Modeling of tool wear in drilling by statistical analysis and artificial neural network
The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequentl...
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| Format: | Article |
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2005
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| Online Access: | http://shdl.mmu.edu.my/2155/ |
| _version_ | 1848789978453114880 |
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| author | SANJAY, C NEEMA, M CHIN, C |
| author_facet | SANJAY, C NEEMA, M CHIN, C |
| author_sort | SANJAY, C |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3,..., 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation. (c) 2005 Elsevier B.V. All rights reserved. |
| first_indexed | 2025-11-14T18:05:18Z |
| format | Article |
| id | mmu-2155 |
| institution | Multimedia University |
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| last_indexed | 2025-11-14T18:05:18Z |
| publishDate | 2005 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-21552011-09-21T07:53:50Z http://shdl.mmu.edu.my/2155/ Modeling of tool wear in drilling by statistical analysis and artificial neural network SANJAY, C NEEMA, M CHIN, C TA Engineering (General). Civil engineering (General) The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3,..., 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation. (c) 2005 Elsevier B.V. All rights reserved. 2005-12 Article NonPeerReviewed SANJAY, C and NEEMA, M and CHIN, C (2005) Modeling of tool wear in drilling by statistical analysis and artificial neural network. Journal of Materials Processing Technology, 170 (3). pp. 494-500. ISSN 09240136 http://dx.doi.org/10.1016/j.jmatprotec.2005.04.072 doi:10.1016/j.jmatprotec.2005.04.072 doi:10.1016/j.jmatprotec.2005.04.072 |
| spellingShingle | TA Engineering (General). Civil engineering (General) SANJAY, C NEEMA, M CHIN, C Modeling of tool wear in drilling by statistical analysis and artificial neural network |
| title | Modeling of tool wear in drilling by statistical analysis and artificial neural network |
| title_full | Modeling of tool wear in drilling by statistical analysis and artificial neural network |
| title_fullStr | Modeling of tool wear in drilling by statistical analysis and artificial neural network |
| title_full_unstemmed | Modeling of tool wear in drilling by statistical analysis and artificial neural network |
| title_short | Modeling of tool wear in drilling by statistical analysis and artificial neural network |
| title_sort | modeling of tool wear in drilling by statistical analysis and artificial neural network |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://shdl.mmu.edu.my/2155/ http://shdl.mmu.edu.my/2155/ http://shdl.mmu.edu.my/2155/ |