Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

Murthy, K. Sundara; Rajendran, I.

doi:10.1590/s1678-58782012000300005

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…The 0.021 P-Value of flow rate which less than 0.05 indicates that MQL flow rate is the most significant factor influencing machined parts surface roughness compared to other parameters. This finding is similar to The-Vinh and Hsu [14] and Murthy and Rajendran [15] which also found that the result of surface roughness was low at level 3 of fluid flow rate. Low surface roughness obtained at high lubricant flow rate because the good coverage of lubricant on the machining area.…”

Section: Methodssupporting

confidence: 89%

Experimental Investigation of MQL Optimum Parameters in End Milling of AA6061-T6 using Taguchi Method

Safiei¹,

Rahman²,

Rusdan³

2018

IJET

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Minimum Quantity Lubricants is a technique in supplying small quantity of lubricant into machining area which also part of green manufacturing approach that receive wide attention globally. The main driven of introducing MQL method was due to negative environmental impact which leads to safety and health issues of conventional coolant among workers especially in tool and mould industries. Besides, based on research findings, the MQL system has the capability for lubricating and cooling both work piece and cutting tool. In order to find the best solution for machining and also to enhance machining performance, first and foremost the MQL parameters must be controlled wisely as it has remarkable effects on lubricant coverage, droplets size and subsequently influence the machining performance. Nozzle angle, nozzle distance and MQL flow rate are the important parameters studied and surface roughness is the response parameter. Therefore, in this study, MQL optimum parameters were explored by minimizing surface roughness in end milling process using Taguchi L9 orthogonal method. Aluminum Alloy 6061-T6 was selected as work piece material. The results show that the best combination of MQL parameters in minimizing surface roughness was obtained at 30mm nozzle distance, 30 degree nozzle angle and 1.98 mL/min MQL flow rate. Hence, based on this optimal condition, three confirmation runs were conducted. The margin error is acceptable which less than 10% and within prediction interval. This results can work as a base line guidance for any experimental that employ MQL system.

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Section: Methodssupporting

confidence: 89%

Experimental Investigation of MQL Optimum Parameters in End Milling of AA6061-T6 using Taguchi Method

Safiei¹,

Rahman²,

Rusdan³

2018

IJET

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“…Flank wear occurs on the tool flank face due to abrasion of tool with part machined surface. Notch wear is an aggregate of flank and crater wear, which occurs abreast to the intersection of cutting tool and machined surface [43]. For ceramic cutting tools, the wear mechanisms include the abrasive wear, adhesive wear, chemical wear, diffusion wear and oxidation wear, etc.…”

Section: Tool Wearmentioning

confidence: 99%

Fabrication and dry cutting performance of Si3N4/TiC ceramic tools reinforced with the PVD WS2/Zr soft-coatings

Xing

Deng

Zhang

et al. 2015

Ceramics International

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“…All the related works utilize a hybrid strategy, which combines the ANN and GA or combines the Taguchi method, ANN, and GA, for real-world case studies in the engineering field, as shown in Tables 1 and 2. For the milling operation, the Taguchi method, ANN, and GA were hybridized to cover all the steps from the experimental design to optimization for milling processes (Tansel et al 2011;Murthy and Rajendran 2012). Additionally, in (Tansel et al 2011), the ANN is embedded inside the GA to produce the predicted process output, whereas in (Murthy and Rajendran 2012), the GA is embedded inside the ANN to optimize the synaptic weights between neurons.…”

Section: Related Workmentioning

confidence: 99%

Hybrid evolutionary optimization for nutraceutical manufacturing processes

2015

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In this paper, an intelligent approach, called HERON (hybrid evolutionary optimization for nutraceutical manufacturing), is proposed to optimize a variety of manufacturing processes in the nutraceutical field. The approach integrates the Taguchi method, an artificial neural network (ANN), and a genetic algorithm (GA). The Taguchi method is used to cost-effectively gather the data on the process parameters. Data obtained by the Taguchi method are divided into input and output data for an ANN's input and output parameters, respectively. The ANN trains itself to develop the relationship between its input and output parameters. The trained ANN is then integrated into a GA as the fitness function, such that the GA can evolutionarily obtain the optimal process parameters. The HERON is validated through a manufacturing process on soft-shell turtle soft-capsules. The objective is to minimize the soft-capsule defect rate. Compared to the defect rates obtained by the empirical and Taguchi methods, the HERON reduces the defect rate by 43.75 and 32.5 %, respectively. In addition, compared to the manufacturing costs obtained by the empirical and Taguchi methods, the HERON reduces the manufacturing cost by 11.81 and 25.29 %, respectively.

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Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis

Cited by 17 publications

References 29 publications

Experimental Investigation of MQL Optimum Parameters in End Milling of AA6061-T6 using Taguchi Method

Experimental Investigation of MQL Optimum Parameters in End Milling of AA6061-T6 using Taguchi Method

Fabrication and dry cutting performance of Si3N4/TiC ceramic tools reinforced with the PVD WS2/Zr soft-coatings

Hybrid evolutionary optimization for nutraceutical manufacturing processes

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