Abstract
AISI-D6 steel is widely used in the creation of dies and molds. In the present paper, first the electrical discharge machining (EDM) of the aforementioned material is performed with a testing plan of 32 trials. Then, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were applied to predict the outputs. The effects of some significant operational parameters—specifically pulse on-time (Ton), pulse current (I), and voltage (V)—on the performance measures of EDM processes such as the material removal rate (MRR), tool wear ratio (TWR), and average surface roughness (Ra) are extracted. To lead the process operators, process plans (i.e., parameter–effect correlations) are created. The outcomes exposed the upper values of pulse on-time caused by higher amounts of MRR and Ra, and likewise lower volumes of TWR. Furthermore, growing the pulse current resulted in upper volumes of the material removal rate, tool wear ratio, and surface roughness. Besides, the higher input voltage resulted in a lower amount of MRR, TWR, and Ra. The estimation models developed by using experimental data recounting MRR, TWR, and Ra. The root means the square error was used to determine the error of training models. Furthermore, the estimated outcomes based on the models have been proven with an unseen validation set of experiments. They are found to be in decent agreement with the experimental issues. The investigation shows the powerful learning capability of an ANFIS model and its advantage in terms of modeling complex linear machining processes.
Highlights
The difficulty in treating hard-to-cut materials impacted the start of numerous progressive machining approaches such as water jet machining, electric discharge machining, laser machining, and electrochemical machining
The results showed that the material removal rate (MRR) increased with an increase in the Crystals 2022, 12, 343 discharge current and pulse on-time, and the lower surface roughness was obtained at the initial conditions of the discharge current and pulse-on time
The results for this study showed that the adaptive neuro-fuzzy inference system (ANFIS) model with 21 rules was the best
Summary
The difficulty in treating hard-to-cut materials impacted the start of numerous progressive machining approaches such as water jet machining, electric discharge machining, laser machining, and electrochemical machining. These processes, commonly termed nontraditional machining procedures, create energy to remove residual material from the stock to create the preferred portion. Of these procedures, electric discharge machining (EDM) has received ample attention from the nuclear, aerospace, and automobile subdivisions [1]. The EDM procedure has found numerous uses such as fabrication of molds/dies and the cutting of holes in a variety of materials containing metals and composites [2] by vast distance to diameter ratio
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