scholarly journals Optimization of process parameters in machining of nimonic super-alloy on EDM using genetic algorithm

2020 ◽  
Vol 2 (1) ◽  
pp. 35-44
Author(s):  
Madderla Sandhya ◽  
D. Ramasamy ◽  
Irshad ahamad Khilji ◽  
Anil Kumar ◽  
S. Chandramouli ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Process Parameters ◽  
Significant Influence ◽  
Optimal Choice ◽  
Copper Electrode ◽  
Taguchi Design Of Experiments ◽  
Optimization Of Process Parameters ◽  
Pulse On Time ◽  
Machining Characteristics

This project aims to investigate and predict the optimal choice for each EDM parameter using Taguchi Method by conducting a limited number of experiments on “Nimonic” Material. These parameters have a significant influence on the machining characteristics like MRR and TWR. Taguchi design of experiments (DOE) are implemented, particularly L9 orthogonal array is chosen and the effect of dominating process parameters is evaluated using analysis of variance. Nimonic refers to a family of Nickel-based high-temperature low creep superalloys. Due to its ability to withstand very high temperatures, Nimonic is ideal for typical applications such as aircraft parts, gas turbine components and blades, exhaust nozzles etc., for instance, where the pressure and heat are extreme. However, the conventional methods are not suitable to machine the hardest material such as Nimonic superalloy. The EDM, one of the popular unconventional machining methods, is used to the machine with a copper electrode, which in turn uses Taguchi methodology to analyze the effect of each parameter on the machining characteristics. The optimal choice for each EDM parameter such as peak current, gap voltage, duty cycle and pulse on time using the Taguchi method and Genetic Algorithm are identified. These parameters have a significant influence on machining characteristics such as MRR, EWR and surface roughness.

Modeling and Optimization of Process Parameters in Micro Wire EDM by Genetic Algorithm

2009 ◽  
Vol 76-78 ◽  
pp. 566-570 ◽  
Author(s):  
K.P. Somashekhar ◽  
N. Ramachandran ◽  
Jose Mathew
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Electric Discharge ◽  
Regression Models ◽  
Electric Discharge Machining ◽  
Optimization Of Process Parameters ◽  
Wire Electric Discharge Machining ◽  
Factor Regression ◽  
Wedm Process ◽  
Machining Characteristics

The present work is aimed at optimizing the parameters of micro Wire Electric Discharge Machining (µ-WEDM) process by considering the simultaneous effects of input parameters viz: gap voltage, capacitance and feed rate. Experiments were planned and conducted using DoE techniques. ANOVA was performed to find out the significance of each factor. Regression models were developed for the experimental results of surface roughness and overcut of the micro slots produced on aluminium. Then Genetic Algorithm (GA) was employed to determine the values of optimal process parameters for the desired output value of micro wire electric discharge machining characteristics.

scholarly journals Study of wire-electrical discharge machining parameters of titanium alloy by using taguchi method

2018 ◽  
Vol 7 (2.8) ◽  
pp. 10
Author(s):  
A VS Ram Prasad ◽  
Koona Ramji ◽  
B Raghu Kumar
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Process Parameters ◽  
Optimization Technique ◽  
Machining Process ◽  
Machining Parameters ◽  
Wire Edm ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Edm Process

Machining of Titanium alloys is difficult due to their chemical and physical properties namely excellent strength, chemical reactivity and low thermal conductivity. Traditional machining of such materials leads to formation of continuous chips and tool bits are subjected to chatter which leads to formation of poor surface on machined surface. In this study, Wire-EDM one of the most popular unconventional machining process which was used to machine such difficult-to-cut materials. Effect of Wire-EDM process parameters namely peak current, pulse-on- time, pulse-off-time, servo voltage on MRRand SR was investigated by Taguchi method. 0.25 mm brass wire was used in this process as electrode material. A surface roughness tester (Surftest 301) was used to measure surface roughness value of the machined work surface. A multi-response optimization technique was then utilized to optimize Wire-EDM process parameters for achieving maximum MRR and minimum SR simultaneously.

Machining Characteristics Estimation in WEDM Process While Machining Titanium Grade-2 Material Using ANN

2020 ◽  
Author(s):  
Prathik Jain Sudhir ◽  
Ravindra Holalu Venkatadas ◽  
Ugrasen Gonchikar
Keyword(s):  
Process Parameters ◽  
Machining Process ◽  
Electrode Wear ◽  
Training Set ◽  
Hard Materials ◽  
Titanium Grade ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process ◽  
Machining Characteristics

Abstract Wire Electrical Discharge Machining (WEDM) provides an effective solution for machining hard materials with intricate shapes. WEDM is a specialized thermal machining process is capable to accurately machining parts of hard materials with complex shapes. However, selection of process parameters for obtaining higher machining efficiency or accuracy in wire EDM is still not fully solved, even with the most up-to-date CNC WED machine. The study presents the machining of Titanium grade 2 material using L’16 Orthogonal Array (OA). The process parameters considered for the present work are pulse on time, pulse off time, current, bed speed, voltage and flush rate. Among these process parameters voltage and flush rate were kept constant and the other four parameters were varied for the machining. Molybdenum wire of 0.18mm is used as the electrode material. Titanium is used in engine applications such as rotors, compressor blades, hydraulic system components and nacelles. Its application can also be found in critical jet engine rotating and airframes components in aircraft industries. Firstly optimization of the process parameters was done to know the effect of most influencing parameters on machining characteristics viz., Surface Roughness (SR) and Electrode Wear (EW). Then the simpler functional relationship plots were established between the parameters to know the possible information about the SR and EW. This simpler method of analysis does not provide the information on the status of the material and electrode. Hence more sophisticated method of analysis was used viz., Artificial Neural Network (ANN) for the estimation of the experimental values. SR and EW parameters prediction was carried out successfully for 50%, 60% and 70% of the training set for titanium material using ANN. Among the selected percentage data, at 70% training set showed remarkable similarities with the measured value then at 50% and 60%.

Optimization of Process Parameters in Plasma Arc Cutting Applying Genetic Algorithm and Fuzzy Logic

2018 ◽  
pp. 123-139
Author(s):  
Nehal Dash ◽  
Apurba Kumar Roy ◽  
Sanghamitra Debta ◽  
Kaushik Kumar
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Plasma Arc ◽  
Plasma Arc Cutting ◽  
Optimization Of Process Parameters

Plasma Arc Cutting (PAC) process is a widely used machining process in several fabrication, construction and repair work applications. Considering gas pressure, arc current and torch height as the inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as factors that determines the quality, machining time and machining cost. In order to reduce the number of experiments Design of Experiments (DOE) would be carried out. In later stages applications of Genetic Algorithm (GA) and Fuzzy Logic would be used for Optimization of process parameters in Plasma Arc Cutting (PAC). The output obtained would be minimized and maximized for Surface Roughness and Material Removal Rate respectively using Genetic Algorithm (GA) and Fuzzy Logic.

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