Abstract
This research work investigates the use of Gas-Assisted Electrical Discharge Drilling (GAEDD) of high carbon-chromium die steel. The poor material removal rate (MRR) is one of the profound drawbacks of the traditional Electrical Discharge Drilling (EDD) process. Hence, over the years researchers have been feeling the requisite to develop an advanced strategy that can enhance the MRR. This study has examined the utilization of compressed gas in conventional EDM under the constraint state to assess MRR. The impact of procedure parameters likedischarge current, pulse on time, duty cycle, electrode speed, and discharge gas pressure, on MRR, has been explored too. In addition, Variance Analysis (ANOVA) was performed to determine the significant parameters affecting the MRR. During the examination, a mathematical model was established MRR employing Buckingham π-theorem while the GAEDD was being applied. The experiment and anticipated values of the model show a noteworthy impact of the coefficient of thermal expansion in GAEDD of high carbon-chromium steel. In addition, the Response Surface Method (RSM) model has also been evolved. The comparative analysis of the models developed shows considerable agreement in anticipation. Moreover, the semi-empirical model appears to be even more adaptable especially in comparison to the RSM-based model. In fact, the conclusion of this work is that the dimensional analysis model is an effective and reliable strategy to precise EDD response prediction.
Highlights
Electrical discharge drilling (EDD) is a commonly adopted non-traditional method of machining that absorbs heat from a spark and excretes material from a rigid and stiff object, something that cannot be machined by a traditional technique
Yahya and Manning (2004) analyzed the significant factors affecting the EDM operation. They notable parameters were ascertained by using the ANOVA and these parameters were considered to evolve a model to predict the material removal (MRR) during the EDM process
Yahya et al (2012) performed comparative studies of finding obtained through the predictive model based on Buckingham’s π-theorem and Artificial Neural Network (ANN) for the EDM operation
Summary
Electrical discharge drilling (EDD) is a commonly adopted non-traditional method of machining that absorbs heat from a spark and excretes material from a rigid and stiff object, something that cannot be machined by a traditional technique. Yahya and Manning (2004) analyzed the significant factors affecting the EDM operation They notable parameters were ascertained by using the ANOVA and these parameters were considered to evolve a model to predict the MRR during the EDM process. Patil and Brahmankar (2010) developed a mathematical model based on Buckingham’s π-theorem to assess the MRR during the wire-EDM (WEDM) process. Yahya et al (2012) performed comparative studies of finding obtained through the predictive model based on Buckingham’s π-theorem and Artificial Neural Network (ANN) for the EDM operation. Phate and Toney (2019) studied WEDM process responses by applying dimensional analysis and artificial neural network (ANN) techniques. In light of the above mentioned problems, the purpose of the contemporary investigation is to explore and establish a mathematical model for predicting MRR by introducing high-pressure argon gas via perforated electrode in conventional EDD
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