Electrical discharge machining (EDM) is a well-established machining option for manufacturing geometrically complex or hard material parts that are extremely difficult-to-machine by conventional machining processes. The non-contact machining technique has been continuously evolving from a mere tool and die making process to a micro-scale application machining alternative attracting a significant amount of research interests. In recent years, EDM researchers have explored a number of ways to improve the sparking efficiency including some unique experimental concepts that depart from the EDM traditional sparking phenomenon. Despite a range of different approaches, this new research shares the same objectives of achieving more efficient metal removal coupled with a reduction in tool wear and improved surface quality. This paper reviews the research work carried out from the inception to the development of die-sinking EDM within the past decade. It reports on the EDM research relating to improving performance measures, optimising the process variables, monitoring and control the sparking process, simplifying the electrode design and manufacture. A range of EDM applications are highlighted together with the development of hybrid machining processes. The final part of the paper discusses these developments and outlines the trends for future EDM research.

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State of the art electrical discharge machining (EDM)

https://escholarship.org/content/qt4h56k1ch/qt4h56k1ch.pdf

Advanced monitoring of machining operations

Wire electrical discharge machining (WEDM) is a specialised thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This practical technology of the WEDM process is based on the conventional EDM sparking phenomenon utilising the widely accepted non-contact technique of material removal. Since the introduction of the process, WEDM has evolved from a simple means of making tools and dies to the best alternative of producing micro-scale parts with the highest degree of dimensional accuracy and surface finish quality. Over the years, the WEDM process has remained as a competitive and economical machining option fulfilling the demanding machining requirements imposed by the short product development cycles and the growing cost pressures. However, the risk of wire breakage and bending has undermined the full potential of the process drastically reducing the efficiency and accuracy of the WEDM operation. A significant amount of research has explored the different methodologies of achieving the ultimate WEDM goals of optimising the numerous process parameters analytically with the total elimination of the wire breakages thereby also improving the overall machining reliability. This paper reviews the vast array of research work carried out from the spin-off from the EDM process to the development of the WEDM. It reports on the WEDM research involving the optimisation of the process parameters surveying the influence of the various factors affecting the machining performance and productivity. The paper also highlights the adaptive monitoring and control of the process investigating the feasibility of the different control strategies of obtaining the optimal machining conditions. A wide range of WEDM industrial applications are reported together with the development of the hybrid machining processes. The final part of the paper discusses these developments and outlines the possible trends for future WEDM research.

State of the art in wire electrical discharge machining (wedm)

Accuracy of machined components is one of the most critical considerations for any manufacturer. Many key factors like cutting tools and machining conditions, resolution of the machine tool, the type of workpiece etc., play an important role. However, once these are decided upon, the consistent performance of the machine tool depends upon its ability to accurately position the tool tip vis-a-vis the required workpiece dimension. This task is greatly constrained by errors either built into the machine or occurring on a periodic basis on account of temperature changes or variation in cutting forces. The three major types of error are geometric, thermal and cutting-force induced errors. Geometric errors make up the major part of the inaccuracy of a machine tool, the error caused by cutting forces depending on the type of tool and workpiece and the cutting conditions adopted. This part of the paper attempts to review the work done in analysing the various sources of geometric errors that are usually encountered on machine tools and the methods of elimination or compensation employed in these machines. A brief study of cutting-force induced errors and other errors is also made towards the end of this paper.

Error compensation in machine tools — a review: Part I: geometric, cutting-force induced and fixture-dependent errors

The state of a cutting tool is an important factor in any metal cutting process as additional costs in terms of scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage. Several methods to develop monitoring devices for observing the wear levels on the cutting tool on-line while engaged in cutting have been attempted. This paper presents a review of some of the methods that have been employed in tool condition monitoring. Particular attention is paid to the manner in which sensor signals from the cutting process have been harnessed and used in the development of tool condition monitoring systems (TCMSs).

Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods

Owing to the complexity of wire electrical discharge machining (wire-EDM), it is very difficult to determine optimal cutting parameters for improving cutting performance. The paper utilizes a feedforward neural network to associate the cutting parameters with the cutting performance. A simulated annealing (SA) algorithm is then applied to the neural network for solving the optimal cutting parameters based on a performance index within the allowable working conditions. Experimental results have shown that the cutting performance of wire-EDM can be greatly enhanced using this new approach.

Determination of optimal cutting parameters in wire electrical discharge machining

A neutral-network approach for the on-line monitoring of the electrical discharge machining process

In this paper, a neural network is used to construct the relationships between welding process parameters and weld pool geometry in tungsten inert gas (TIG) welding. An optimization algorithm called simulated annealing (SA) is then applied to the network for searching the process parameters with an optimal weld pool geometry. Finally, the quality of aluminum welds based on the weld pool geometry is classified and verified by a fuzzy clustering technique. Experimental results are presented to explain the proposed approach.

Modeling, optimization and classification of weld quality in tungsten inert gas welding

This paper presents a new mechanistic model to study the complex and highly nonlinear process damping force in chatter vibration. In the developed model, a feedforward neural network is used to model cutting force components. The process damping force due to the interface between the tool flank and machined surface is estimated through the calculation of the volume of the work material displaced by the tool flank. To properly calculate the volume of the displaced work material, the vibration of the tool relative to the workpiece is solved using the equations of motion iteratively until a convergence criterion is satisfied. The study has shown that the developed model is much better than previous models in the analysis of dynamic behaviors of the nonlinear process damping force in chatter vibration.

Modeling of the process damping force in chatter vibration

In this paper, the use of fuzzy set theory to construct a new pulse discriminator in electrical discharge machining (EDM) is reported. The classification of various discharge pulses in EDM is based on the features of the measured gap voltage and gap current. To obtain optimal classification performance, a machine learning method based on a simulated annealing algorithm is adopted to automatically synthesize the membership functions of the fuzzy pulse discriminator. Experimental results have shown that EDM discharge pulses can be not only correctly but also quickly classified under varying cutting conditions using this approach.

Y.S. Tarng

Papers

Determination of optimal cutting parameters in wire electrical discharge machining

A neutral-network approach for the on-line monitoring of the electrical discharge machining process

Modeling, optimization and classification of weld quality in tungsten inert gas welding

Modeling of the process damping force in chatter vibration

A fuzzy pulse discriminating system for electrical discharge machining