Optimization of Micro-WEDM Process of Aluminum Matrix Composite (A413-B<sub>4</sub>C): A Response Surface Approach

Sivaprakasam, P.; Hariharan, P.; Gowri, S.

doi:10.1080/10426914.2013.823502

Cited by 67 publications

(31 citation statements)

References 18 publications

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“…In this example, the experimental data of Sivaprakasam et al (2013) are considered for multi-objective optimization of the responses using the adopted approach. Based on a central composite design plan, Sivaprakasam et al (2013) conducted 20 experiments in a micro-WEDM set-up while taking into account three process parameters, i.e.…”

Section: Examplementioning

confidence: 99%

“…The derived experimental data are exhibited in Table 8. Using desirability function approach, Sivaprakasam et al (2013) determined the optimal values of MMR, KW and SR responses as 0.0260 mm 3 /min, 87 μm and 0.97 μm respectively at a parametric combination of voltage = 80 V, capacitance = 0.01 μF and feed rate =15 μm/sec. In this example, MRR is an L-type response, whereas, KW and SR are S-type responses.…”

Section: Examplementioning

confidence: 99%

“…Micro-WEDM process parameters and their levels (Sivaprakasam et al, 2013) Based on the RSM technique, three regression models were developed highlighting the influences of the micro-WEDM process parameters on the considered responses. Using these three regression equations and employing the multivariate loss function approach, the corresponding optimization problem is now formulated which is subsequently solved using LINGO software.…”

Section: Tablementioning

confidence: 99%

“…Based on a central composite design plan, Sivaprakasam et al (2013) conducted 20 experiments in a micro-WEDM set-up while taking into account three process parameters, i.e. voltage, capacitance and feed rate, and three responses, i.e.…”

Section: Examplementioning

confidence: 99%

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A multivariate quality loss function approach for parametric optimization of non-traditional machining processes

Chakraborty¹,

Das²

2018

10.5267/j.msl

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Due to various added advantages over the conventional material removal processes, non-traditional machining (NTM) processes have now been widely applied in different manufacturing industries. To achieve the desired response values, it is always recommended to operate these NTM processes at their optimal parametric settings. Various single response optimization techniques are already available to determine the optimal combinations of NTM process parameters for achieving maximum or minimum value of a single response. In this paper, a multivariate quality loss function approach is adopted for simultaneous optimization of responses for three NTM processes. It is observed that this approach outperforms the other multi-response optimization techniques, like desirability function, distance function and mean squared error methods with respect to the achieved response values. With modification of the corresponding objective function and constraints of the developed non-linear programming problem, it can be effectively applied to any non-traditional as well as conventional machining process as a multi-objective optimization tool.

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Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Tablementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

See 2 more Smart Citations

A multivariate quality loss function approach for parametric optimization of non-traditional machining processes

Chakraborty¹,

Das²

2018

10.5267/j.msl

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“…Sivaprakasam Palani [7], investigated micro wire electrical discharge machining process with multiple performance characteristics, based on the response surface methodology, using AMCs and Al Alloy 2024.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wire EDM Process Parameters for Machining of AMCs (413/B4c) using Taguchi Technique

al.¹

2017

IJMPERD

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Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

Rao

2015

Teaching Learning Based Optimization Algorithm

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Multiobjective optimization aspects of a traditional machining process namely surface grinding and five modern machining processes namely, wire-electro discharge machining process, micro-wire-electric discharge machining process, laser cutting process, electrochemical machining process, and electrochemical discharge machining process are presented in this chapter. A posteriori multiobjective optimization algorithm named as nondominated sorting teaching-learning-based optimization (NSTLBO) algorithm is proposed to solve the multiobjective optimization problems of the machining processes. A Pareto optimal set of solutions along with a Pareto front is presented for each of the considered machining processes.

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Optimization of Micro-WEDM Process of Aluminum Matrix Composite (A413-B₄C): A Response Surface Approach

Cited by 67 publications

References 18 publications

A multivariate quality loss function approach for parametric optimization of non-traditional machining processes

A multivariate quality loss function approach for parametric optimization of non-traditional machining processes

Optimization of Wire EDM Process Parameters for Machining of AMCs (413/B4c) using Taguchi Technique

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

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Optimization of Micro-WEDM Process of Aluminum Matrix Composite (A413-B4C): A Response Surface Approach

Cited by 67 publications

References 18 publications

A multivariate quality loss function approach for parametric optimization of non-traditional machining processes

A multivariate quality loss function approach for parametric optimization of non-traditional machining processes

Optimization of Wire EDM Process Parameters for Machining of AMCs (413/B4c) using Taguchi Technique

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

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Optimization of Micro-WEDM Process of Aluminum Matrix Composite (A413-B₄C): A Response Surface Approach