Optimization of Electrical Discharge Machining Process Using an Adaptive Controller Based on Fuzzy Logic

Abstract

Electrical Discharge Machining (EDM) is a manufacturing process whereby a desired shape is obtained by using electrical discharges. Material is removed from the work piece by a series of rapidly recurring current discharges between two electrodes, separated by a dielectric liquid and subject to an electric voltage. The EDM process has several advantages over conventional machining processes, key among them being the capability to machine very hard materials and cut complex internal profiles. It is also used to machine micro-parts with high dimensional accuracy and surface finish. The EDM mechanism is however very complex mainly due to the many machining parameters involved. The EDM process has some disadvantages such as high rate of electrical energy consumption, low material removal rate, high rate of tool wear and poor surface finish when not properly controlled. These disadvantages have undermined the full potential of EDM. Various researchers have used varied approaches with the aim of optimizing the EDM process for improved efficiency and quality. However, most of these researches have focused on optimization of at most two parameters and have used either predictive neural fuzzy techniques or modeling approaches. These approaches have not addressed the realtime control of the process which could guarantee maximum machining efficiency and high surface quality. In view of this, the main goal of this research was to study the EDM process with a view to designing a fuzzy-based controller that is capable of improving the process’ performance by increasing material removal rate, lowering tool wear rate and improving the quality of the surface finish. This would be achieved by optimizing the gap voltage and the duty cycle in realtime. First, a transistorized pulse generation circuit for an EDM machine at JKUAT was developed. Then extensive experimental work was carried out to determine the effects of gap voltage and duty cycle on material removal rate, tool wear rate and surface quality for machining of aluminium, brass and medium carbon xvi steel. The data from the experimental results was then used in the creation of data/knowledge base for the fuzzy logic inference system. Based on this data, a Multi-Input Single-Output (MISO) adaptive controller for the optimization of the spark gap voltage/discharge current and duty cycle was developed. The optimization was achieved through the adjustment of the spark gap. The adaptive controller uses realtime monitoring and adjustment modules to detect any changes in the machining parameters and give corresponding voltage control signals to optimize the machining process based on the set parameters and the rule base created for the fuzzy logic controller. Thus the controller continually monitors the actual machining parameters across the electrodes during machining and compares the difference with the optimum values. It also monitors the rate of change of the parameters. The difference between the measured and the optimum values and the rate at which the difference is varying is used to compute the input signals to the machine controllers. To test the performance of the proposed controller, the MRR, TWR and surface finish of the machined part for the controlled process were compared with those of the uncontrolled process. From this study, it was demonstrated that, the fuzzy logic based adaptive controller increased MRR by an average of 36.7%. Surface finish was improved by 54.5% and MRR to TWR ratio was increased by an average of 12.9%. The increase in MRR and MRR to TWR ratio through the use of the controller makes the EDM process suitable for applications not only in cases where machining of hard materials is needed, but also where faster machining is required. The application of the controller leads to higher productivity, reduced machining costs and wider applicability of the EDM process. Moreover, improved surface quality of the finished products makes the EDM process attractive for machining of dies and molds which require high accuracy and surface quality. Potential beneficiaries of the results obtained in this research include EDM machine manufacturers and specialized machining industries such as mold and die making industries.