Monitoring variations in multimode surface topography

Abstract

The recent development of optical measuring instruments has increased the use of surface topographic data for monitoring the quality of the engineered surface. Due to the complexities of modern industrial processes, however, surfaces of final products under the normal manufacturing process may have multiple modes, such that the surface consists of different topographic features from one in-control mode to another. In this case, existing monitoring approaches based on the single mode surface cannot characterise normal surfaces with multiple modes, and result in poor detection performance. In this article, a new approach for monitoring variations in multimode surface topography is presented. We propose a multimode surface prediction model, which characterises the generic behaviour of normal surfaces with multiple in-control modes. Moreover, we present a mode-specific surface monitoring approach that identifies topographic variations on the surfaces based on the similarity between probability density function (PDF) of residuals from observed and normal surfaces obtained through the prediction model. A novel probabilistic distance measure is introduced to effectively measure the similarity between a single residual PDF and a set of residual PDFs under the same mode. The effectiveness of the proposed approach is demonstrated through numerical simulation and real-life application of paper surface monitoring.