scholarly journals Measurement of Process Parameters in Magneto-Rheological Fluid Assisted Cylindrical Surface Nano Finishing Process using Grey Relational Methods

2019 ◽  
Vol 9 (2) ◽  
pp. 3394-3400
Keyword(s):  
Cylindrical Surface ◽  
Removal Rate ◽  
Work Piece ◽  
Optimum Level ◽  
304L Stainless Steel ◽  
Machining Parameters ◽  
Finishing Process ◽  
Grey Relational Grade ◽  
Magneto Rheological ◽  
Grey Relational

Techniques for the analysis of machining parameters on cylindrical surface finish of 304L stainless steel with multiple response. It depends on quadratic pattern - (GRG) Grey Relational Grade is proposed in this paper. In this work, optimized the machining parameters such as working gap, Work-Piece Speed (WPS), and wheel speed rate and flew value are concluded the various responses such as Material Removal Rate (MRR), Normal Force (F-N), and surface roughness (Ra). Optimal process parameter is determined by Taguchi concept utilizing the GRG the performance index. And value of GRG used to recognize parameters optimum level. A antecedent of Variance (ANOVA) is used to resolve the augmentation of aspect r.

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.

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.

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.

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.

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.

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.

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.

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.

Modeling of Material Removal Mechanism in Micro Electric Discharge Milling of Ti-6Al-4V

2014 ◽  
Vol 592-594 ◽  
pp. 516-520 ◽  
Author(s):  
Basil Kuriachen ◽  
Jose Mathew
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Milling Process ◽  
Work Piece ◽  
Machining Parameters ◽  
Removal Mechanism ◽  
Material Removal Mechanism ◽  
Electric Discharge Milling

Micro EDM milling process is accruing a lot of importance in micro fabrication of difficult to machine materials. Any complex shape can be generated with the help of the controlled cylindrical tool in the pre determined path. Due to the complex material removal mechanism on the tool and the work piece, a detailed parametric study is required. In this study, the influence of various process parameters on material removal mechanism is investigated. Experiments were planned as per Response Surface Methodology (RSM) – Box Behnken design and performed under different cutting conditions of gap voltage, capacitance, electrode rotation speed and feed rate. Analysis of variance (ANOVA) was employed to identify the level of importance of machining parameters on the material removal rate. Maximum material removal rate was obtained at Voltage (115V), Capacitance (0.4μF), Electrode rotational Speed (1000rpm), and Feed rate (18mm/min). In addition, a mathematical model is created to predict the material removal

Process Optimization in Non-Conventional Processes

2017 ◽  
pp. 82-119
Author(s):  
Milan Kumar Das ◽  
Tapan Kumar Barman ◽  
Prasanta Sahoo ◽  
Kaushik Kumar
Keyword(s):  
Process Parameters ◽  
Morphological Study ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Work Piece ◽  
Bee Colony ◽  
Hard Metals ◽  
Optimal Values ◽  
Grey Relational ◽  
Conventional Machining

Conventional machining becomes non-efficient and non-effective in case of intricate shape and also while working with hard metals and alloys due to excessive tool wear. In such situations non-conventional machining, in contrast becomes more appropriate due to non-contact between tool and work-piece. In the present study, EN31 steel was machined using Plasma Arc Cutting with pre-defined process parameters. Material Removal Rate and Surface roughness were considered as responses for the study. The responses were optimized both as single and multi-response. Considering the complexities of this present problem, experimental data were generated and the results were analyzed by using Taguchi, Grey Relational Analysis and Artificial Bee Colony (ABC) Algorithm. Responses variances with the variation of process parameters were thoroughly studied and analyzed and ‘best optimal values' were identified. The result were verified by the morphological study. It was observed that there was an improvement in responses from mean to optimal values of process parameters.

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