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.

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

Performance prediction of the electric discharge machining (EDM) process is the key to the overall study of EDM. Two approaches currently exist for predicting performance; one is an experimental approach and is dependent upon recurrent experiments while the other is a numerical approach with typically poor accuracy. A novel numerical method is proposed in this study to predict different materials’ EDM process performance under various conditions in an effective and economic way. This method is based on the specific discharge energy (SDE), a significant factor characterizing performance of materials in the EDM process. The materials with similar SDE exhibit similar performance under same conditions and materials with lower SDE tend to be removed quicker but with diminished performance. A numerical model of the EDM process considering the parameters-variant proportion of the energy distribution and equivalent temperature is established to compute the value of SDE and the model is compared with the previous models. The method is tested utilizing three different materials and is proved to be effective for predicting EDM performance, ultimately providing a convenient and accurate implication to guide manufacturing of various materials, especially new and/or expensive materials.

A novel numerical predicting method of electric discharge machining process based on specific discharge energy

Optimal selection of control parameters for automatic machining based on BP neural network

The wire electrical discharge machining (WEDM) process has inherent instability due to its high complexity and stochastic characteristics. For more stable and efficient machining, the process must be actively controlled in real time while precisely determining the state of the instantaneous machining process. Feedback information when used in conjunction with an unstable discharge pulse ratio and instantaneous discharge energy can be applied to this micro-control system. This study presents the design and implementation of a system that fulfills these demands. Experimental results demonstrate that the proposed system is capable of simultaneously enhancing the machining stability, machining efficiency, and machining performance. For fine machining, the surface quality was enhanced by approximately 10 % without a loss of machining efficiency. Overall, both the feedrate and the surface roughness could be improved concurrently by more than 5 %.

Experimental investigation of the real-time micro-control of the WEDM process

Concerning Drifts to Optimization Techniques of Wire-EDM Process for Titanium Based Super Alloys: A Review

In process planning of wire electrical discharge machining (WEDM), determination of appropriate machining conditions is likely to face problems in many ways. In addition to the construction of the relationship between machining parameters and machining characteristics, optimization search technique, a large number of experiments must be conducted repeatedly to renew parameters for different workpiece materials. The concept of specific discharge energy (SDE) was employed in this paper to represent the WEDM property of workpiece materials as one of the machining parameters. Two kinds of materials with distinctive SDE values, i.e., higher and lower, respectively, were selected for our experiments. The experimental data obtained were used, and a neural network that can accurately predict the relationship between machining parameters and machining characteristics was constructed. It was found that the predicted error was less than 7 %. The optimization technique of genetic algorithms was employed, and the optimal combination of machining parameters that meet the required machining characteristics for different workpiece materials was obtained. The system proposed in this study is both user-friendly and practical. It can save considerable time and cost during the construction of the database for the expert system of process planning.

Study of machining parameters optimization for different materials in WEDM

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Study of machining parameters optimization for different materials in WEDM