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

Palanikumar, K.; Latha, B.; Senthilkumar, V.; Karthikeyan, R.

doi:10.1007/s12540-009-0249-7

Cited by 58 publications

(18 citation statements)

References 15 publications

(17 reference statements)

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“…If the constraints are not satisfied, the population is neglected and the fitness functions will be set as infinite, which could guarantee the random generation of population feasible. Afterwards, the multi-objective optimization module is implemented to classify the solutions with NSGA II [29][30][31]. Finally, a new population will be created to restart the procedure until the optimization process converges (the iteration number reaches the maximum number of generations or the number of stall generations exceeds a default value), thus the Pareto optimal front can be obtained.…”

Section: Description Of the Optimization Proceduresmentioning

confidence: 99%

Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

Zhu

Cai

2017

Energies

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Abstract:A procedure based on MATLAB combined with ANSYS is presented and utilized for the multi-objective aerodynamic and structural optimization of horizontal-axis wind turbine (HAWT) blades. In order to minimize the cost of energy (COE) and improve the overall performance of the blades, materials of carbon fiber reinforced plastic (CFRP) combined with glass fiber reinforced plastic (GFRP) are applied. The maximum annual energy production (AEP), the minimum blade mass and the minimum blade cost are taken as three objectives. Main aerodynamic and structural characteristics of the blades are employed as design variables. Various design requirements including strain, deflection, vibration and buckling limits are taken into account as constraints. To evaluate the aerodynamic performances and the structural behaviors, the blade element momentum (BEM) theory and the finite element method (FEM) are applied in the procedure. Moreover, the non-dominated sorting genetic algorithm (NSGA) II, which constitutes the core of the procedure, is adapted for the multi-objective optimization of the blades. To prove the efficiency and reliability of the procedure, a commercial 1.5 MW HAWT blade is used as a case study, and a set of trade-off solutions is obtained. Compared with the original scheme, the optimization results show great improvements for the overall performance of the blade.

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Section: Description Of the Optimization Proceduresmentioning

confidence: 99%

Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

Zhu

Cai

2017

Energies

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“…The former two provide a sufficiently accurate solution of the aerodynamic performances and structural behaviors of the blade; the latter handles the design variables of the optimization problem and promotes functions optimization. The non-dominated sorting genetic algorithm (NSGA) II [26][27][28] is adapted for the integrated optimization design. It is one of the most efficient and well-known multi-objective evolutionary algorithms and has been widely applied to solve complicated optimization problems.…”

Section: The Integrated Optimization Design Proceduresmentioning

confidence: 99%

Aerodynamic and Structural Integrated Optimization Design of Horizontal-Axis Wind Turbine Blades

Zhu

Cai

2016

Energies

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Abstract:A procedure based on MATLAB combined with ANSYS is presented and utilized for the aerodynamic and structural integrated optimization design of Horizontal-Axis Wind Turbine (HAWT) blades. Three modules are used for this purpose: an aerodynamic analysis module using the Blade Element Momentum (BEM) theory, a structural analysis module employing the Finite Element Method (FEM) and a multi-objective optimization module utilizing the non-dominated sorting genetic algorithm. The former two provide a sufficiently accurate solution of the aerodynamic and structural performances of the blade; the latter handles the design variables of the optimization problem, namely, the main geometrical shape and structural parameters of the blade, and promotes function optimization. The scope of the procedure is to achieve the best trade-off performances between the maximum Annual Energy Production (AEP) and the minimum blade mass under various design requirements. To prove the efficiency and reliability of the procedure, a commercial 1.5 megawatt (MW) HAWT blade is used as a case study. Compared with the original scheme, the optimization results show great improvements for the overall performance of the blade.

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“…Sarma et al [21] proposed a model to correlate the process parameters with the cutting force by using Response Surface Methodology (RSM) whilst turning of GFRP pipes with CBN tools. Palanikumar et al [22] conducted experiments with the machining variables including cutting speed, feed and depth of cut on GFRP composite. A statistical model was developed to predict different responses and the Non-dominated Sorting Genetic Algorithm (NSGA-II) were used to optimize the machining parameters.…”

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

Cited by 58 publications

References 15 publications

Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

Aerodynamic and Structural Integrated Optimization Design of Horizontal-Axis Wind Turbine Blades

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

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