Clustering of similar features for the application of statistical process control in small batch and job production

Statistical Process Control (SPC) is seldom applied in small batch and job production because it requires large batch sizes for statistically significant results. This thesis shows that the clustering of similar features from different products can be used to create virtual mass production processes satisfying batch size requirements. An algorithm is developed that identifies similar features based on process parameters such as tool, material, etc. and creates appropriate clusters automatically.