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
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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.