Comparative study of expert predictive models based on adaptive neuro fuzzy inference system, nonlinear autoregressive exogenous and Hammerstein–Wiener approaches for electrical discharge machining performance: Material removal rate and surface roughness

2016 ◽  
Vol 230 (9) ◽  
pp. 1690-1701 ◽  
Author(s):  
Ehsan Roodgar Amoli ◽  
Saeed Salehinia ◽  
Majid Ghoreishi
Keyword(s):  
Predictive Models ◽  
Electrical Discharge ◽  
Material Removal ◽  
Fuzzy Inference ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Machining Performance ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Nonlinear Autoregressive

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.

Machining Feasibility of a New Developed Medium in Electrical Discharge Machining

2015 ◽  
Vol 656-657 ◽  
pp. 335-340 ◽  
Author(s):  
Fang Pin Chuang ◽  
Yan Cherng Lin ◽  
Hsin Min Lee ◽  
Han Ming Chow ◽  
A. Cheng Wang
Keyword(s):  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Environmentally Friendly ◽  
Industrial Applications ◽  
Deionized Water ◽  
Machining Parameters ◽  
Machining Performance ◽  
Machining Characteristics

The environment issue and green machining technique have been induced intensive attention in recent years. It is urgently need to develop a new kind dielectric to meet the requirements for industrial applications. The aim of this study is to develop a novel dielectric using gas media immersed in deionized water for electrical discharge machining (EDM). The developed machining medium for EDM can fulfill the environmentally friendly issue and satisfy the demand of high machining performance. The experiments were conducted by this developed medium to investigate the effects of machining parameters on machining characteristics in terms of material removal rate (MRR) and surface roughness. The developed EDM medium revealed the potential to obtain a stabilizing progress with excellent machining performance and environmentally friendly feature.

Influence of Material Microstructure on Micro EDM

2011 ◽  
Vol 130-134 ◽  
pp. 927-930
Author(s):  
Jian Zhong Li ◽  
Fei Hu Shen ◽  
Mei Gang Guo
Keyword(s):  
Material Properties ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Micro Edm ◽  
Machining Performance ◽  
Material Microstructure ◽  
Workpiece Material ◽  
Micro Holes

In conventional electrical discharge machining (EDM), the workpiece material is considered as homogenous material. When a micro feature is machined in alloy by micro EDM, microstructure of alloy may be of the same order as the micro feature. This may lead to the variation of machining performance of micro EDM. This paper demonstrates the influence of material microstructure on the machining performance by drilling micro holes within the crystal grain and on the boundary of TA0-1. Experimental results indicate that the average values of material removal rate, tool wear ratios and the discharge gaps on different locations are different because their material properties are different.

Micro-EDMing of SKD-5 die steel stimulated with magnetic field and ultrasonic vibration

2021 ◽  
pp. 095440892110504
Author(s):  
Gurpreet Singh ◽  
Vivek Sharma
Keyword(s):  
Magnetic Field ◽  
Ultrasonic Vibration ◽  
Material Removal Rate ◽  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Cumulative Effect ◽  
Machining Performance ◽  
Micro Electrical Discharge Machining

Electrical discharge machining is an essential process in the domain of micromachining. However, many issues need to be solved to implement it in the industrial field. Increasing the machining rate still remains a challenging task in case of micro electrical discharge machining. It becomes impossible to machine a microfeature at a larger depth. Numerous investigators have investigated the positive effect of assistance such as magnetic field and ultrasonic vibration. This paper the discusses machining performance by simultaneously applying the ultrasonic vibration and magnetic field to the machining zone in micro-electrical discharge machining. The process performance is analyzed by measuring the performance characteristics such as material removal rate and taper of the microfeature. The results confirmed that the cumulative effect of each assistance ends in a better material removal rate and low taper of the microfeature.

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