Selecting Significant Process Parameters of ECG Process Using Fuzzy-MCDM Technique

2015 ◽  
Vol 2 (1) ◽  
pp. 38-53 ◽  
Author(s):  
Goutam Kumar Bose
Keyword(s):  
Surface Roughness ◽  
Removal Rate ◽  
Multiple Criteria Decision Making ◽  
Lower Surface ◽  
Substantial Improvement ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Performance ◽  
Grey Relational Grade ◽  
Grey Relational

The present paper highlights selection of significant machining parameters during Electrochemical grinding while machining alumina-aluminum interpenetrating phase composites by MCDM techniques. The conflicting responses like higher material removal rate, lower surface roughness, lower overcut and lower cutting force are ensured simultaneously by a single parametric combination. Control parameters like electrolyte concentration, voltage, depth of cut and electrolyte flow rate have been considered for experimentation. VIKOR is one of the multiple criteria decision making (MCDM) models to determine the reference ranking from a set of alternatives in the presence of conflicting criteria. Finally Grey Relational Analysis is performed to optimize multiple performances in which different levels combinations of the factors are ranked based on grey relational grade. Surface roughness is given more importance than other responses, using Fuzzy Set Theory considering basic objective of the process. It is observed that substantial improvement in machining performance takes place following this technique. The study highlights the effects of different process variables on multiple performances for complex process like ECG.

The Use of the Taguchi-Grey Based to Optimize High Speed End Milling with Multiple Performance Characteristics

2006 ◽  
Vol 505-507 ◽  
pp. 835-840 ◽  
Author(s):  
Shen Jenn Hwang ◽  
Yunn Lin Hwang ◽  
B.Y. Lee
Keyword(s):  
High Speed ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Performance ◽  
Grey Relational Grade ◽  
Grey Relational

This paper presents a new approach for the optimization of the high speed machining (HSM) process with multiple performance characteristics based on the orthogonal array with the grey relational analysis has been studied. Optimal machining parameters can then be determined by the grey relational grade as the performance index. In this study, the machining parameters such as cutting speed, feed rate and axial depth of cut are optimized under the multiple performance characteristics including, tool life, surface roughness, and material removal rate(MMR). As shown experimental results, machining performance in the HSM process can be improved effectively through this approach.

Multi-Objective Optimization of ECG Process Applying Soft Computing Techniques

2019 ◽  
pp. 68-98
Author(s):  
Pritam Pain ◽  
Goutam Kumar Bose
Keyword(s):  
Soft Computing ◽  
Removal Rate ◽  
Research Work ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Soft Computing Techniques ◽  
Response Optimization ◽  
Grey Relational ◽  
Electrochemical Grinding

The present research work focuses on the selection of significant machining parameters depending on the nature-inspired algorithm while machining alumina-aluminum interpenetrating phase composites during electrochemical grinding. Control parameters like electrolyte concentration (C), voltage (V), depth of cut (D) and electrolyte flow rate (F) have been considered for experimentation. The response data are initially trained and tested by using Artificial Neural Network. The contradictory responses like higher material removal rate (MRR), lower surface roughness (Ra), lower overcut (OC) and lower cutting force (Fc) are ensured individually by employing Firefly Algorithm. A multi-response optimization for all the responses is done initially by using the Genetic algorithm. Finally, in order to obtain a single set of parametric combination for all the output simultaneously fuzzy based Grey Relational Analysis technique is adopted. These natures driven soft computing techniques corroborates well during the parametric optimization of the electrochemical grinding process.

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.

Surface Roughness Prediction in End Milling of Machinable Glass Ceramic and Optimization by Response Surface Methodology

2011 ◽  
Vol 189-193 ◽  
pp. 1376-1381
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou El Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Glass Ceramic ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters

This paper presents the prediction of a statistically analyzed model for the surface roughness,R_a of end-milled Machinable glass ceramic (MGC). Response Surface Methodology (RSM) is used to construct the models based on 3-factorial Box-Behnken Design (BBD). It is found that cutting speed is the most significant factor contributing to the surface roughness value followed by the depth of cut and feed rate. The surface roughness value decreases for higher cutting speed along with lower feed and depth of cut. Additionally, the process optimization has also been done in terms of material removal rate (MRR) to the model’s response. Ideal combinations of machining parameters are then suggested for common goal to achieve lower surface roughness value and higher MRR.

scholarly journals Multiple Response Optimization of machining parameters on turning of AA 6063 T6 aluminum alloy which established on Taguchi L9 orthogonal array coupled with Grey relational analysis

2021 ◽  
pp. 974-982
Author(s):  
D. S. Sai Ravi Kiran ◽  
Sanapala Sri Ram ◽  
Tangeti Bhaskararao ◽  
Boddu Eswar Venkat Sai ◽  
Kari Suraj Kumar ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Orthogonal Array ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Dry Cutting ◽  
Grey Relational Grade ◽  
Grey Relational

With numerous responses established on Taguchi L9, orthogonal array coupled with current work proposes a novel methodology for optimizing machining parameters on turning of AA 6063 T6 aluminum alloy. Experimental assessments are accomplished on AA 6063 T6 aluminum alloy. Turning trails are carried out under dry cutting conditions using an uncoated carbide insert. Cutting parameters such as cutting speed, feed rate, and depth of cut are optimized in this effort while numerous responses such as surface roughness(Ra) and material removal rate are taken into consideration (MRR). From the grey analysis, a grey relational grade(GRG) is calculated. The optimal amounts of parameters have been identified based on the values of grey relational grade, and then ANOVA is used to determine the significant influence of parameters. To authenticate the test result, a confirmation test is executed. The result of the experiments shows that by using this method. the turning process responses can be significantly improved.

Influence of Tool Nose Radius and Cutting Performance during Face Milling of Magnesium Alloy for Output Responses by Taguchi based Grey Relation Analysis

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Durai Kumaran ◽  
S.P. Sundar Singh Sivam ◽  
Harshavardhana Natarajan ◽  
P.R. Shobana Swarna Ratna
Keyword(s):  
Removal Rate ◽  
Cutting Speed ◽  
Face Milling ◽  
Machining Process ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
New Approach ◽  
Grey Relational Grade ◽  
Grey Relational

In order to take advantage of the machining characteristics of magnesium, it is useful to consider recommended tool design and angles. The geometry of the tool can have a large influence on the machining process. Tool geometry can be used to aid with chip flow and clearance, reduce excessive heat generation, reduce tool build up, enable greater feed rates to be employed and improved tool life. This paper presents a new approach for the optimization of machining parameters on face milling of ZE41 with multiple responses based on orthogonal array with grey relational analysis. Machining tests are carried out by inserting 12 mm diameter of insert having 1 flute under dry condition. In this study, machining parameters namely cutting speed, feed and depth of cut and tool node radius are optimized with the considerations of multi responses such as surface roughness, material removal rate, tool wear and thrust force. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in Machining process can be improved effectively through the new approach.

scholarly journals Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics

2017 ◽  
Author(s):  
Shen Jenn Hwang ◽  
Yi-Hung Tsai
Keyword(s):  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Experimental Results ◽  
Depth Of Cut ◽  
Machining Performance ◽  
Radial Depth ◽  
Grey Relational Grade ◽  
Grey Relational

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for AA7050-T7451 by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of cutting variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is proved that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were performed to make a comparison between the experimental results. Experimental results have shown that machining performance in precision turn-boring process can be improved effectively through this approach

scholarly journals Multi-Optimization of Performance Parameters in Turn-Boring for Difficult-to-Cut Ti-6Al-4V

2017 ◽  
Author(s):  
Shen-Jenn Hwang ◽  
Xin-Tang Li
Keyword(s):  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Experimental Results ◽  
Depth Of Cut ◽  
Machining Performance ◽  
Radial Depth ◽  
Grey Relational Grade ◽  
Grey Relational

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for Ti-6Al-4V by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were carried out to make a comparison between the experimental results. Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this 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.

Multi Objective Optimization of Cutting Parameters in Machining Cellulose Based Hybrid Composites

2015 ◽  
Vol 761 ◽  
pp. 287-292
Author(s):  
Raja Izamshah ◽  
Zainudin Zuraidah ◽  
Mohd Shahir Kasim ◽  
M. Hadzley ◽  
M. Amran
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Hybrid Composites ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multi Objective Optimization ◽  
Machining Performance ◽  
Machined Surface ◽  
Multi Objective ◽  
Optimization Of Cutting Parameters

Cellulose based hybrid composites are gaining popularity in the growing green communities. With extensive studies and increasing applications for future advancement, the need for an accurate and reliable guidance in machining this type of composites has increased enormously. Smooth and defect free machined surface are always the ultimate objectives. The present work deals with the study of machining parameters (i.e. spindle speed, feed rate and depth of cut) and their effects on machining performance (i.e. surface roughness and delamination) to establish an optimized setup of machining parameters in achieving multi objective machining performance. Cellulose based hybrid composites consist of jute (a bast fiber) and glass fiber embedded in polyester resins. Response Surface Methodology (RSM) using Box-Behnken Design (BBD) was chosen as the design of experiment approach for this study. Based on that experimental approach, 17 experimental runs were conducted. Mathematical model for each response was developed based on the experimental data. Adequacy of the models were analyzed statistically using Analysis of Variance (ANOVA) in determining the significant input variables and possible interactions. The multi objective optimization was performed through numerical optimization, and the predicted results were validated. The agreement between the experimental and selected solution was found to be strong, between 95% to 96%, thus validating the solution as the optimal machining condition. The findings suggest that feed rate was the main factor affecting surface roughness and delamination .

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