Optimization of Process Parameters for Submerged Arc Welding by Weighted Principal Component Analysis Based Taguchi Method

2012 ◽  
Vol 622-623 ◽  
pp. 45-50 ◽  
Author(s):  
Joydeep Roy ◽  
Bishop D. Barma ◽  
J. Deb Barma ◽  
S.C. Saha
Keyword(s):  
Principal Component Analysis ◽  
Taguchi Method ◽  
Arc Welding ◽  
Principal Component ◽  
Traverse Speed ◽  
Component Analysis ◽  
Submerged Arc Welding ◽  
Bead Geometry ◽  
Weighted Principal Component ◽  
Submerged Arc

In submerged arc welding (SAW), weld quality is greatly affected by the weld parameters such as welding current, traverse speed, arc voltage and stickout since they are closely related to weld joint. The joint quality can be defined in terms of properties such as weld bead geometry and mechanical properties. There are several control parameters which directly or indirectly affect the response parameters. In the present study, an attempt has been made to search an optimal parametric combination, capable of producing desired high quality joint in submerged arc weldment by Taguchi method coupled with weighted principal component analysis. In the present investigation three process variables viz. Wire feed rate (Wf), stick out (So) and traverse speed (Tr) have been considered and the response parameters are hardness, tensile strength (Ts), toughness (IS).

scholarly journals COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS

Kursor ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annisa Eka Haryati ◽  
Sugiyarto Sugiyarto ◽  
Rizki Desi Arindra Putri
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Subtractive Clustering ◽  
Validity Index ◽  
Fuzzy Clustering Method ◽  
Dependent Variables ◽  
Better Than

Multivariate statistics have related problems with large data dimensions. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce data dimensions consisting of several dependent variables while maintaining variance in the data. PCA can be used to stabilize measurements in statistical analysis, one of which is cluster analysis. Fuzzy clustering is a method of grouping based on membership values ​​that includes fuzzy sets as a weighting basis for grouping. In this study, the fuzzy clustering method used is Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) with a combination of the Minkowski Chebysev distance. The purpose of this study was to compare the cluster results obtained from the FSC and FCM using the DBI validity index. The results obtained indicate that the results of clustering using FCM are better than the FSC.

scholarly journals On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance-weighted principal component analysis

2016 ◽  
Vol 38 (3) ◽  
pp. 1208-1223 ◽  
Author(s):  
Francisco Jesús Martinez-Murcia ◽  
Meng-Chuan Lai ◽  
Juan Manuel Górriz ◽  
Javier Ramírez ◽  
Adam M. H. Young ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Brain Structure ◽  
Site Effects ◽  
Principal Component ◽  
Component Analysis ◽  
Weighted Principal Component ◽  
Structure Heterogeneity ◽  
The Brain
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