Taguchi-fuzzy inference system and grey relational analysis to optimise process with multiple performance characteristics in precision turning of Ti-6AL-4V

2016 ◽  
Vol 8 (3) ◽  
pp. 295
Author(s):  
R. Vinayagamoorthy ◽  
M. Anthony Xavior
Keyword(s):  
Grey Relational Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Performance Characteristics ◽  
Inference System ◽  
Precision Turning ◽  
Relational Analysis ◽  
Grey Relational

Optimization of Cutting Parameters of Multiple Performance Characteristics in End Milling of AlSi/AIN MMC – Taguchi Method and Grey Relational Analysis

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
S. H. Tomadi ◽  
J. A. Ghani ◽  
C. H. Che Haron ◽  
M. S. Kasim ◽  
A. R. Daud
Keyword(s):  
Taguchi Method ◽  
Grey Relational Analysis ◽  
Volume Fraction ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

The main objective of this paper is to investigate and optimize the cutting parameters on multiple performance characteristics in end milling of Aluminium Silicon alloy reinforced with Aluminium Nitride (AlSi/AlN MMC) using Taguchi method and Grey relational analysis (GRA). The fabrication of AlSi/AlN MMC was made via stir casting with various volume fraction of particles reinforcement (10%, 15% and 20%). End milling machining was done under dry cutting condition by using two types of cutting tool (uncoated & PVD TiAlN coated carbide). Eighteen experiments (L18) orthogonal array with five factors (type of tool, cutting speed, feed rate, depth of cut, and volume fraction of particles reinforcement) were implemented. The analysis of optimization using GRA concludes that the better results for the combination of lower surface roughness, longer tool life, lower cutting force and higher material removal could be achieved when using uncoated carbide with cutting speed 240m/min, feed 0.4mm/tooth, depth of cut 0.3mm and 15% volume fraction of AlN particles reinforcement. The study confirmed that with a minimum number of experiments, Taguchi method is capable to design the experiments and optimized the cutting parameters for these performance characteristics using GRA for this newly develop material under investigation.

Multiple Performance Optimization in WEDM Parameters Using Grey Relational Analysis

2015 ◽  
Vol 813-814 ◽  
pp. 357-361
Author(s):  
T. Rajmohan ◽  
Gopi Krishna ◽  
Ankit Kumar Singh ◽  
A.P.V. Swamy Naidu
Keyword(s):  
Grey Relational Analysis ◽  
Performance Characteristics ◽  
304L Stainless Steel ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Grey Relational ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process ◽  
Off Time

In this investigation, a new approach is based on Grey Relational Analysis and Taguchi method to optimize the machining parameters with multi performance characteristics in WEDM of 304L SS. Experiments are conducted using Taguchi Quality Concept, L9,3-level orthogonal array was chosen for experiments .The WEDM parameters namely pulse-on time (TON), pulse-off time (TOFF), and wire feed (WF) on material removal rate (MRR) .The Grey Relational Analysis with multiple performance characteristics indicates that the pulse-on time (TON), pulse-off time (TOFF) are the most significant factors . The optimum machining parameters have been identified by Grey relational analysis and significant contribution of parameters can be determined by analysis of variance (ANOVA). The confirmation test is also conducted to validate the test result. The results from this study will be useful for manufacturing engineers to select appropriate WEDM process parameters to machine 304L Stainless Steel.

scholarly journals A Hybrid Grey Relational Analysis and Nondominated Sorting Genetic Algorithm-II for Project Portfolio Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Farshad Faezy Razi
Keyword(s):  
Genetic Algorithm ◽  
Environmental Risk ◽  
Grey Relational Analysis ◽  
Decision Problem ◽  
Project Selection ◽  
Optimal Portfolio ◽  
Inference System ◽  
Relational Analysis ◽  
Grey Relational ◽  
Nondominated Sorting

Project selection and formation of an optimal portfolio of selected projects are among the main challenges of project management. For this purpose, several factors and indicators are simultaneously examined considering the terms and conditions of the decision problem. Obviously, both qualitative and quantitative factors may influence the formation of a portfolio of projects. In this study, the projects were first ranked using grey relational analysis to form an optimal portfolio of projects and to create an expert system for the final project selection. Because of the fuzzy nature of the environmental risk of each project, the environmental risk was predicted and analyzed using the fuzzy inference system and failure mode and effect analysis based on fuzzy rules. Then, the rank and risk of each project were optimized using a two-objective zero-one mathematical programming model considering the practical constraints of the decision problem through the nondominated sorting genetic algorithm-II (NSGA-II). A case study was used to discuss the practical methodology for selecting a portfolio of projects.

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