Selection of Optimal EDM Process Parameters for Machining Maraging Steel Using Grey-Fuzzy Relational analysis—An Experimental Approach

Sameer, Mohammad; Reddy, B. Sai Kartheek; Amrutha, N.; Srishma, K.; Samantha, K.

doi:10.1007/978-981-16-7787-8_71

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Because of the complexity of the physical phenomena occurring during electrical discharge machining, a significant part of the research has focused on the development of predictive models for the process. One of the most frequently used methodologies that allow us to determine the relationship between the input factors and the results of process optimization are the response surface methodology [ 17 , 18 , 19 , 20 , 21 ], artificial neural networks [ 22 , 23 , 24 ], desirability functions [ 25 , 26 , 27 , 28 ], the fuzzy possibility approach [ 29 , 30 ], and gray relational analysis [ 31 , 32 , 33 ]. The study provided by Jatakar et al [ 34 ] shows that using the ANN algorithm can effectively diagnose and self-monitor complex manufacturing processes without human intervention.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Prediction Models of the Microwire EDM Process of Inconel 718 Using ANN and RSM Methods

2022

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Precise machining of micro parts from difficult-to-cut materials requires using advanced technology such as wire electrical discharge machining (WEDM). In order to enhance the productivity of micro WEDM, the key role is understanding the influence of process parameters on the surface topography and the material’s removal rate (MRR). Furthermore, effective models which allow us to predict the influence of the parameters of micro-WEDM on the qualitative effects of the process are required. This paper influences the discharge energy, time interval, and wire speed on the surface topography’s properties, namely Sa, Sk, Spk, Svk, and MRR, after micro-WEDM of Inconel 718 were described. Developed RSM and ANN model of the micro-WEDM process, showing that the discharge energy had the main influence (over 70%) on the surface topography’s parameters. However, for MRR, the time interval was also significant. Furthermore, a reduction in wire speed can lead to a decrease in the cost process and have a positive influence on the environment and sustainability of the process. Evaluation of developed prediction models of micro-WEDM of Inconel 718 indicates that ANN had a lower value for the relative error compared with the RSM models and did not exceed 4%.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Prediction Models of the Microwire EDM Process of Inconel 718 Using ANN and RSM Methods

2022

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Experimental Investigation of Technological Indicators and Surface Roughness of Hastelloy C-22 after Electrical Discharge Machining Using POCO Graphite Electrodes

et al. 2022

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Modern industry is focused on looking for new and effective technologies to manufacture complex shapes from alloys based on nickel and chromium. One of the materials widely used in the chemical and aerospace industry is Hastelloy C-22. This material is difficult to machine by conventional methods, and in many cases, unconventional methods are used to manufacture it, such as electrical discharge machining (EDM). In the EDM process, the material is removed by electrical discharges between a workpiece and a tool electrode. The physical and mechanical properties of the tool electrodes have a direct impact on the process efficiency, machining accuracy, and surface roughness. Currently, there has been a significant increase in the use of graphite as a material for tool electrodes due to the low purchase cost of the raw material, good machinability, and high sublimation temperature. In this work, an experimental investigation of the influence of the grain size of the graphite tool electrode on material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra) of Hastelloy C-22 was carried out. Two POCO graphite tool electrodes with a grain size of 1 µm (AF-5) and 10 µm (S-180) were used. Based on the experimental studies, empirical models describing the influence of machining parameters on technological indicators and the condition of the surface texture were determined. The research indicates that graphite with a larger grain provides higher process efficiency with high relative wear of the tool electrode. The lowest surface roughness was obtained for graphite with a smaller grain size (AF-5). The analysis of the machining parameters proves that the discharge current and pulse duration are the main factors determining the MRR and Ra values for both AF-5 and S-180 graphite. The time interval is the dominant parameter with regard to the relative wear of the graphite electrode.

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Selection of Optimal EDM Process Parameters for Machining Maraging Steel Using Grey-Fuzzy Relational analysis—An Experimental Approach

Cited by 2 publications

References 10 publications

Evaluation of Prediction Models of the Microwire EDM Process of Inconel 718 Using ANN and RSM Methods

Evaluation of Prediction Models of the Microwire EDM Process of Inconel 718 Using ANN and RSM Methods

Experimental Investigation of Technological Indicators and Surface Roughness of Hastelloy C-22 after Electrical Discharge Machining Using POCO Graphite Electrodes

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