K-means partitioning approach to predict the error observations in small datasets

The partitioning algorithm was used to identify the uncertainty and the similarity in large sets of databases. K values are set based on the models. The effect of change in k values from the lowest to the highest level was analysed for a small set of databases that are acquired through machining AlSi7/63% SiC hybrid composite. An attempt has been made to identify the correlation between the k value clustered class and with a developed linear regression model. Further, the analysis was done to identify the critical machining observations that have a high error rate while on machining AlSi7/63% SiC hybrid composite using abrasive water jet at the varied parameters condition. Taguchi L27 orthogonal array observations are clustered into different groups with a k value of 2 to 8. The study was limited to k = 8 because at this level, clustered classes have very few observations that make unfit to predict the model.