Determination of Electro Discharge Machining Parameters in AISI202 Stainless Steel Using Grey Relational Analysis

Jegan, T. M. Chenthil; Anand, M. Dev; Ravindran, D.

doi:10.1016/j.proeng.2012.06.458

Cited by 21 publications

(12 citation statements)

References 4 publications

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“…The peak current is represented by the maximum level that is reached during the on-time of each pulse. This parameter has a direct effect on the material removal rate (MRR), tool wear rate (TWR) and machining accuracy [56,57,58,59,60,61]. These characteristics make it very important and has resulted in research into high wear resistance that can occur with high current conditions [47].…”

Section: General View Of the Edm Methodsmentioning

confidence: 99%

“…Jegan, et al [56] studied the effect of the machining parameters, including discharge current, pulse on-time and pulse off-time on the performance of EDM when machining AISI 202 stainless steel. They utilized the grey relational analysis to find the optimal performance parameters such as MRR and SR. Rajmohan, et al [157] studied the effect of EDM machining parameters such as pulse-on time, pulse-off time, voltage and current on MRR for 304 stainless steel signal to noise ratio (S/N) and analysis of variance (ANOVA) was used.…”

Section: Research On Edm Of Stainless Steelmentioning

confidence: 99%

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Advanced Electric Discharge Machining of Stainless Steels: Assessment of the State of the Art, Gaps and Future Prospect

et al. 2019

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Electric discharge machining (EDM) is a material removal process that is especially useful for difficult-to-cut materials with complex shapes and is widely used in aerospace, automotive, surgical tools among other fields. EDM is one of the most efficient manufacturing processes and is used to achieve highly accurate production. It is a non-contact thermal energy process used to machine electrically conductive components irrespective of the material’s mechanical properties. Studies related to the EDM have shown that the process performance can be considerably improved by properly selecting the process material and operating parameters. This paper reviews research studies on the application of EDM to different grades of stainless steel materials and describes experimental and theoretical studies of EDM that have attempted to improve the process performance, by considering material removal rate, surface quality and tool wear rate, amongst others. In addition, this paper examines evaluation models and techniques used to determine the EDM process conditions. This review also presents a discussion on developments in EDM and outlines the likely trend for future research.

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Section: General View Of the Edm Methodsmentioning

confidence: 99%

Section: Research On Edm Of Stainless Steelmentioning

confidence: 99%

Advanced Electric Discharge Machining of Stainless Steels: Assessment of the State of the Art, Gaps and Future Prospect

et al. 2019

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“…GRA has been successfully utilized to many fields. [21][22][23][24][25] There are three main steps in the grey relational analysis. The first step is grey relational generating.…”

Section: Static and Fatigue Testing Simulation By Finite Element Methodsmentioning

confidence: 99%

Optimization design of rail clip in Vossloh fastening system by uniform design and grey relation analysis

Cheng

Kuo

Lee

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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This study presents the innovative optimization design procedure to increase the strength and fatigue safety of a fastening system under the static and fatigue loading. Applying the uniform design of experiment, a group of simulation experiment is generated. Utilizing ANSYS/Workbench software, the finite element models are constructed and used to evaluate the von Mises stress of rail clip for EN 13146-1 and EN 13146-2 testing simulations. Furthermore, the fatigue safety factor of rail clip for EN 13146-4 testing simulation is also presented. By the Kriging interpolation method, the surrogate models are rebuilt by the input and output data from the uniform design. Applying grey relational analysis, entropy weighting method, genetic algorithm and Hooke–Jeeves algorithm, the improvement design version is presented by solving the multi-objective optimization problem. Compared with the original design, the maximum von Mises stress of rail clip has been decreased to 25.189 MPa, the fatigue safety factor is upgraded to 3.829. As a result, the strength and fatigue safety of the rail clip in the fastening system is increased.

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“…The effect of EDM on surface characteristics and machining damage of AISI D2 tool steel have been studied by Guu et al [6]. Chenthil et al [7] studied that unconventional machining processes are used only when no other traditional machining process can meet the necessary requirements efficiently and economically. Determination of machining parameters is essential for the effective utilization of these processes.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Electric Discharge Machining of M2 Tool Steel Using Grey Relational Analysis

Purohit

Rana

Dwivedi

et al. 2015

Materials Today: Proceedings

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Determination of Electro Discharge Machining Parameters in AISI202 Stainless Steel Using Grey Relational Analysis

Cited by 21 publications

References 4 publications

Advanced Electric Discharge Machining of Stainless Steels: Assessment of the State of the Art, Gaps and Future Prospect

Advanced Electric Discharge Machining of Stainless Steels: Assessment of the State of the Art, Gaps and Future Prospect

Optimization design of rail clip in Vossloh fastening system by uniform design and grey relation analysis

Optimization of Electric Discharge Machining of M2 Tool Steel Using Grey Relational Analysis

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