Using a principal components analysis for developing a robust design of electron beam welding

This paper presents the application of principal components analysis for Taguchi orthogonal experiments to develop a robust electron beam welding treatment (EBWT) process with high efficiency multiple performance characteristics (MPCs). In this study, the principal components analysis (PCA) design incorporating the correlation matrix of tested trials is employed. In the first step, the MPCs are reduced to two independent components using PCA. Both components accounted for 98.8% of the total variance. The first principal component (PC), which refers to the integrated hardening capability index of the EBWT process, accounts for 70.7% of the total variation. The remaining 28.1% were contributed by the second PC, which can be interpreted as the penetration capability index. In the second step, we identify the most important PC loading vectors using PCA, and estimate the importance of the PCs. By using PCA, relationships between different MPCs can be investigated and the most important factors for the variance of the EBWT process can be identified.The experimental results show that redundant information could be eliminated by using principal components in conjunction with Taguchi’s orthogonal array experiments. This proposed approach is simple, effective, and efficient for developing a robust and high-efficiency EBWT process of high quality. In this study, the MPCs in the EBWT process are successfully optimized.

[1]  Takayuki Hirata,et al.  Electron beam hardening , 1989 .

[2]  G. Dunteman Principal Components Analysis , 1989 .

[3]  Mats Nygren,et al.  Rapidly quenched materials : proceedings of the Seventh International Conference on Rapidly Quenched Materials, Stockholm, Sweden, 13-17 August 1990 , 1991 .

[4]  Ilaria De Munari,et al.  On the Astm electromigration test structure applied to Al–1%Si/TiN/Ti bamboo metal lines , 1995 .

[5]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[6]  Subhash Sharma Applied multivariate techniques , 1995 .

[7]  Angus Jeang,et al.  Process parameters determination for precision manufacturing , 2000 .

[8]  C. Wykes,et al.  A new method of optimising material removal rate using EDM with copper–tungsten electrodes , 2000 .

[9]  Lambda Technologies,et al.  OPTIMIZING THE AUSTENITE CONTENT AND HARDNESS IN 52100 STEEL , 2001 .

[10]  L.S. Kim,et al.  Development of an Intelligent System for Selection of the Process Variables in Gas Metal Arc Welding Processes , 2001 .

[11]  P. Prevéy,et al.  Iterative taguchi analysis: Optimizing the austenite content and hardness in 52100 steel , 2001 .

[12]  Jiju Antony,et al.  Simultaneous Optimisation of Multiple Quality Characteristics in Manufacturing Processes Using Taguchi's Quality Loss Function , 2001 .

[13]  T.-R. Lin Optimisation Technique for Face Milling Stainless Steel with Multiple Performance Characteristics , 2002 .

[14]  Y. S. Tarng,et al.  Process parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel , 2002 .