Optimization of surface roughness in turning of GFRP composites using genetic algorithm

Hussain, Syed Altaf; Pandurangadu, V.; Kumar, Kaushik

doi:10.4314/ijest.v6i1.6

Cited by 16 publications

(5 citation statements)

References 9 publications

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“…However, Kumar Parida et al [27] stated that optimum cutting parameters for better surface quality were obtained from low feed rate, cutting speed, and depth of cut, and they stated that the most effective parameter was cutting speed. In a study conducted by Hussain et al [28], surface roughness value increased with increasing fiber orientation angle and feed rate, and it decreased with increasing cutting speed and depth of cut. According to Gupta and Kumar [29], in turning of GFRP composites, surface roughness value decreased with increasing tool nose radius.…”

Section: Introductionmentioning

confidence: 95%

Investigation of mechanical characteristics of GFRP composites produced from chopped glass fiber and application of taguchi methods to turning operations

Çelik

Türkan

2020

SN Appl. Sci.

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Glass fiber-reinforced plastic (GFRP) composites take place in engineering materials owing to their low-weight and high-mechanical properties. In some cases, they need to be shaped by machining before using in industrial applications. However, when these composites are machined, many problems such as bad surface quality, rapid tool wear are encountered. Therefore, optimization of cutting parameters is essential to eliminate or minimize these problems. In this study, GFRP composites were produced by combining polyester matrix material with glass fibers (GF) having 6 mm, 6-12 mm, 12 mm fiber length, and 20%, 25%, 30% fiber ratio by weight. The tensile strengths of these composites were investigated. Turning tests were also performed with cutting speeds of 40, 80, and 120 m/min, feed rates of 0.1, 0.2 and 0.3 mm/rev, and depth of cut of 1, 2, and 3 mm, according to Taguchi L 27 standard orthogonal array method. The effect of fiber length and ratio, and cutting parameters on cutting forces and surface roughness were analyzed. As a result of the experiments, it was observed that the reinforced polymer matrix with GF provide to increase the tensile strength. The highest tensile strength was obtained as 55.95 MPa from the composite having a fiber length of 12 mm and a fiber ratio of 25%. Besides, the feed rate was determined as the most effective parameter among the all parameters on both cutting force and surface roughness. Therefore, the feed rate should be chosen low for lower cutting force and surface roughness values.

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Section: Introductionmentioning

confidence: 95%

Investigation of mechanical characteristics of GFRP composites produced from chopped glass fiber and application of taguchi methods to turning operations

Çelik

Türkan

2020

SN Appl. Sci.

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“…Experiments were carried out with different machining parameters like cutting speed, feed rate, depth of cut and cutting environment (dry and wet) and these machining parameters were optimized by using feed forward back propagation Artificial Neural Network (ANN) method. Hussain et al [30] applied the Genetic Algorithm (GA) for the optimization of machining variables viz. cutting speed, feed, depth of cut, and work piece (fiber orientation angle) for surface roughness in turning of GFRP composites with poly-crystalline diamond (PCD) tool.…”

Section: State Of Art: Turning Of Gfrp Compositesmentioning

confidence: 99%

Multi-response Optimization in Machining of GFRP (Epoxy) Composites: An Integrated Approach

Verma

Abhishek

Datta

et al. 2015

Journal for Manufacturing Science and Production

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This paper investigates on optimization of process control parameters during machining (drilling and turning) of Glass Fiber Reinforced Polymer (GFRP) composites by considering multiple process performance yields. The main characteristic indices for evaluating drilling performance are thrust force, torque and delamination factor (at entry as well as exit); the corresponding machining parameters are drill speed, feed rate and diameter of the drill bit. The following process parameters viz. spindle speed, feed rate, and depth of cut have been considered to investigate multiple process responses viz. Material Removal Rate (MRR), surface roughness (R a ), tool-tip temperature (maximum temperature generated during machining at tool-tip) and resultant cutting force whilst turning of GFRP (epoxy) composite specimens. As traditional Taguchi method is unable to solve multiobjective optimization problem; to overcome this limitation; the study proposes Principal Component Analysis (PCA) along with fuzzy logic and finally Taguchi philosophy towards multiple-objective optimization in machining of GFRP composites. Analysis of the solutions for the multiobjective optimization by aforesaid approach has been depicted through two case experimental researches. It has also been observed from drilling experiments that PCA-fuzzy (integrated with Taguchi method) has provided better result as compared to WPCA (Weighted Principal Component Analysis) based Taguchi method. The proposed PCA-Fuzzy based Taguchi method can fruitfully be applied for continuous quality improvement and off-line quality control of the process/product.

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“…Taguchi method combination with fuzzy logic resulted in low cutting force, surface roughness, cutting power, and specific cutting pressure in machining GFRP composites using carbide K20 cutting tool. Surface methodology and genetic algorithm were also used by Hussain et al [16] to optimise parameters during turning of GFRP. It was observed that the quality of the surface improved when response surface method was used in combination with genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Investigation of surface roughness and material removal rate for UD-GFRP composite using Taguchi grey relational analysis

Meenu¹

2022

Int. J. Automot. Mech. Eng.

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In this paper, a grey relational method was used for minimising surface roughness and maximising material removal rate during turning of unidirectional glass fibre reinforced plastic composite (UD-GFRP). Taguchi approach was used for the experiment. The parameters studied were tool rake angle, speed, depth of cut, feed, tool nose radius, and cutting environment. Castrol water (1:6) miscible soluble coolant was flooded in the machining zone. Cutting environment: Wet (33°-38°c) and Cold (5°-7°c) were used. The Taguchi L18 orthogonal array was used for performing the experiment. Significant parameters were found by applying ANOVA. The grey relational analysis was used to optimise the parameters affecting the response. Cutting speed was the factor, followed by feed rate that influenced surface roughness and material removal rate. The surface roughness of 1.761 µm and material removal rate of 275.78 mm 3 were achieved.

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Optimization of surface roughness in turning of GFRP composites using genetic algorithm

Cited by 16 publications

References 9 publications

Investigation of mechanical characteristics of GFRP composites produced from chopped glass fiber and application of taguchi methods to turning operations

Investigation of mechanical characteristics of GFRP composites produced from chopped glass fiber and application of taguchi methods to turning operations

Multi-response Optimization in Machining of GFRP (Epoxy) Composites: An Integrated Approach

Investigation of surface roughness and material removal rate for UD-GFRP composite using Taguchi grey relational analysis

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