Optimization of machining parameters in turning GFRP composites using a carbide (K10) tool based on the taguchi method with fuzzy logics

Palanikumar, K.; Karunamoorthy, L.; Karthikeyan, R.; Latha, B.

doi:10.1007/bf03027748

Cited by 57 publications

(24 citation statements)

References 15 publications

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“…9 utilized fuzzy logic for optimizing multiple performance characteristics. 13 Chen and Savage developed a fuzzy net-based multilevel in-process surface recognition system for milling operations, 14,15 while Arup Kumar and Dilip Kumar designed a genetic-fuzzy system to predict surface finish and the power requirement in grinding.…”

mentioning

confidence: 99%

Surface hardness prediction of CrN thin film coating on AL7075-T6 alloy using fuzzy logic system

Zalnezhad

Sarhan

Hamdi

2013

Int. J. Precis. Eng. Manuf.

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In recent years, CrN coating has been identified as one of the most promising protective layers on surfaces of tools and dies due to its excellent mechanical properties, corrosion resistance, and surface hardness. This study presents the predicting of chromium nitride (CrN) coating surface hardness on AL7075-T6 using fuzzy logic technique. First, Al7075-T6 was coated with CrN at different parameter conditions, after which the surfaces hardness of the CrN-coated specimens was measured using a micro hardness machine. Next, a fuzzy logic model was established to predict the surface hardness of CrN coating on AL7075-T6 with respect to changes in input process parameters, DC power, temperature, and nitrogen flow rate based on the trained data obtained from the micro hardness test. Three membership functions were allocated in connection with each model input. Finally, five new experimental tests were carried out to verify the predicted results achieved via the fuzzy logic model. The results indicate an agreement between the fuzzy model and experimental results with 94.664% accuracy.

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mentioning

confidence: 99%

Surface hardness prediction of CrN thin film coating on AL7075-T6 alloy using fuzzy logic system

Zalnezhad

Sarhan

Hamdi

2013

Int. J. Precis. Eng. Manuf.

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“…Due to the above facts, achieving multiple performances is very difficult. [22]. The NSGA-II is used for optimization to achieve better multiple perfor- …”

Section: Discussionmentioning

confidence: 99%

Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II)

Palanikumar¹,

Latha

Senthilkumar

et al. 2009

Met. Mater. Int.

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Optimization of cutting parameters is important to achieving high quality in the machining process, especially where more complex multiple performance optimization is required. The present investigation focuses on the multiple performance optimization on machining characteristics of glass fiber reinforced plastic (GFRP) composites. The cutting parameters used for the experiments, which were carried out according to Taguchi's L27, 3-level orthogonal array, were cutting speed, feed and depth of cut. Statistical models based on secondorder polynomial equations were developed for the different responses. The Non-dominated Sorting Genetic Algorithm (NSGA-II) tool was used to optimize the cutting conditions, yielding a non-dominated solution set that is reported here.

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“…The fuzzy rules were developed based on experimental results and expertise. Twenty-seven fuzzy rules were developed for 27 experimental variations by taking the maxmin compositional operation [19].…”

Section: Methodsmentioning

confidence: 99%

“…Fang [18] has used fuzzy logic diagnosis for machining condition monitoring of finish turning process. Palanikumar et al [19] have used fuzzy logic for optimization of machining parameters in machining GFRP composites. This method is highly useful when the process is complex and uncertain in nature.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Modeling and Analysis of Machining Parameters in Machining Titanium Alloy

Ramesh

Karunamoorthy

Palanikumar

2008

Materials and Manufacturing Processes

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Titanium alloys are utilized in many engineering fields such as chemical, industrial, marine, and aerospace due to their unique properties. Machining of these materials causes severe problems. At high temperatures, they become chemically active and tend to react with tool materials. In the present study, fuzzy logic (a tool in artificial intelligence) is used for the prediction of cutting parameters in turning titanium alloy (Ti-6Al-4V). The parameters considered in this study are cutting speed, feed, and the depth of cut. Fuzzy rule-based modeling is employed for prediction of tool flank wear, surface roughness, and specific cutting pressure in machining of titanium alloy. These models can be effectively used to predict the tool flank wear, surface roughness, and specific cutting pressure in machining of titanium alloys. Analysis of the influences of the individual important machining parameters on the responses have been carried out and presented in this study.

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Optimization of machining parameters in turning GFRP composites using a carbide (K10) tool based on the taguchi method with fuzzy logics

Cited by 57 publications

References 15 publications

Surface hardness prediction of CrN thin film coating on AL7075-T6 alloy using fuzzy logic system

Surface hardness prediction of CrN thin film coating on AL7075-T6 alloy using fuzzy logic system

Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II)

Fuzzy Modeling and Analysis of Machining Parameters in Machining Titanium Alloy

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