Optimization of laser-assisted jet electrochemical machining parameters by grey relational analysis and fuzzy logic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anup Malik ◽  
Neel Sanghvi
Keyword(s):  
Fuzzy Logic ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Electrochemical Machining ◽  
Machining Parameters ◽  
Taper Angle ◽  
Content Type ◽  
Relational Analysis ◽  
Grey Relational ◽  
Jet Electrochemical Machining

Purpose The purpose of this paper is to optimize the laser-assisted jet electrochemical machining parameters, namely, supply voltage, inter-electrode gap, duty cycle and electrolyte concentration during machining of WC-Co composite using grey relational analysis and fuzzy logic. Design/methodology/approach In this work, experiments were carried out as per the Taguchi methodology and an L16 orthogonal array was used to study the influence of various combinations of process parameters on material removal rate, hole taper angle and surface roughness height. As a dynamic approach, the multiple response optimization was carried out using grey relational analysis and fuzzy logic. Findings The process parameters were optimized using grey relational analysis and fuzzy logic for different machining conditions such as balanced manufacturing, high-speed manufacturing and high-quality manufacturing. The research documented in this paper can be scaled up for case studies regarding industrial applications to compare optimization methods for manufacturing processes that are already being carried out. Originality/value An attempt to optimize material removal rate, hole taper angle and surface roughness height together by a combined approach of grey relational analysis and fuzzy logic has not been previously done.

Modelling and Optimization of Machining parameters for Turning Automotive Shafts using RSM and Grey Relational Analysis

2020 ◽  
Vol 12 (4) ◽  
Author(s):  
S. Dinesh ◽  
K. Rajaguru ◽  
K. Saravanan ◽  
R. Yokeswaran ◽  
V. Vijayan
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Input Parameters ◽  
Grey Relational ◽  
Central Composite ◽  
Modelling And Optimization

Automotive shafts require maximum strength with regard to axial, bending and torsional loading to transmit power to various parts of a vehicle. Hence, it is very critical to analyse the manufacturing process and its governing parameters to exercise control over the surface properties of the shaft as it needs to be precisely manufactured in terms of dimensions and the surface roughness. The effect of three input parameters over two responses are considered as two major criteria's for production of shaft. The input parameters are speed, feed and depth of cut whereas the responses are material removal rate and surface roughness. Central Composite design was used and experimental results were analysed with Response Surface Methodology. ANOVA analysis was carried out to identify the most contributing parameter for MRR and SR. Grey Relational Analysis was adopted to identify the most feasible combination of machining parameters for turning process. The optimized parameter is identified as speed of 1000 rpm, 0.15 mm of feed and 0.35 mm of depth of cut using Grey Relational Analysis.

Optimization of Multiple Performances for EDM in Gas Media Using Grey Relational Analysis

2014 ◽  
Vol 620 ◽  
pp. 173-178
Author(s):  
Fang Pin Chuang ◽  
Yan Cherng Lin ◽  
Han Ming Chow ◽  
A. Cheng Wang
Keyword(s):  
Grey Relational Analysis ◽  
Electrical Discharge Machining ◽  
Design Method ◽  
Removal Rate ◽  
Performance Characteristics ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Grey Relational ◽  
Machining Parameter

The aim of this investigation is to optimize the multiple performance characteristics of electrical discharge machining (EDM) for SKD 61 tool steel in gas media using grey relational analysis. The three most important machining characteristics namely material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR) were considered as the measures of the performance characteristics. A series of experiments were conducted according to an L18 orthogonal array based on the Taguchi experimental design method. The observed data obtained from the experiments were evaluated to determine the optimization of machining parameters correlated with multiple performance characteristics through grey relational analysis. Moreover, analysis of variance (ANOVA) was conducted to explore the significant machining parameters crucially affecting the multiple performance characteristics. In addition, the optimal combination levels of machining parameters were also determined from the response graph of grey relational grades for each level of machining parameter.

Powder mixed electric discharge machining of high-speed steel T1 grade

2019 ◽  
Vol 15 (4) ◽  
pp. 699-713 ◽  
Author(s):  
Kanwal Jit Singh
Keyword(s):  
Electric Discharge ◽  
Grey Relational Analysis ◽  
High Speed ◽  
High Speed Steel ◽  
Machining Parameters ◽  
Electric Discharge Machining ◽  
Content Type ◽  
Relational Analysis ◽  
Input Parameters ◽  
Grey Relational

Purpose The purpose of this paper is to represent the innovative process of powder-mixed electrical discharge machining of high-speed steel T1 grade and to conduct experimental investigation to optimize the machining parameters associated with multiple performance characteristics using grey relational analysis. The machining of high-speed steel T1 grade via conventional machining is a difficult process. However, it can be easily machined by powder-mixed electric discharge machining. Design/methodology/approach Carefully selected machining parameters give the optimum output results. For experimentation, the following input parameters have been used: pulse on-time, discharge current, tool material and powder concentration. The effects of input parameters, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), have been investigated in this research. Findings Grey relational analysis and analysis of variance have been performed to optimize the input parameters for better output response. Optimized results show increment of TWR, MRR and SR, which is 63.24, 52.18 and 42.49 per cent, respectively. Originality/value This research paper will be beneficial for the industrial application. The GRA result gives the better output response.

Optimization of Multiple Response Characteristics of EDM Process Using Taguchi-Based Grey Relational Analysis and Modified PSO

2015 ◽  
Vol 14 (03) ◽  
pp. 123-148 ◽  
Author(s):  
S. Panda ◽  
D. Mishra ◽  
B. B. Biswal ◽  
P. Nanda
Keyword(s):  
Grey Relational Analysis ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Work Piece ◽  
Optimum Level ◽  
Machining Parameters ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational

Electrical discharge machining is an alternative process for machining complex and intricate shapes. In this paper, an inter-relationship of various electrical discharge machining parameters, namely discharge current, pulse on and off time and dielectric flow rate on material removal rate (MRR), tool wear rate (TWR), surface finish ( SR a) and dimensional tolerance using a Taguchi–Grey relational analysis. The response surface methodology is used to develop a second order model for MRR, TWR and SR a in terms of process parameters. Finally, a multi-objective optimization problem is formulated by using MRR, TWR and SR a. The multi-objective problem is then optimized through a modified particle swarm optimization (PSO) algorithm to find the optimum level of parameters. In this research, the results of the proposed method are validated through confirmation experiment. The work piece material used for experimentation is stainless steel of S304 grade.

Experimental Investigation of Electrochemical Micromachaning Process Parameters on Pure-Titanium Using Taguchi-Grey Relational Analysis

2016 ◽  
Vol 852 ◽  
pp. 198-204
Author(s):  
T. Geethapriyan ◽  
K. Kalaichelvan
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
Metal Removal ◽  
Removal Rate ◽  
Pure Titanium ◽  
Electrochemical Machining ◽  
Electrochemical Micromachining ◽  
Influence Of Process Parameters ◽  
Relational Analysis ◽  
Grey Relational

Non-conventional machine are nowadays plays a vital role in manufacturing complex shaped products and to produce the product with high accuracy the electrochemical machining is widely used to machine complicated shapes for electrically conducting difficult-to-machine materials such as super alloys, Ti-alloys, alloy steel, tool steel, stainless steel, etc. such titanium-based alloys are in common use for aero engine components such as blades and blisks (blade integrated disks). Therefore, in this present work to investigate the influence of some predominant electrochemical process parameters such as applied voltage, electrolyte concentration, Micro-tool feed rate and duty cycle on the metal removal rate , overcut and surface roughness to fulfill the effective utilization of electrochemical machining of Pure-titanium. The purpose of this study is to investigate the influence of process parameters on machining characteristics and optimize the combination of those parameters using Taguchi-grey relational analysis. From this result, it is observed that process parameters have significant role in Electrochemical Micromachining process and the optimization values has been found using proposed multi-response methodology.

Multi-Objective Optimization of EDM Parameters Using Grey Relational Analysis for Titanium Alloy (Ti–6Al–4V)

2014 ◽  
Vol 592-594 ◽  
pp. 540-544 ◽  
Author(s):  
A. Palanisamy ◽  
R. Rekha ◽  
S. Sivasankaran ◽  
C. Sathiya Narayanan
Keyword(s):  
Grey Relational Analysis ◽  
Removal Rate ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Edm Process

In this paper optimization of the electrical discharge machining (EDM) process with multiple performance characteristics based on the orthogonal array with the grey relational analysis was studied and investigated. A grey relational grade obtained from the grey relational analysis is used to solve the EDM process. Optimal machining parameters are determined by considering the grey relational grade as the performance index. The input independent parameters of peak current, pulse on time and pulse off time were examined and optimized on multiple response characters (material removal rate, electrode wear ratio and surface roughness). Experimental results have shown that machining performance in the EDM process can be improved effectively through this approach.

scholarly journals Optimization of Machining Parameters using ANOVA and Grey Relational Analysis while Turning Aluminium 7075

2019 ◽  
Vol 8 (2) ◽  
pp. 5682-5686
Keyword(s):  
Grey Relational Analysis ◽  
High Speed ◽  
Removal Rate ◽  
High Speed Steel ◽  
Depth Of Cut ◽  
Total Production ◽  
Machining Parameters ◽  
Turning Operation ◽  
Relational Analysis ◽  
Grey Relational

In this research a detailed study is carried out on machining parameters for turning operation on aluminium 7075 with high speed steel. This grade of aluminium is known for its applications in aerospace industry and research about its machining parameters will lead to more developments in the field of production. Aim of this work is to optimize turning operation. Machining parameters viz. speed, feed and depth of cut are taken as input parameters. Material removal rate (MRR), tool wear (TWR), surface roughness (SR) are taken as output parameters and the set of optimized parameters means reduction in total production cost. The experiments are planned using Taguchi’s L9 orthogonal array. Grey relational analysis (GRA) is used for multi objective optimization using grey relational grades. Application of analysis of variance(ANOVA) helps in the identification of most prominent parameters among speed, feed and depth of cut

Multi-Objective Optimization of Photochemical Machining Process Based on Grey Relational Analysis Method for Spray Etching

2014 ◽  
Vol 612 ◽  
pp. 77-82 ◽  
Author(s):  
D.P Agrawal ◽  
K.V. Gurav ◽  
D.N. Kamble
Keyword(s):  
Grey Relational Analysis ◽  
Removal Rate ◽  
Performance Measure ◽  
Machining Process ◽  
Optimum Level ◽  
Machining Parameters ◽  
Machining Performance ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational

Non-conventional process like Photochemical Machining (PCM) is found to show a promise for machining very thin metal components. In the present study, the effect of various selected parameters such as time of etching, temperature of etchant and concentration of etchant on material removal rate, undercut in PCM of phosphor bronze has been investigated by using multi-objective grey relational analysis and their optimal conditions are evaluated. Full factorial (L27) orthogonal array (DoE) has been used to perform the experiments. GRG value indicates most significant parameters affecting the PCM process. The above factors are selected on the basis of effect - cause analysis and literature survey. Mathematical models relating to the machining performance and machining parameters have been formulated. Optimal settings for each performance measure have also been obtained. The results obtained after conference test prove that improvement in the quality will take place is if the setting of parameters are done at optimum level predicted by multi-objective grey relational analysis. The ANN model is prepared to predict the result by training neural which can be compared with actual experiments to confirm the satisfactory performance during the experimentation.

Modeling of the machining parameters in turning of Al-5052/TiC/SiC composites: a statistical modeling approach using grey relational analysis (GRA) and response surface methodology (RSM)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkateshwar Reddy Pathapalli ◽  
Meenakshi Reddy Reddigari ◽  
Eswara Kumar Anna ◽  
P. Srinivasa Rao ◽  
D V. Ramana Reddy
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Metal Matrix ◽  
Machining Parameters ◽  
Matrix Composites ◽  
Content Type ◽  
Relational Analysis ◽  
Desirability Approach ◽  
Grey Relational ◽  
Grey Taguchi

PurposeMetal matrix composites (MMC) has been a section which gives an overview of composite materials and owing to those exceptional physical and mechanical properties, particulate-reinforced aluminum MMCs have gained increasing interest in particular engineering applications. Owing to the toughness and abrasive quality of reinforcement components such as silicon carbide (SiC) and titanium carbide (TiC), such materials are categorized as difficult materials for machining. The work aims to develop the model for evaluating the machinability of the materials via the response surface technique by machining three distinct types of hybrid MMCs.Design/methodology/approachThe combined effects of three machining parameters, namely “cutting speed” (s), “feed rate” (f) and “depth of cut” (d), together with three separate composite materials, were evaluated with the help of three performance characteristics, i.e. material removal rate (MRR), cutting force (CF) and surface roughness (SR). Response surface methodology and analysis of variance (ANOVA) both were initially used for analyzing the machining parameters results.FindingsThe contours were developed to observe the combined process parameters along with their correlations. The process variables were concurrently configured using grey relational analysis (GRA) and the composite desirability methodology. Both the GRA and composite desirability approach obtained similar results.Practical implicationsThe results obtained in the present paper will be helpful for decision-makers in manufacturing industries, who work on metal cutting area especially composites, to select the suitable solution by implementing the Grey Taguchi and modeling techniques.Originality/valueThe originality of this research is to identify the suitability of process parameters combination based on the obtained research results. The optimization of machining parameters in turning of hybrid metal matrix composites is carried out with two different methods such as Grey Taguchi and composite desirability approach.

scholarly journals Multi Performance Optimizationof Shoulder Milling Process Parameters of AA6063 T6 Aluminium Alloy by Taguchi Based GRA

2019 ◽  
Vol 8 (10S) ◽  
pp. 420-425
Keyword(s):  
Process Parameters ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Analysis Data ◽  
Milling Process ◽  
Depth Of Cut ◽  
Analysis Method ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

In this paper, a grey relational analysis method based on Taguchi is proposed to improve the multi-performance characteristics of VMC shoulder milling process parameters in the processing of AA6063 T6. Taking into account four process parameters such as coolant, depth of cut,speed and feed, there are three level of each process parameter in addition to two levels of coolant. 18 experiments were used by L18 orthogonal array using the taguchi method. Multi-performance features like surface roughness and material removal rate are used. Grey Relational Analysis method is used to obtain the Grey Relational Grade, and the multiperformance characteristics of the process are pointed out. Then, the Taguchi response table method and ANOVA are used to analysis data. In order to ensure the validity of the test results, a confirmation test was conducted. The study also shows that this method can effectively improve the multi-function characteristics of shoulder milling process.In his work microstructure and mechanical properties of AA6063 T6before and after shoulder milling have been investigated.

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