Prediction on Material Removal Rate in Electrical Discharge Machining Based Upon Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Author(s):  
Laith Abdullah Al-Juboori ◽  
Shukry Hamed
Keyword(s):  
Material Removal Rate ◽  
Fuzzy Inference System ◽  
Electrical Discharge ◽  
Material Removal ◽  
Fuzzy Inference ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 922 ◽  
Author(s):  
C. J. Luis Pérez
Keyword(s):  
Fuzzy Inference System ◽  
Electrical Discharge ◽  
Material Removal ◽  
Fuzzy Inference ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Operating Conditions ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Inference System

Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques.

Optimization of μEDM process assisted with rotating magnetic pulling force and ultrasonic vibration

2021 ◽  
pp. 095440892098440
Author(s):  
Gurpreet Singh ◽  
DR Prajapati ◽  
PS Satsangi
Keyword(s):  
Ultrasonic Vibration ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Machining Process ◽  
Micro Electrical Discharge Machining ◽  
Pulling Force ◽  
Tool Rotation

The micro-electrical discharge machining process is hindered by low material removal rate and low surface quality, which bound its capability. The assistance of ultrasonic vibration and magnetic pulling force in micro-electrical discharge machining helps to overcome this limitation and increase the stability of the machining process. In the present research, an attempt has been made on Taguchi based GRA optimization for µEDM assisted with ultrasonic vibration and magnetic pulling force while µEDM of SKD-5 die steel with the tubular copper electrode. The process parameters such as ultrasonic vibration, magnetic pulling force, tool rotation, energy and feed rate have been chosen as process variables. Material removal rate and taper of the feature have been selected as response measures. From the experimental study, it has been found that response output measures have been significantly improved by 18% as compared to non assisted µEDM. The best optimal combination of input parameters for improved performance measures were recorded as machining with ultrasonic vibration (U1), 0.25 kgf of magnetic pulling force (M1), 600 rpm of tool rotation (R2), 3.38 mJ of energy (E3) and 1.5 mm/min of Tool feed rate (F3). The confirmation trail was also carried out for the validation of the results attained by Grey Relational Analysis and confirmed that there is a substantial improvement with both assistance applied simultaneously.

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