Adaptive neuro-fuzzy interference system modelling of carbon nanotube-based electrical discharge machining process

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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Application of adaptive Neuro-fuzzy interference system, fuzzy interference system and least squares support vector machine for rapid simultaneous spectrophotometric determination of antipsychotic drugs in binary mixtures and biological fluid

Optik ◽

10.1016/j.ijleo.2021.166569 ◽

2021 ◽

Vol 232 ◽

pp. 166569

Author(s):

Mojdeh Alibakhshi ◽

Mahmoud Reza Sohrabi ◽

Mehran Davallo

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Spectrophotometric Determination ◽

Antipsychotic Drugs ◽

Biological Fluid ◽

Support Vector ◽

Fuzzy Interference System ◽

Interference System ◽

Neuro Fuzzy

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Fault diagnosis of micro energy grids using Bayesian belief network and adaptive neuro-fuzzy interference system

2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE) ◽

10.1109/sege.2017.8052790 ◽

2017 ◽

Cited By ~ 4

Author(s):

Yahya Koraz ◽

Hossam A. Gabbar

Keyword(s):

Fault Diagnosis ◽

Bayesian Belief Network ◽

Belief Network ◽

Fuzzy Interference System ◽

Interference System ◽

Neuro Fuzzy

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Multi-objective Optimization of Electrical Discharge Machining Process During Machining of NiTi Alloy Using Taguchi and Utility Concept

Techno-Societal 2018 ◽

10.1007/978-3-030-16962-6_49 ◽

2019 ◽

pp. 479-489

Author(s):

Vaibhav S. Gaikwad ◽

Vijaykumar S. Jatti ◽

Padmakar J. Pawar ◽

Keshav N. Nandurkar

Keyword(s):

Electrical Discharge ◽

Electrical Discharge Machining ◽

Niti Alloy ◽

Machining Process ◽

Multi Objective Optimization ◽

Multi Objective ◽

Utility Concept

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Experimental Study of Machining of Shape Memory Alloys Using Dry Micro Electrical Discharge Machining Process

Volume 4: Processes ◽

10.1115/msec2018-6573 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sagil James ◽

Sharadkumar Kakadiya

Keyword(s):

Shape Memory Alloys ◽

Shape Memory ◽

Electrical Discharge ◽

Material Removal ◽

Electrical Discharge Machining ◽

Removal Rate ◽

Dielectric Medium ◽

Machining Process ◽

Machining Processes ◽

Micro Electrical Discharge Machining

Shape Memory Alloys are smart materials that tend to remember and return to its original shape when subjected to deformation. These materials find numerous applications in robotics, automotive and biomedical industries. Micromachining of SMAs is often a considerable challenge using conventional machining processes. Micro-Electrical Discharge Machining is a combination of thermal and electrical processes, which can machine any electrically conductive material at micron scale independent of its hardness. It employs dielectric medium such as hydrocarbon oils, deionized water, and kerosene. Using liquid dielectrics has adverse effects on the machined surface causing cracking, white layer deposition, and irregular surface finish. These limitations can be minimized by using a dry dielectric medium such as air or nitrogen gas. This research involves the experimental study of micromachining of Shape Memory Alloys using dry Micro-Electrical Discharge Machining process. The study considers the effect of critical process parameters including discharge voltage and discharge current on the material removal rate and the tool wear rate. A comparison study is performed between the Micro-Electrical Discharge Machining process with using the liquid as well as air as the dielectric medium. In this study, microcavities are successfully machined on shape memory alloys using dry Micro-Electrical Discharge Machining process. The study found that the dry Micro-Electrical Discharge Machining produces a comparatively better surface finish, has lower tool wear and lesser material removal rate compared to the process using the liquid as the dielectric medium. The results of this research could extend the industrial applications of Micro Electrical Discharge Machining processes.

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