On the Influence of Electrical Discharge Drilling Parameters and Per-formance Measures of Inconel 718 Superalloy - a Study

In order to achieve higher productivity and product quality, the investigation of machining parameters on Electrical Discharge Drilling and surface characteristic analysis are most critical for manufacturing industries. The intention of this article is to assess the impact on performance matrices including Material Removal Rate, and Surface Roughness of input factors of peak current, pulse-on and off duration while drilling with a rotary hollow copper tool on Inconel 718 under Tungsten powder suspended kerosene. Analysis of Variance has been implemented using MINITAB release 18 software to identify the most significant input factors. An Artificial Neural Network was used for validating the experimental results of the drilling process. The additional intention of this research is to discover the significance of influencing input parameters and analyze the quality surface of the workpiece were observed by microscope tests. The experimental results indicated that the peak current and pulse-on period have an effect on the performance of the drilling process considerably.

A Multiobjective Genetic-Algorithm-Based Optimization of Micro-Electrical Discharge Drilling

Inconel 718 superalloy finds wide range of applications in various industries due to its superior mechanical properties including high strength, high hardness, resistance to corrosion, etc. Though poor machinability especially in micro-domain by conventional machining processes makes it one of the “difficult-to-cut” material. The micro-electrical discharge machining (µ-EDM) is appropriate process for machining any conductive material, although selection of machining parameters for higher machining rate and accuracy is difficult task. The present study attempts to optimize parameters in micro-electrical discharge drilling (µ-EDD) of Inconel 718. The material removal rate, electrode wear ratio, overcut, and taper angle have been selected as performance measures while gap voltage, capacitance, electrode rotational speed, and feed rate have been selected as process parameters. The optimum setting of process parameters has been obtained using Genetic Algorithm based multi-objective optimization and verified experimentally.

A Mathematical Model to Assess the Material Removal Rate during Gas-Assisted Electrical Discharge Drilling Process

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.

Tool Wear and Material Removal Predictions in Micro-EDM Drilling: Advantages of Data-Driven Approaches

In micro electrical discharge drilling, regression models are commonly used for predicting the material removal rate (MRR) and tool wear rate (TWR) from the applied processing parameters. However, these models can be inaccurate since the processing parameters might not always be representative of the actual machining conditions, which depend on several other factors such as the tool length or gap flushing efficiency. In order to increase the prediction accuracy, the present work investigates the capability of data-driven regression models for tool wear and material removal prediction. The errors in predicting the MRR and TWR are shown to decrease of about 65% and 85% respectively when using data collected through process monitoring as input of the regression models. Data-driven approaches for in-process tool wear prediction have also been implemented in drilling experiments, demonstrating that a more accurate control of the hole depth (50% average reduction of the depth error) can be achieved by using data-driven predictive models.