Multi-Objective Optimisation and Analysis of EDM of AISI P20 Tool Steel

By: Dewangan, Shailesh KumarContributor(s): Biswas, C K [Supervisor] | Gangopadhyay, S [Supervisor] | Department of Mechanical EngineeringMaterial type: TextTextLanguage: English Publisher: 2014Description: 244 pSubject(s): Engineering and Technology | Mechanical Engineering | Production EngineeringOnline resources: Click here to access online Dissertation note: Thesis (Ph.D)- National Institute of Technology, Rourkela Summary: Electric Discharge Machining (EDM) is one of the non traditional machining processes used to produce critical shape on hard or brittle conductive materials and it can also be successfully applied on materials that are extremely difficult-to-machine using traditional machining processes. The experimental investigation of EDM process parameters is of utter importance in order to improve the productivity, surface integrity and quality characteristics. An efficient method for determining the optimum process parameters for multiple performance characteristics, through various multi-optimisation techniques from the experiment trials, is a necessity of the present industry. The work piece material for the current research work was AISI P20 tool steel and a cylindrical copper electrode was used with lateral flushing of dielectric fluid during the first phase of the study. AISI P20 tool steel has growing range of applications like in plastic moulds, frames for plastic pressure dies, hydro forming tools, which offer difficulty in conventional machining in hardened condition. Influence of various process parameters on MRR, TWR and OC has been investigated during EDMof AISI P20 tool steel. Different multi-objective optimisation techniques such as grey-Taguchi and fuzzy logic combined with Response Surface Methodology (RSM) have been utilized in order to achieve optimal combinations of EDM parameters like discharge current, pulse-on time, work time, lift time, and inter electrode gap which would result in maximum MRR as well as minimum TWR and OC. Working time did not have any influence on performance measures of EDM, while other parameters had significant effect. Both grey relation analysis and fuzzy logic technique have been implemented to convert multiple responses in EDM into a single one and optimise the above responses. Finally, respective confirmation tests were carried out to obtain optimal process parameters.
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Thesis (Ph.D)- National Institute of Technology, Rourkela

Electric Discharge Machining (EDM) is one of the non traditional machining processes used to produce critical shape on hard or brittle conductive materials and it can also be successfully applied on materials that are extremely difficult-to-machine using traditional machining processes. The experimental investigation of EDM process
parameters is of utter importance in order to improve the productivity, surface integrity and quality characteristics. An efficient method for determining the optimum process parameters for multiple performance characteristics, through various multi-optimisation techniques from the experiment trials, is a necessity of the present industry. The work piece material for the current research work was AISI P20 tool steel and a cylindrical copper electrode was used with lateral flushing of dielectric fluid during the first phase of the study. AISI P20 tool steel has growing range of applications like in plastic moulds, frames for plastic pressure dies, hydro forming tools, which offer difficulty in conventional machining in hardened condition. Influence of various process parameters on MRR, TWR and OC has been investigated during EDMof AISI P20 tool steel. Different multi-objective optimisation techniques such as grey-Taguchi and fuzzy logic combined with Response Surface Methodology (RSM) have been utilized in order to achieve optimal combinations of EDM parameters like discharge current, pulse-on time, work time, lift time, and inter electrode gap which would
result in maximum MRR as well as minimum TWR and OC. Working time did not have any influence on performance measures of EDM, while other parameters had significant effect. Both grey relation analysis and fuzzy logic technique have been implemented to convert multiple responses in EDM into a single one and optimise the above responses. Finally, respective confirmation tests were carried out to obtain optimal process parameters.

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