Development of multi-objective optimization models for electrochemical machining process

Asokan, P.; Kumar, Ravinder; Jeyapaul, R.; Santhi, M.

doi:10.1007/s00170-007-1204-8

Cited by 124 publications

(57 citation statements)

References 13 publications

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“…Pulse-on time ANNs [14] are widely used as modeling tools in different engineering fields due to their ability to map from one multivariable space of information to another. They are able to approximating functions to the desired degree of accuracy and unlike physics based models, the shape of the approximation function does not need to be assumed before training.…”

Section: A Forward Prediction Modellingmentioning

confidence: 99%

Electrochemical Micro-Machining Process Parameter Optimization Using a Neural Network-Genetic Algorithm Based Approach

Zou¹,

Rajora²,

Ma³

et al. 2018

IJMMM

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Abstract-In spite of much work done in mapping between the process parameters and performance indicators of electrochemical micro-machining (EMM), very sparse research is available on the optimization of its process parameters. In this article, first, an ANN trained using a hybrid Simulated Annealing (SA) -Levenberg-Marquardt (LM) is developed to map between the process parameters (voltage, feed-rate, and pulse-on time) and performance indicators (inlet and outlet diameters) of EMM. Once the prediction capabilities of the ANN are verified by the use of several testing data sets, the trained ANN is then used as a fitness function to optimize the process parameters of EMM that would lead to the minimization of taper and overcut. The optimization of the process parameters was accomplished using a Genetic Algorithm (GA) based approach. The prediction model was further validates by comparing the tendencies seen in the prediction model to those obtained using partial correlation coefficient.Index Terms-Electrochemical micro-machining (EMM), genetic algorithm (GA), levenberg-marquardt (LM) algorithm, simulated annealing (SA).

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Section: A Forward Prediction Modellingmentioning

confidence: 99%

Electrochemical Micro-Machining Process Parameter Optimization Using a Neural Network-Genetic Algorithm Based Approach

Zou¹,

Rajora²,

Ma³

et al. 2018

IJMMM

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show abstract

“…This model estimates the behavior of unknown system [14] and also successfully applied to many applications includes industry, social systems, ecological systems, economy, geography, traffic, management, education, environment etc. [15][16][17]. The following steps are involved in multi objective optimization of process parameters using Grey relational analysis [14].…”

Section: Grey Relational Analysismentioning

confidence: 99%

Multi Objective Optimisation of Turning Process Parameters on EN 8 Steel using Grey Relational Analysis

Sridhar¹,

Venkateswarlu²

2014

IJEM

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The objective of the present paper is to optimize the machining parameters for turning of EN8steel on lathe machine using a combination of Taguchi and Grey Relational Analysis to yield minimum cutting forces and surface roughness. The process parameters such as rotational speed, feed, depth of cut and cutting fluid have been selected. In this study, the experiments were carried out as per Taguchi experimental design and L 9 orthogonal array was used. Analysis of variance (ANOVA) was also used to find out the most influence of processing parameters on the responses. The regression equations were also established between the process parameters and responses. The results indicate that the depth of cut is the most significant factor affecting the cutting force and surface roughness followed by a feed, speed and cutting fluid.

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“…Asokan P. et la, Studied a practical method of multi-objective optimization in cutting parameters for ECM based on multiple regression models and multiple input single output ANN model [4]. Kuo Tsai et la., showed the ability of different neural networks models to predict the surface finish of work piece based on the effect of changing the electrode polarity in the EDM process.…”

Section: Taguchi Integrated Least Square Support Vector Machine An Almentioning

confidence: 99%

Taguchi integrated Least Square Support Vector Machine an alternative to Artificial Neural Network analysis of electrochemical machining process

Nayak¹,

Tripathy²

2012

IOSRJMCE

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Development of multi-objective optimization models for electrochemical machining process

Cited by 124 publications

References 13 publications

Electrochemical Micro-Machining Process Parameter Optimization Using a Neural Network-Genetic Algorithm Based Approach

Electrochemical Micro-Machining Process Parameter Optimization Using a Neural Network-Genetic Algorithm Based Approach

Multi Objective Optimisation of Turning Process Parameters on EN 8 Steel using Grey Relational Analysis

Taguchi integrated Least Square Support Vector Machine an alternative to Artificial Neural Network analysis of electrochemical machining process

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