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

Abstract

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.

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

“…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.…”