani, Che Hassan Che Haron, |
| author_facet | Salah al-Zubaidi, Jaharah A. Ghani, Che Hassan Che Haron, |
| author_sort | Salah al-Zubaidi, |
| collection | UKM |
| description | Tool life of the cutting tools is considered as one of the factors which has effects on machining costs and the quality of machined parts. The topic of tool life prediction has been an interesting and important research topic attracting the attention of a wide number of researchers in this particular area. In terms of the suitable methods used in this research topic, it is stated that both statistical and artificial intelligence (AI) approaches can be employed to model tool life. For further justifying the capability of the ANN model in predicting tool life, the current study was based on conducting experimental work for collecting the experimental data. After carrying out the experiment, 17 data sets were collected and they were divided into two subsets; the first one for training and the second for testing. Since the data sets seemed to be lower than the number of data sets used in previous studies, we attempted to make verification of the ability of the ANN model in learning and adapting with low training and testing data. Diverse topologies accompanied with single and two hidden layers were created for modeling the tool life. For choosing the best and most effective network, the study adopted the mean square error function as criteria for the evaluation of the network selection. Thus, based on the data generated from the same experiment, a regression model (RM) was constructed employing the SPSS software. A comparison between the ANN model and RMs in terms of their accuracy was carried out and the findings revealed that the accuracy of the ANN was higher than that of the RM. |
| first_indexed | 2024-03-06T04:01:49Z |
| format | Article |
| id | ukm.eprints-6683 |
| institution | Universiti Kebangsaan Malaysia |
| language | English |
| last_indexed | 2024-03-06T04:01:49Z |
| publishDate | 2013 |
| publisher | Universiti Kebangsaan Malaysia |
| record_format | dspace |
| spelling | ukm.eprints-66832016-12-14T06:41:54Z http://journalarticle.ukm.my/6683/ Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models Salah al-Zubaidi, Jaharah A. Ghani, Che Hassan Che Haron, Tool life of the cutting tools is considered as one of the factors which has effects on machining costs and the quality of machined parts. The topic of tool life prediction has been an interesting and important research topic attracting the attention of a wide number of researchers in this particular area. In terms of the suitable methods used in this research topic, it is stated that both statistical and artificial intelligence (AI) approaches can be employed to model tool life. For further justifying the capability of the ANN model in predicting tool life, the current study was based on conducting experimental work for collecting the experimental data. After carrying out the experiment, 17 data sets were collected and they were divided into two subsets; the first one for training and the second for testing. Since the data sets seemed to be lower than the number of data sets used in previous studies, we attempted to make verification of the ability of the ANN model in learning and adapting with low training and testing data. Diverse topologies accompanied with single and two hidden layers were created for modeling the tool life. For choosing the best and most effective network, the study adopted the mean square error function as criteria for the evaluation of the network selection. Thus, based on the data generated from the same experiment, a regression model (RM) was constructed employing the SPSS software. A comparison between the ANN model and RMs in terms of their accuracy was carried out and the findings revealed that the accuracy of the ANN was higher than that of the RM. Universiti Kebangsaan Malaysia 2013-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/6683/1/07_Salah_Al-Zubaidi.pdf Salah al-Zubaidi, and Jaharah A. Ghani, and Che Hassan Che Haron, (2013) Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models. Sains Malaysiana, 42 (12). pp. 1735-1741. ISSN 0126-6039 http://www.ukm.my/jsm/ |
| spellingShingle | Salah al-Zubaidi, Jaharah A. Ghani, Che Hassan Che Haron, Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models |
| title | Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models |
| title_full | Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models |
| title_fullStr | Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models |
| title_full_unstemmed | Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models |
| title_short | Prediction of tool life in end milling of Ti-6Al-4V alloy using artificial neural network and multiple regression models |
| title_sort | prediction of tool life in end milling of ti 6al 4v alloy using artificial neural network and multiple regression models |
| url | http://journalarticle.ukm.my/6683/1/07_Salah_Al-Zubaidi.pdf |
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