Teaching—Learning-Based Optimization Coupled with Response Surface Methodology for Micro Electrochemical Machining of ALUMINIUM NANOCOMPOSITE

Prakash, J.; Gopalakannan, S.

doi:10.1007/s12633-020-00434-0

Cited by 24 publications

(6 citation statements)

References 55 publications

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“…Response surface methodology is used to find a relationship between the input parameters and the output responses [18,22]. RSM is a collection of mathematical and statistical techniques for empirical model building.…”

Section: Response Surface Methodologymentioning

confidence: 99%

Modeling and prediction of electrical discharge machining performance parameters for AA 8081 hybrid composite using artificial neural network

Vivekanandhan¹,

Rajmohan²,

Senthilkumar³

2022

Surf. Topogr.: Metrol. Prop.

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Aluminum matrix composites (AMCs) are gaining increasing attention from various industries due to their lightweight and more excellent wear resistance than conventional materials. Manufacturers embracing that difficulty in machining MMC due to reinforcing particles abrasive nature shorten the tool life. Electro-discharge machining (EDM) is an enormously used non-conventional process to remove material in die making, aerospace, and automobile industries and machine any material with the highest hardness. Hence in the present study, EDM was performed on an aluminium alloy 8081 (AA8081) with reinforcement of 10% SiC, 5% B4C, and 5% Gr particles utilizing an ultrasonic cavitation assisted stir casting process. The machining investigation was carried out adopting face-centered central composite design (CCD) with three parameters such as current, pulse-on time, and pulse-off time to ascertain the effects of two sustainable measures, viz., Material removal rate (MRR) and tool wear rate (TWR) the data were collected. An Artificial Neural Network (ANN) model was developed based on data obtained from experiments. Finally, experimental values are compared with the predicted values of ANN and found high prediction accuracy. The advanced model results are used to approximate the responses fairly precisely. The version features a mean coefficient of correlation of 0.99072. Effects uncovered that the projected version is employed for the prediction of the complex EDM process.

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Section: Response Surface Methodologymentioning

confidence: 99%

Modeling and prediction of electrical discharge machining performance parameters for AA 8081 hybrid composite using artificial neural network

Vivekanandhan¹,

Rajmohan²,

Senthilkumar³

2022

Surf. Topogr.: Metrol. Prop.

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“…Pure magnesium was used by only one author: Kumari et al (2020). Some others that used aluminum (alloy and composites) are Dikshit et al (2017), Abdallah et al (2014, Rudrapati et al (2016), Prakash & Gopalakannan (2021), Patel et al (2020), Pare et al (2015). Lastly, those that made use of Polymer materials are , Natarajan et al (2018), andAbhishek, Kumar, Datta, &.…”

Section: Aspects Studiedmentioning

confidence: 99%

“…Kumar et al (2020) and Rao & Kalyankar (2013) used wire electrical discharge turning (WEDT) as a machining process in their studies. Prakash & Gopalakannan (2021) made use of microelectrochemical machining (μECM) for a machining operation with material removal rate (MRR), radial overcut, tool electrode wear, and surface roughness as output parameters. Another author, Sharma et al (2020), used electric discharge drilling (EDD) for drilling operation with drilling rate (DR) and electrode wear ratio (EWR) as output parameters.…”

Section: Aspects Studiedmentioning

confidence: 99%

“…Upon evaluation, It is concluded that the TLBO approach needs quite a small size of population and low maximum iteration to reach optimal process parameters. Prakash & Gopalakannan (2021) claimed that when the input parameters are increased, the MRR, surface roughness, and tool wears. Radial overcut increases also when Aluminum alloy (AA) 7075 reinforced with nano silicon carbide particles (1.5 wt%) goes through Micro Electro Chemical Machining (μECM) process.…”

Section: Generalmentioning

confidence: 99%

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Optimizing The Machining Process of IS 2062 E250 Steel Plates with The Boring Operation Using a Hybrid Taguchi-Pareto Box Behnken-teaching Learning-based Algorithm

Abdullahi¹,

Oke²

2022

IJIEEM

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In this article, a new method termed the Taguchi-Pareto-Box Behnken design teaching learning-based optimization (TPBBD–TLBO) was developed to optimize the boring process, which promotes surface roughness as the output. At the same time, the speed, feed, and depth of cut are taken as the inputs. The case examines experimental data from the literature on the boring of IS 2062 E250 steel plates. The proposed method draws from a recent idea on the Taguchi-Pareto-Box Behnken design method that argues for a possible relationship between the Taguchi-Pareto method and the Box Behnken design method. This idea was used as a basis for the further argument that teaching learning-based optimization has a role in the further optimization of the established TPBBD method. The optimal solutions were investigated when the objective function was generated using the Box Behnken design in a case. It was replaced with the regression method in the other case, and the python programming codes were used to execute the computations. Then the optimal solutions concerning the parameters of speed, feed rate, depth of cut, and nose radius were evaluated. With the Box Behnken as the objective function for the TLBO method, convergence was reached at 50 iterations with a class population of 5. The optimal parametric solutions are 800 rpm of speed, 0.06 min/min of feed rate, 1 min for depth of cut, and 0 min for nose radius. On the use of the regression method for the objective function, while the TLBO method was deployed, convergence was experienced after 50 iterations with a class population of 200 students. The optimal parametric solution is 1135rpm of speed, 0.06 min/min of feed rate, 1024 min of the depth of cut, and 0.61 min of nose radius. The speed, depth of cut, and nose radius showed higher values, indicating the use of more energy resources to accomplish the optimal goals using the regression method-based objective function. Therefore, the proposed method constitutes a promising route to optimize further the results of the Taguchi-Pareto-Box Behnken design for boring operation improvement.

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“…They have also optimized the micro ECM process parameters using TOPSIS method and percentage reinforcement of ground-granulated blast furnace slag has the significant effect on the machining speed and accuracy [10]. Prakash et al, [11] have analyzed the micro ECM performance using Teaching-Learning-Based Optimization (TLBO) algorithm and RSM method on AA7075 reinforced with nano silicon carbide particles and the results of these analysis are similar. Sathishkumar et al, [12] have fabricated the Al6061+ (6wt % SiC + 4wt % Gr) composites using stir casting process.…”

Section: Introductionmentioning

confidence: 98%

Production of Aluminum 6063 Metal Matrix Composite with 12% Magnesium Oxide and 5% Graphite and its Machinability Studies using Micro Electrochemical Machining

Babu¹,

Sabarinathan²,

Dharmalingam³

2020

JNMES

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Hybrid Aluminum Metal Matrix Composites (AMMCs) are finding application in aerospace and automobile industries for its light weight. In this research hybrid AMMCs with 12% magnesium oxide (MgO) and 5% graphite (Gr) were fabricated and mechanical properties were studied. The compressive strength, bending stress frictional force, impact strength are found to be 292.94 N.mm-2 , 1.31 N.mm-2 , 11.3 N (maximum) J.m-2 = 0.4x10 5 respectively. Moreover the highest hardness values are obtained during Brinell and Rockwell testing's 127.23 BHN, 59 HRB respectively. Machinability studies using micro electrochemical machining (micro ECM) is performed on the hybrid AMMCs by varying the voltage, electrolyte concentration, duty cycle and electrolyte temperature. L9 Orthogonal array (OA) is conducted and the experimental results are analyzed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Based on the TOPSIS analysis the optimal combination for the higher MRR and lesser OC is 9 V,25 gm.L-1 ,80 % duty cycle and 35º C electrolyte temperature (A2B2C3D1). Analysis of Variance (ANOVA) shows that the duty cycle is the most significant factor followed by voltage and electrolyte temperature for higher material removal rate (MRR) and lower overcut (OC).

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Teaching—Learning-Based Optimization Coupled with Response Surface Methodology for Micro Electrochemical Machining of ALUMINIUM NANOCOMPOSITE

Cited by 24 publications

References 55 publications

Modeling and prediction of electrical discharge machining performance parameters for AA 8081 hybrid composite using artificial neural network

Modeling and prediction of electrical discharge machining performance parameters for AA 8081 hybrid composite using artificial neural network

Optimizing The Machining Process of IS 2062 E250 Steel Plates with The Boring Operation Using a Hybrid Taguchi-Pareto Box Behnken-teaching Learning-based Algorithm

Production of Aluminum 6063 Metal Matrix Composite with 12% Magnesium Oxide and 5% Graphite and its Machinability Studies using Micro Electrochemical Machining

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