Multi-objective optimization of single point incremental sheet forming of AA5052 using Taguchi based grey relational analysis coupled with principal component analysis

2014 ◽  
Vol 15 (11) ◽  
pp. 2309-2316 ◽  
Author(s):  
Pandivelan Chinnaiyan ◽  
A. K. Jeevanantham
Keyword(s):  
Principal Component Analysis ◽  
Grey Relational Analysis ◽  
Single Point ◽  
Principal Component ◽  
Component Analysis ◽  
Incremental Sheet Forming ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational

scholarly journals An Integrated Method for Multi-Objective Optimization of Multi-Pass Fe50/TiC Laser Cladding on AISI 1045 Steel based on Grey Relational Analysis and Principal Component Analysis

Coatings ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 151 ◽  
Author(s):  
Qianting Wang ◽  
Xianbin Zeng ◽  
Changrong Chen ◽  
Guofu Lian ◽  
Xu Huang
Keyword(s):  
Principal Component Analysis ◽  
Laser Cladding ◽  
Grey Relational Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Relational Analysis ◽  
Aisi 1045 ◽  
Grey Relational

As an essentially multi-input multi-output process, determination of optimal conditions for laser cladding normally requires multi-objective optimization. To understand multi-response coupling, the effects of processing parameters on the morphology quality of multi-pass laser claddings of Fe50/TiC on medium carbon steel AISI 1045 were investigated based on composite central design using response surface methodology. Multiple responses, including clad width, flatness, and non-fusion area, were transformed into a single objective through grey relational analysis, with weights objectively identified by principal component analysis. The correlation between grey relational grade (GRG) and process parameters was established by regression analysis. The results show that the GRG response model has excellent goodness of fit and predictive performance. A validation experiment was conducted at the process condition optimized for maximum GRG. The relative error of the predicted optimal GRG is 4.87% whereas those of interested individual objectives, i.e. clad width, flatness, and non-fusion area, are 5.73%, 2.97%, and 6.73%, respectively, which verifies the accuracy of the established model. The investigation of mechanical properties suggests the hardness of substrate can be improved from 20 HRC to 60 HRC and wear resistance to over 8.14 times better.

Assessment of Water Quality Using Grey Relational Analysis and Principal Component Analysis

2011 ◽  
Vol 255-260 ◽  
pp. 2829-2835 ◽  
Author(s):  
Yong Qian Cheng ◽  
Hong Mei Ma ◽  
Qian Wu Song ◽  
Yue Zhang
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Grey Relational Analysis ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Component Analysis ◽  
Comprehensive Assessment ◽  
Relative Importance ◽  
Relational Analysis ◽  
Grey Relational

This paper investigates the comprehensive assessment of water quality, which is generally a multi-attribute assessment problem. In this context, the grey relational analysis is adopted to settle the no uniformity problem of water quality attributes. The principal component analysis is applied to calculate the weighting values corresponding to various attributes of water quality so that their relative importance can be properly and objectively described. Results of study reveal that grey relational analysis coupled with principal component analysis can effectively solve the multi-attribute water quality assessment. The method is universal and can be a useful tool to improve the comprehensive assessment of water quality.

Multi-Objective Optimization of EDM Process Parameters using Taguchi Method, Principal Component Analysis and Grey Relational Analysis

2014 ◽  
Vol 4 (2) ◽  
pp. 29-46 ◽  
Author(s):  
U. Shrinivas Balraj ◽  
A. Gopala Krishna
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Process Parameters ◽  
Grey Relational Analysis ◽  
Electrical Discharge Machining ◽  
Principal Component ◽  
Component Analysis ◽  
Optimum Process ◽  
Relational Analysis ◽  
Grey Relational

This paper investigates multi-objective optimization of electrical discharge machining process parameters using a new combination of Taguchi method and principal component analysis based grey relational analysis. In this study, three conflicting performance characteristics related to surface integrity such as surface roughness, white layer thickness and surface crack density are considered in electrical discharge machining of RENE80 nickel super alloy. The process parameters considered are peak current, pulse on time and pulse off time. The experiments are conducted based on Taguchi method and these experimental results are used in grey relational analysis and weights of the corresponding performance characteristics are determined by principal component analysis. The weighted grey relational grade is used as a performance index to determine optimum process parameters and results of the confirmation experiments indicate that the combined approach is effective in determining optimum process parameters.

Optimization of EDM Process with Magnetic Field Using Grey Relational Analysis Coupled with Principal Component Analysis

2015 ◽  
Vol 799-800 ◽  
pp. 388-392 ◽  
Author(s):  
G. Anand ◽  
M. Manzoor Hussian ◽  
S. Satyanarayana
Keyword(s):  
Principal Component Analysis ◽  
Grey Relational Analysis ◽  
Metal Removal ◽  
Removal Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Optimal Combination ◽  
Relational Analysis ◽  
Grey Relational ◽  
Edm Process

This paper investigates optimized design of Electro Discharge Machining process parameters on HCHCr i.e. DIN 17350-1.2080 Die steel. This process is one of the most widely applied non-traditional machining processes. To determine the optimal EDM conditions in several industrial fields. Taguchi method has been utilized to optimize only a single performance characteristic. To overcome this limitation, the Grey Relational Analysis theory has been used to determine grey relational grade as performance index to determine the optimal combination of the parameters such as peak current (I), pulse duration (Ton), voltage (V) to evaluate multiple performance characteristic such as metal removal rate and surface roughness simultaneously. Moreover, the Principal Component Analysis is applied to evaluate the weighting values corresponding to metal removal rate and surface roughness performance characteristics so that their relative importance can be properly defined. The analysis reveal that Grey Relational Analysis coupled with Principal Component Analysis can effectively be used to obtain the optimal combination of EDM process parameters. The obtained optimal machining conditions were Peak current at 15A, pulse on time at 250μs, Voltage at 85V. It is also observed that magnetic field in spark zone have improved metal removed rate and surface finish.

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