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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| Vydáno v: | Journal of cleaner production Ročník 172; s. 3289 - 3298 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
20.01.2018
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| Témata: | |
| ISSN: | 0959-6526, 1879-1786 |
| On-line přístup: | Získat plný text |
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| Abstract | 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.
•We investigated the drill wear using cutting force signal.•We conducted sensitivity analysis of the drill wear on mild steel.•We used Adaptive Neuro Fuzzy Inference system to predict drill wear.•We optimized the drilling parameters using genetic algorithm method. |
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| AbstractList | 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.
•We investigated the drill wear using cutting force signal.•We conducted sensitivity analysis of the drill wear on mild steel.•We used Adaptive Neuro Fuzzy Inference system to predict drill wear.•We optimized the drilling parameters using genetic algorithm method. 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. |
| Author | Yew, Ming Kun Yew, Ming Chian Ng, Tan Ching Saw, Lip Huat Yusof, Farazila Ho, Li Wen Pambudi, Nugroho Agung |
| Author_xml | – sequence: 1 givenname: Lip Huat surname: Saw fullname: Saw, Lip Huat email: sawlh@utar.edu.my organization: Lee Kong Chian Faculty of Engineering and Science, UTAR, Selangor, 43000, Malaysia – sequence: 2 givenname: Li Wen surname: Ho fullname: Ho, Li Wen organization: Tech-lab Manufacturing Sdn Bhd, Selangor, 43200, Malaysia – sequence: 3 givenname: Ming Chian surname: Yew fullname: Yew, Ming Chian organization: Lee Kong Chian Faculty of Engineering and Science, UTAR, Selangor, 43000, Malaysia – sequence: 4 givenname: Farazila surname: Yusof fullname: Yusof, Farazila organization: Department of Mechanical Engineering, University of Malaya, Kuala Lumpur, 53600, Malaysia – sequence: 5 givenname: Nugroho Agung surname: Pambudi fullname: Pambudi, Nugroho Agung organization: Mechanical Engineering Education, Universitas Negeri Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta 57126, Indonesia – sequence: 6 givenname: Tan Ching surname: Ng fullname: Ng, Tan Ching organization: Lee Kong Chian Faculty of Engineering and Science, UTAR, Selangor, 43000, Malaysia – sequence: 7 givenname: Ming Kun surname: Yew fullname: Yew, Ming Kun organization: Lee Kong Chian Faculty of Engineering and Science, UTAR, Selangor, 43000, Malaysia |
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| Keywords | Drilling Multi-objective optimization ANFIS Tool condition monitoring Tool wear |
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