A hybrid algorithm to optimize cutting parameter for machining GFRP composite using alumina cutting tools

Khan, M. Adam; Kumar, A. Ramesh; Poomari, A.

doi:10.1007/s00170-011-3553-6

Cited by 31 publications

(7 citation statements)

References 29 publications

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“…Khan et al [26] presented two different models namely, genetic algorithm based neural network (GA-NN) model and particle swarm optimization based neural network (PSO-NN) model to predict unit production cost in machining GFRP composite with Ti[C, N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina based ceramic cutting tool (CC670). Gupta and Gill [27] developed cutting force prediction model for machining of unidirectional glass fiber reinforced plastics (UD-GFRP) composite with PCD insets.…”

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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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 study developed a relativily novel fuzzy based taguchi approach for machining of GFRP and equate this module with WPCA and PCA based Taguchi component. Khan et al [4] generate mathematical model for machining of polymer composites which is reinforced by glass fibre with Ti [C, N] blended alumina-based ceramic cutting tool (CC650) and Si-C whisker strengthened tool (CC670) for adjusting the element manufacture rate. Kumar et al [5] examined two distinguished developmental procedure constructed neural structure namely genetic algorithm based neural network (GA-NN) & particle swarm optimization constructed neural network (PSO-NN).…”

Section: Previous Workmentioning

confidence: 99%

Grey embedded in artificial neural network (ANN) based on hybrid optimization approach in machining of GFRP epoxy composites

Kharwar¹,

Verma²

2019

FME Transactions

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Glass fibre reinforced epoxy polymers (GFRP) composites have gathered enormous attraction because of their exceptional engineering properties such as superior proportion in strength-to-weight and enhanced durability. However, to develop a machined component is a difficult task due to nonhomogeneity and anisotropic behavior of GFRP. In this study a hybrid module, Grey relational analysis (GRA) embedded in artificial neural network (ANN) based on Taguchi approach for multicriteria optimization in Turning of GFRP materials has been carried out. The desired machining characteristics are minimum cutting force, minimum surface roughness & maximum rate of material removal & these have been used to calculate the Grey relational coefficient for individual response and then converted into the single response function i.e. Grey relational grade (GRG) value which was used for converting multiobjective problem into single objective problem and then well-trained ANN based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm has been used to predict the most favourable process parameters setting i.e. spindle speed (880 rpm), feed rate (0.05 mm/rev.), depth of cut (0.4 mm). The predicted GRG of process parameters via L 16 OA of Taguchi-based GRA with ANN has been improved by 8.5%.This setting has been selected based on highest GRG predicted by well-trained ANN which finally has been checked by the confirmatory test which produces satisfactory results.

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“…The combination of evolutionary techniques in optimizing machining parameters is also a developing trend. Khan et al 9 developed two hybrid neural network models, namely, genetic algorithm–based neural network (GA-NN) model and particle swarm optimization–based neural network (PSO-NN) model to find the optimal cutting conditions, and the objective considered was the minimization of unit production cost subjected to various machine constraints. Zuperl et al 10 built an off-line optimization and feed-forward neural control scheme (UNKS) based on the hybrid process modeling to control the cutting force adaptively and maintain constant roughness of surface being milled.…”

Section: Introductionmentioning

confidence: 99%

Stability-based selection of cutting parameters to increase material removal rate in high-speed machining process

Cao

Zhou

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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The reasonable selection of cutting parameters is of great significance to improve the productivity in high-speed machining process. In this article, a stability-based selection method of cutting parameters is proposed to increase material removal rate in high-speed machining process. First, the coupled dynamics of the high-speed spindle system and machining process is modeled. Then, the interaction mechanism between the spindle-tool system and the cutting process is investigated. Based on the process stability and surface quality of workpiece, a cutting parameter selection method is proposed from the perspective of machining stability. In order to select reasonable spindle speed and depth of cut, the lower bound of chatter stability lobe diagram and surface finish are taken as constraints, and the maximum material removal rate is set as the target. The proposed method is applied to the machining of the front face of a gearbox cover, which is made of Aluminum-7050. The axial depth of cut and spindle speed are chosen reasonably with the increase in machining efficiency by about 133%.

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A hybrid algorithm to optimize cutting parameter for machining GFRP composite using alumina cutting tools

Cited by 31 publications

References 29 publications

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

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

Grey embedded in artificial neural network (ANN) based on hybrid optimization approach in machining of GFRP epoxy composites

Stability-based selection of cutting parameters to increase material removal rate in high-speed machining process

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