Relevance vector machine‐based defect modelling and optimisation – an application

In presence of many correlated and autocorrelated process variables, initially the support vector machine (SVM) and later the relevance vector machine (RVM) were used for modelling the bonding defect in Hi‐Cr rolls as function of explanatory variables by mapping the original input data space to high‐dimensional feature space using appropriate kernels. The RVM‐Bessel kernel, which turned out to be the best‐fit regression model with minimum error (MSE) from among the competing kernels, was developed when the best‐fit SVM‐RBF kernel regression model was found associated with high absolute value of MSE and a large number of support vectors. The final sparse defect model was developed with the relevance vectors (RVs) generated while fitting the RVM‐Bessel kernel model by taking recourse to hierarchical regression. Constrained optimisation treatment of the sparse defect model helped identifying the factor‐setting corresponding to minimum length (0) of bonding defect. Confirmatory trial runs showed encouraging trends.