scholarly journals Optimization of Process Parameters During Electrochemical Machining

2019 ◽  
Vol 8 (12) ◽  
pp. 2683-2687
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Research Work ◽  
Electrochemical Machining ◽  
Machining Processes ◽  
Electrode Gap ◽  
Optimization Of Process Parameters ◽  
Finishing Process ◽  
Nimonic 80A ◽  
The Right

Electrochemical machining is one of the most efficient machining processes due to its ability to produce completely stress-free machined components without any need of further finishing process. However, the right understanding of the effects of key factors during machining of various materials is very important to carry out the machining. It is one of the most efficient way of cutting present in modern era. This present paper deals with the electrochemical machining of Nimonic 80A. Design of the experiments are done by using response surface methodology to study the material removal rate and surface roughness. Process parameters such as voltage, tool feed rate, inter-electrode gap and electrolyte concentration has been optimized by using the ANOVA. The regression models are developed to be used as predictive tools. The confirmation test was conducted to validate the results achieved by GRA approach. This research work helps the industrialist for selecting parameters to attain desired outputs.

Optimization of Process Parameters for Electro-Chemical Machining of EN19

2019 ◽  
pp. 127-142 ◽  
Author(s):  
Divya Zindani ◽  
Nadeem Faisal ◽  
Kaushik Kumar
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Input Parameter ◽  
Electrochemical Machining ◽  
Pso Algorithm ◽  
Machining Process ◽  
Matrix Composites ◽  
Machining Performance ◽  
Electrode Gap ◽  
Optimization Of Process Parameters

Electrochemical machining (ECM) is a non-conventional machining process that is used for machining of hard-to-machine materials. The ECM process is widely used for the machining of metal matrix composites. However, it is very essential to select optimum values of input process parameters to maximize the machining performance. However, the optimization of the output process parameters and hence the machining performance is a difficult task. In this chapter an attempt has been made to carry out single and multiple optimization of the material removal rate (MRR) and the surface roughness (SR) for the ECM process of EN19 using the particle swarm optimization (PSO) technique. The input parameter considered for the optimization are electrolyte concentration (%), voltage (V), feed rate (mm/min), and inter-electrode gap (mm). The optimum value of MRR and SR as found using the PSO algorithm are 0.1847 cm3/min and 25.0612, respectively.

scholarly journals Optimization of advanced manufacturing processes using socio inspired cohort intelligence algorithm

2022 ◽  
Vol 13 ◽  
pp. 6
Author(s):  
Ishaan R. Kale ◽  
Mayur A. Pachpande ◽  
Swapnil P. Naikwadi ◽  
Mayur N. Narkhede
Keyword(s):  
Process Parameters ◽  
Removal Rate ◽  
Harmony Search ◽  
Optimization Techniques ◽  
Technological Advancement ◽  
Machining Processes ◽  
Bee Colony ◽  
Optimization Of Process Parameters ◽  
Advanced Machining ◽  
Linear And Nonlinear Constraints

The demand of Advanced Machining Processes (AMP) is continuously increasing owing to the technological advancement. The problems based on AMP are complex in nature as it consisted of parameters which are interdependent. These problems also consisted of linear and nonlinear constraints. This makes the problem complex which may not be solved using traditional optimization techniques. The optimization of process parameters is indispensable to use AMP's at its aptness and to make it economical to use. This paper states the optimization of process parameters of Ultrasonic machining (USM) and Abrasive water jet machining (AWJM) processes to maximize the Material Removal Rate (MRR) using a socio inspired Cohort Intelligent (CI) algorithm. The constraints involved with these problems are handled using static penalty function approach. The solutions are compared with other contemporary techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Modified Harmony Search (HS_M) and Genetic Algorithm (GA).

Investigations on Machining of Monel 400 Alloys Using Electrochemical Machining with Sodium Nitrate as Electrolyte

2014 ◽  
Vol 592-594 ◽  
pp. 467-472 ◽  
Author(s):  
M. Kalaimathi ◽  
G. Venkatachalam ◽  
Neil Pradeep Makhijani ◽  
Ankit Agrawal ◽  
M. Sivakumar
Keyword(s):  
Process Parameters ◽  
Sodium Nitrate ◽  
Removal Rate ◽  
Electrochemical Machining ◽  
Research Field ◽  
Electrode Gap ◽  
Conventional Machine ◽  
Monel 400 ◽  
Physical And Chemical ◽  
The Impact

Monel 400 is a cuprous nickel alloy which is very well-known for its resistivity towards physical and chemical strength. It is probably one of the hardest and most non-corrosive materials known in industrial as well as research field. These properties have enhanced its applications in various fields such as aerospace industries, marine industries, automotive industries etc. Monel 400 alloys are too hard to machine using conventional machine tools and methods as it work hardens rapidly on its surface. Authors concluded that electrochemical machining is the choice of machining of these materials. The present work is carried out to analyze the impact of ECM process parameters such as applied voltage (V), inter-electrode gap (IEG) and electrolyte concentration (EC) on material removal rate (MRR) and surface roughness (Ra). An aqueous sodium nitrate (NaNO3) is used as basic electrolyte in the electrochemical machining of Monel 400 alloys. Response surface methodology (RSM) based central composite design (CCD) is used as experimental strategy. Effects of process parameters as well as their interactions are analysed and the process parameters are optimized.

scholarly journals Effect on Material Removal Rate and Surface Finish in ECM Process When Machining Stainless Steel-316 with Cu Electrode

2019 ◽  
Vol 8 (4) ◽  
pp. 2933-2941
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Process Parameters ◽  
Surface Finish ◽  
Feed Rate ◽  
Electrolyte Concentration ◽  
Removal Rate ◽  
Electrochemical Machining ◽  
Machining Process ◽  
Machining Processes

Electrochemical Machining process is one of the popular non-traditional machining processes which is used to machine materials such as super alloys, Ti-alloys, stainless steel etc. Its working principle is based upon Faraday law of electrolysis. The aim of the present work is to optimize the ECM process parameters with the combination of SS 316 (job material) and Copper electrode (tool material). To explore the effect of ECM process parameters such as electrolyte concentration, voltage and current, feed rate on MRR and surface finish (Ra) of the job, total 27 experiments were conducted as per experimental scheme. The results of these experiments revealed that increase in electrolyte concentration decrease the mrr and surface roughness initially increases then decreases. Further, increase in current increases mrr initially and then decreases, surface roughness also increases. It is also noticed that increase in Feed rate mrr decreases and then increases, also surface roughness decreases then increases. Through RSM analysis it is found that the optimum conditions for maximum MRR, and minimum Surface roughness (Ra) is electrolyte concentration 150gm/lit, Voltage 13.5 V & feed 0.8 mm/min. The findings are discussed in the light of previous researches and subsequently conclusions are drawn.

scholarly journals The effects of the process parameters in electrochemical machining on the surface quality

2020 ◽  
Vol 3 (SI1) ◽  
pp. First
Author(s):  
Nguyen Thi Bich Nhung ◽  
Dao Thanh Liem ◽  
Truong Quoc Thanh
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Electrolyte Concentration ◽  
Removal Rate ◽  
Electrochemical Machining ◽  
Electrode Gap

Based on the number of previous studies, this study aims to investigate the effects of process parameters of an Electrochemical Machining process, which are electrolyte concentration, the voltage applied to the machine, feed rate of the electrode, and Inter-Electrode Gap between tool and workpiece. Aluminum samples of 25 mm diameter x 25 mm height and 30mm diameter x 25mm height of the tool is made up of copper with a circular cross-section with 2 mm internal hole. The design of the system is based on the Taguchi method. Here, the signal-to-noise (S/N) model, the analysis of variance (ANOVA) and regression analyses are applied to determine optimal levels and to investigate the effects of these parameters on surface quality. Finally, the experiments that use the optimal levels of machining parameters are conducted to verify the effects of the process parameters on the surface quality of the products. The results pointed out a set of optimal parameters of the ECM process. The Inter-Electrode Gap between the tool and workpiece has extremely effected on these Material Removal rates and surface roughness. The Material Removal Rate increases with diseases in Inter-Electrode Gap, and Ra diseases with diseases in Inter-Electrode Gap. The experimental results show that maximum Material Removal Rate has obtained with electrolyte concentration at 100 g/l, feed rate at 0.0375 mm/min, the voltage at 15V, and Inter-Electrode Gap at 0.5mm. The minimum Ra has obtained with electrolyte concentration at 80 g/l, feed rate at 0.0468 mm/min, the voltage at 10V, and Inter-Electrode Gap at 0.5mm. This result has led to need studies on these parameters in Electrochemical Machining, which are improving productivities and surface roughness of the products.   

Effect of Process Parameters on MRR and Surface Roughness in ECM of EN 31 Tool Steel Using WPCA

2017 ◽  
Vol 4 (2) ◽  
pp. 45-63 ◽  
Author(s):  
Milan Kumar Das ◽  
Tapan Kumar Barman ◽  
Kaushik Kumar ◽  
Prasanta Sahoo
Keyword(s):  
Surface Roughness ◽  
Tool Steel ◽  
Process Parameters ◽  
Electrolyte Concentration ◽  
Removal Rate ◽  
Electrochemical Machining ◽  
Machining Parameters ◽  
Electrode Gap ◽  
En 31 ◽  
Taguchi Orthogonal Design

Weighted principal component analysis is used to predict the optimal machining parameters for EN 31 tool steel in electrochemical machining for minimum surface roughness and maximum material removal rate based on L27 Taguchi orthogonal design. For this, multi-response performance index is calculated to derive an equivalent single objective function and then Taguchi method is used to optimize the process parameters. The separable influence of individual machining parameters and the interaction between these parameters are also investigated by using analysis of variance (ANOVA). Results show that the main significant factor on MRR and surface roughness is electrolyte concentration. The effects of process parameters viz. electrolyte concentration, voltage, feed rate and inter-electrode gap on MRR and surface roughness are also investigated using 3D surface and contour plots. Finally, the surface morphology is studied with the help of scanning electron microscopy (SEM) images.

Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm

2017 ◽  
pp. 111-129
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain ◽  
Sayak Mukhopadhyay
Keyword(s):  
Genetic Algorithm ◽  
Tool Steel ◽  
Process Parameters ◽  
Electrical Discharge Machining ◽  
Search Algorithm ◽  
Removal Rate ◽  
Research Work ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Machining Processes

Electrical Discharge Machining (EDM) is nontraditional machining processes applied for precise machining and developing intricate geometries on work materials which are difficult to machine by conventional process. The present research work emphases on the die sinking EDM of AISI P20 tool steel, to study the effect of machining parameters such as pulse on time (POT), pulse off time (POF), discharge current (GI) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) and Overcut (OC) using square-shaped Cu tool with Lateral flushing. The experimentation is performed using L27 orthogonal array and significant process parameters are ascertained using Regression analysis. The influence of the important process parameters on individual responses are detected using Cuckoo search algorithm. The present chapter is aimed at multi-response optimization i.e. higher MRR, lower Ra and minimum OC, which is conceded out using Genetic Algorithm.

Electro Chemical Machining of Aluminum-Boron Carbide-Nanographite Composites

2016 ◽  
Vol 852 ◽  
pp. 136-141 ◽  
Author(s):  
M. Sankar ◽  
A. Gnanavelbabu ◽  
K. Rajkumar ◽  
M. Mariyappan
Keyword(s):  
Boron Carbide ◽  
Process Parameters ◽  
Material Removal ◽  
Solid Lubricant ◽  
Removal Rate ◽  
Electrochemical Machining ◽  
Machining Process ◽  
Abrasive Particles ◽  
Optimization Of Process Parameters ◽  
Machining Optimization

Non-traditional machining process had made possible the machining of hard to cut materials. Among several non-traditional processes electrochemical machining has been given attention since there occurs no burrs or tool wear. Composites with nano reinforcements had outclassed their counterparts in terms of the properties shown by the nano composites. In the present work aluminium matrix has been reinforced with boron carbide and nano graphite which is added as a solid lubricant to improve tribological properties. The composite is subjected to electrochemical machining with a view of optimizing the process parameters. The process involves introducing abrasive particles while machining which aids in machining. Optimization of process parameters was based on the response surface methodology techniques with four independent input parameters such as voltage, current, electrolytic concentration and feed rate and ECM process performance in terms of material removal rate and overcut.

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.

A Multiobjective Genetic-Algorithm-Based Optimization of Micro-Electrical Discharge Drilling

2021 ◽  
pp. 676-698
Author(s):  
Deepak Rajendra Unune ◽  
Amit Aherwar
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Inconel 718 ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Machining Processes ◽  
Micro Electrical Discharge Machining ◽  
Electrical Discharge Drilling ◽  
Machining Rate

Inconel 718 superalloy finds wide range of applications in various industries due to its superior mechanical properties including high strength, high hardness, resistance to corrosion, etc. Though poor machinability especially in micro-domain by conventional machining processes makes it one of the “difficult-to-cut” material. The micro-electrical discharge machining (µ-EDM) is appropriate process for machining any conductive material, although selection of machining parameters for higher machining rate and accuracy is difficult task. The present study attempts to optimize parameters in micro-electrical discharge drilling (µ-EDD) of Inconel 718. The material removal rate, electrode wear ratio, overcut, and taper angle have been selected as performance measures while gap voltage, capacitance, electrode rotational speed, and feed rate have been selected as process parameters. The optimum setting of process parameters has been obtained using Genetic Algorithm based multi-objective optimization and verified experimentally.

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