A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects - Rajesh Ransing (Computers in Industry, 2013)
Traditional techniques are unable to discover correlations among factors in the ‘noisy’ in-process data. The proposed technique of discovering correlations in the reduced space defined by the principal components is shown to be a novel and robust method. It allows process engineers to view limited number of important penalty matrices from the thousands of possible combinations. The approach has been embedded training courses offered by the American Foundrymen and Institute of Cast Metal Engineers in UK. Elsevier publishers have chosen to make this paper open source, for a period of three months, for the benefit foundry engineers around the world.