Clustering Methods for Defect Tracking in Order to Assess the Performance of a Porosity Inspection System

Surface porosity inspection is important for quality assurance of critical mating surfaces on machined components. An important metric for assessing the performance of an automated surface porosity inspection system is repeatability. Traditional gage repeatability analysis is well defined for dimensional measurements of machined part features. However, the analysis becomes more difficult for surface porosity inspection. This is because surface porosity appears in random sizes and in random locations. Repeatability analysis requires painstaking effort in tracking individual pores through repeated measurements. Therefore, this paper presents an automated approach for tracking porosity for the purpose of repeatability analysis. Two different algorithms are proposed and evaluated. The first is a tolerance based method that uses pre-specified tolerances to determine if pores should be grouped together. The second algorithm is similar to hierarchical agglomerative clustering, using a similarity matrix to store differences between cluster centroids. However, this algorithm uses a training period to determine when to stop clustering instead of continuing until all pores are in one cluster. Experimental results describe differences in the accuracy of both approaches and effort required to obtain a solution. The computation time required for the first method is much shorter than that of the second method. However, the first algorithm requires a-priori information to specify the tolerances, whereas the second algorithm requires no prior knowledge.