Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining
Machining processes have an important place in the manufacturing industry and it indeed contributed to the economic growth of a country. About 75% of machining processes involved drilling operation. Tool wear is a common phenomenon in the machining operation and significantly affects the product dim...
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| Format: | Article |
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Elsevier
2018
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| _version_ | 1796961298659934208 |
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| author | Saw, Lip Huat Ho, Li Wen Yew, Ming Chian Yusof, Farazila Pambudi, Nugroho Agung Ng, Tan Ching Yew, Ming Kun |
| author_facet | Saw, Lip Huat Ho, Li Wen Yew, Ming Chian Yusof, Farazila Pambudi, Nugroho Agung Ng, Tan Ching Yew, Ming Kun |
| author_sort | Saw, Lip Huat |
| collection | UM |
| description | Machining processes have an important place in the manufacturing industry and it indeed contributed to the economic growth of a country. About 75% of machining processes involved drilling operation. Tool wear is a common phenomenon in the machining operation and significantly affects the product dimension accuracy, machining efficiency, manufacturing downtime, surface roughness and economic loss. Hence, an intelligent tool condition monitoring system is needed to maximize tool life and reduce machine downtime due to the tool replacement. In this study, experiments were conducted to investigate the influence of different drilling parameters on average drilling torque and thrust force. Effects of spindle rotational speed, feed rate and diameter of drill on tool wear were determined through Adaptive Neuro Fuzzy Inference System (ANFIS). Next, genetic algorithm (GA) was used to identify the optimal drilling parameter for different diameters of drill. Experimental results agreed well with the GA prediction results with a relative error of 3%. Hence, the results showed that ANFIS-GA is a faster and more accurate alternative to the existing methods for tool wear prediction. |
| first_indexed | 2024-03-06T05:52:13Z |
| format | Article |
| id | um.eprints-20762 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:52:13Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-207622019-03-19T07:07:11Z http://eprints.um.edu.my/20762/ Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining Saw, Lip Huat Ho, Li Wen Yew, Ming Chian Yusof, Farazila Pambudi, Nugroho Agung Ng, Tan Ching Yew, Ming Kun TJ Mechanical engineering and machinery Machining processes have an important place in the manufacturing industry and it indeed contributed to the economic growth of a country. About 75% of machining processes involved drilling operation. Tool wear is a common phenomenon in the machining operation and significantly affects the product dimension accuracy, machining efficiency, manufacturing downtime, surface roughness and economic loss. Hence, an intelligent tool condition monitoring system is needed to maximize tool life and reduce machine downtime due to the tool replacement. In this study, experiments were conducted to investigate the influence of different drilling parameters on average drilling torque and thrust force. Effects of spindle rotational speed, feed rate and diameter of drill on tool wear were determined through Adaptive Neuro Fuzzy Inference System (ANFIS). Next, genetic algorithm (GA) was used to identify the optimal drilling parameter for different diameters of drill. Experimental results agreed well with the GA prediction results with a relative error of 3%. Hence, the results showed that ANFIS-GA is a faster and more accurate alternative to the existing methods for tool wear prediction. Elsevier 2018 Article PeerReviewed Saw, Lip Huat and Ho, Li Wen and Yew, Ming Chian and Yusof, Farazila and Pambudi, Nugroho Agung and Ng, Tan Ching and Yew, Ming Kun (2018) Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining. Journal of Cleaner Production, 172. pp. 3289-3298. ISSN 0959-6526, DOI https://doi.org/10.1016/j.jclepro.2017.10.303 <https://doi.org/10.1016/j.jclepro.2017.10.303>. https://doi.org/10.1016/j.jclepro.2017.10.303 doi:10.1016/j.jclepro.2017.10.303 |
| spellingShingle | TJ Mechanical engineering and machinery Saw, Lip Huat Ho, Li Wen Yew, Ming Chian Yusof, Farazila Pambudi, Nugroho Agung Ng, Tan Ching Yew, Ming Kun Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining |
| title | Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining |
| title_full | Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining |
| title_fullStr | Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining |
| title_full_unstemmed | Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining |
| title_short | Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining |
| title_sort | sensitivity analysis of drill wear and optimization using adaptive neuro fuzzy genetic algorithm technique toward sustainable machining |
| topic | TJ Mechanical engineering and machinery |
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