Investigation into electrochemical micromachining (EMM) through response surface methodology based approach

Munda, J.; Bhattacharyya, B.

doi:10.1007/s00170-006-0759-0

Cited by 78 publications

(34 citation statements)

References 7 publications

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“…It is recognized that the negative value of ROC is physically meaningless, however, this value is regarded only as the optimal solution of the mathematical model. Although it was observed by Munda and Bhattacharyya (2008) that the mathematical model for ROC prediction is statistically adequate at 97.5% confidence level, it is observed that there is no guarantee that the model will yield meaningful predictions over the entire experimentally investigated range. …”

Section: Case Studymentioning

confidence: 96%

Application of multi-stage Monte Carlo method for solving machining optimization problems

Madić¹,

Kovačević²,

Radovanović³

2014

10.5267/j.ijiec

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Enhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal combination of different machining parameters. In this paper, multi-stage Monte Carlo (MC) method was employed to determine optimal combinations of machining parameters for six machining processes, i.e. drilling, turning, turn-milling, abrasive waterjet machining, electrochemical discharge machining and electrochemical micromachining. Optimization solutions obtained by using multi-stage MC method were compared with the optimization solutions of past researchers obtained by using meta-heuristic optimization methods, e.g. genetic algorithm, simulated annealing algorithm, artificial bee colony algorithm and teaching learning based optimization algorithm. The obtained results prove the applicability and suitability of the multi-stage MC method for solving machining optimization problems with up to four independent variables. Specific features, merits and drawbacks of the MC method were also discussed.

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Section: Case Studymentioning

confidence: 96%

Application of multi-stage Monte Carlo method for solving machining optimization problems

Madić¹,

Kovačević²,

Radovanović³

2014

10.5267/j.ijiec

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“…Figure 4 shows the convergence of DSA and GA for the considered two responses. As Munda and Bhattacharyya (2008) did not perform any single objective optimization of the responses, so the optimal solutions as achieved applying DSA cannot be compared with those of the past researchers. The effects of various EMM process parameters on MRR and ROC are also observed through the developed scatter diagrams and the trends exactly match with those identified by Munda and Bhattacharyya (2008).…”

Section: Single Objective Optimizationmentioning

confidence: 99%

“…The value of the minimum objective function (Z 2 ) is found as -0.0810. Munda and Bhattacharyya (2008) determined the maximum MRR value as 0.700 g/min and minimum ROC value as around 20μm at the optimal parametric combination of pulse on/off ratio = 1.0, machining voltage = 3 V, electrolyte concentration = 15g/l, voltage frequency = 42.118Hz and tool vibration frequency = 300 Hz. Thus, it can be claimed that while using DSA, the value of MRR is increased from 0.700 g/min to 0.8354 g/min and the ROC value is drastically reduced from 20μm to 9μm.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

“…Using response surface methodology (RSM), Munda et al (2007) correlated the interactive and higher-order influences of various machining parameters on the combined effect of micro-spark and stray-current machining in an EMM process. Munda and Bhattacharyya (2008) developed a mathematical model for correlating the interactive and higher-order influences of various EMM process parameters, i.e. machining voltage pulse on/off ratio, machining voltage, electrolyte concentration, voltage frequency and tool vibration frequency on MRR and radial overcut (ROC).…”

Section: Introductionmentioning

confidence: 99%

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Differential search algorithm-based parametric optimization of electrochemical micromachining processes

Goswami¹,

Chakraborty²

2013

10.5267/j.ijiec

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Electrochemical micromachining (EMM) appears to be a very promising micromachining process for having higher machining rate, better precision and control, reliability, flexibility, environmental acceptability, and capability of machining a wide range of materials. It permits machining of chemically resistant materials, like titanium, copper alloys, super alloys and stainless steel to be used in biomedical, electronic, micro-electromechanical system and nanoelectromechanical system applications. Therefore, the optimal use of an EMM process for achieving enhanced machining rate and improved profile accuracy demands selection of its various machining parameters. Various optimization tools, primarily Derringer's desirability function approach have been employed by the past researchers for deriving the best parametric settings of EMM processes, which inherently lead to sub-optimal or near optimal solutions. In this paper, an attempt is made to apply an almost new optimization tool, i.e. differential search algorithm (DSA) for parametric optimization of three EMM processes. A comparative study of optimization performance between DSA, genetic algorithm and desirability function approach proves the wide acceptability of DSA as a global optimization tool.

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“…It is the reverse of electroplating. The several advantages of ECM is no tool wear, stress-free, high throughput, smooth surfaces, and the ability to machine complex shapes in materials regardless of their hardness or whether they are heat-resistant materials [1]. In through-mask EMM, a photo resist patterned metal workpiece is made an anode in an electrochemical cell such that the exposed metal surface is removed by high-rate anodic metal dissolution by passing an external current.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of Anode Shape Prediction in Through Mask Electrochemical Micro Machining

Jain¹,

Ghelot²,

Bagoriya³

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Abstract:To manufacture quality products at lowest cost in industries, optimization is an effective technique which can be Electrochemical micro-machining (EMM) appears to be very promising as a future micromachining technique, since in many areas of applications it offers several advantages, which include higher machining rate, better precision and control, short lead time and a wide range of materials that can be machined.In this work, the shape evolution in through-mask electrochemical micromachining (ECMM) process is investigated numerically and experimentally. The effect of process parameters on the anode shape was demonstrated by Finite Element Method using COMSOL Multiphysics software. With finite element method (FEM), the anodic evolution process is predicted and effects of stray current also have been identified. The validation experiment is conducted and the hole drilling procedure is observed. The FEM calculation predicted model is in good agreement with experimental model. With the sets of experiments and Finite element analysis simulation, the optimized set and the formulation of the electrolyte in through-mask ECMM are achieved.

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Investigation into electrochemical micromachining (EMM) through response surface methodology based approach

Cited by 78 publications

References 7 publications

Application of multi-stage Monte Carlo method for solving machining optimization problems

Application of multi-stage Monte Carlo method for solving machining optimization problems

Differential search algorithm-based parametric optimization of electrochemical micromachining processes

Experimental Study of Anode Shape Prediction in Through Mask Electrochemical Micro Machining

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