CNC Cutting Tools` Life Prediction Using Data Mining Approach
The failure of CNC machine tools has always been a negative impact on the manufacturing environment. The consequences of the failure will influence the production control, which further increases the duration of unplanned maintenance. To avoid such situations, it is required to predict the tools’...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2022
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1611/ http://eprints.intimal.edu.my/1611/1/joit2022_11r.pdf http://eprints.intimal.edu.my/1611/2/296 |
| _version_ | 1848766785619230720 |
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| author | Chan, Choon Kit Wong, Marven, Zhen Siang |
| author_facet | Chan, Choon Kit Wong, Marven, Zhen Siang |
| author_sort | Chan, Choon Kit |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | The failure of CNC machine tools has always been a negative impact on the
manufacturing environment. The consequences of the failure will influence the production
control, which further increases the duration of unplanned maintenance. To avoid such
situations, it is required to predict the tools’ behaviours based on the raw data collected from
machines. Hence, the objective of this paper is to obtain the machine tool life using the
machining parameters including cutting speed, feed rate, and depth of cut which may affect the
tool life in the prediction. All the data is collected by using different types of machine tools
material against different types of workpieces. In this paper, classification is chosen to be the
data mining approach with two algorithms to build the model for prediction, which are linear
regression and multilayer perceptron. The data collected was being split into training and
testing data. There are 40% of the data used for training data to build the predictive models
while 60% of the data collected is used as testing data. The result of predicted tool life is then
validated with the Taylor’s Extended Tool Life equation according to the ISO standard 3685
and ISO 8688-2. The results show that our proposed method is on par with the tool life
predicted by Taylor’s method. |
| first_indexed | 2025-11-14T11:56:40Z |
| format | Article |
| id | intimal-1611 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:56:40Z |
| publishDate | 2022 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-16112025-07-23T03:33:35Z http://eprints.intimal.edu.my/1611/ CNC Cutting Tools` Life Prediction Using Data Mining Approach Chan, Choon Kit Wong, Marven, Zhen Siang T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery The failure of CNC machine tools has always been a negative impact on the manufacturing environment. The consequences of the failure will influence the production control, which further increases the duration of unplanned maintenance. To avoid such situations, it is required to predict the tools’ behaviours based on the raw data collected from machines. Hence, the objective of this paper is to obtain the machine tool life using the machining parameters including cutting speed, feed rate, and depth of cut which may affect the tool life in the prediction. All the data is collected by using different types of machine tools material against different types of workpieces. In this paper, classification is chosen to be the data mining approach with two algorithms to build the model for prediction, which are linear regression and multilayer perceptron. The data collected was being split into training and testing data. There are 40% of the data used for training data to build the predictive models while 60% of the data collected is used as testing data. The result of predicted tool life is then validated with the Taylor’s Extended Tool Life equation according to the ISO standard 3685 and ISO 8688-2. The results show that our proposed method is on par with the tool life predicted by Taylor’s method. INTI International University 2022-04 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1611/1/joit2022_11r.pdf text en cc_by_4 http://eprints.intimal.edu.my/1611/2/296 Chan, Choon Kit and Wong, Marven, Zhen Siang (2022) CNC Cutting Tools` Life Prediction Using Data Mining Approach. Journal of Innovation and Technology, 2022 (11). pp. 1-7. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Chan, Choon Kit Wong, Marven, Zhen Siang CNC Cutting Tools` Life Prediction Using Data Mining Approach |
| title | CNC Cutting Tools` Life Prediction Using Data Mining
Approach |
| title_full | CNC Cutting Tools` Life Prediction Using Data Mining
Approach |
| title_fullStr | CNC Cutting Tools` Life Prediction Using Data Mining
Approach |
| title_full_unstemmed | CNC Cutting Tools` Life Prediction Using Data Mining
Approach |
| title_short | CNC Cutting Tools` Life Prediction Using Data Mining
Approach |
| title_sort | cnc cutting tools` life prediction using data mining
approach |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery |
| url | http://eprints.intimal.edu.my/1611/ http://eprints.intimal.edu.my/1611/ http://eprints.intimal.edu.my/1611/1/joit2022_11r.pdf http://eprints.intimal.edu.my/1611/2/296 |