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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Vydané v:Journal of cleaner production Ročník 172; s. 3289 - 3298
Hlavní autori: Saw, Lip Huat, Ho, Li Wen, Yew, Ming Chian, Yusof, Farazila, Pambudi, Nugroho Agung, Ng, Tan Ching, Yew, Ming Kun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 20.01.2018
Predmet:
algorithms
ANFIS
Drilling
economic development
financial economics
fuzzy logic
manufacturing
monitoring
Multi-objective optimization
prediction
surface roughness
Tool condition monitoring
Tool wear
torque
Drilling
Multi-objective optimization
ANFIS
Tool condition monitoring
Tool wear
ISSN:0959-6526, 1879-1786
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2017.10.303