Residual-Based Surface Segmentation for Monitoring Topographic Variations

Monitoring topographic variations in the engineered surface is crucial for quality engineers since the change in the surface finish is closely related to the performance of products. However, several challenging issues such as the existence of spatial autocorrelation within the surface, and changes of topographic features such as position and shape of peaks and valleys across defect-free surfaces make it difficult to monitor variations in the surface. In addition, existing monitoring approaches fail to detect local changes in the surface. In this article, we present a new approach for monitoring topographic variations in surfaces. We develop a residual-based separation deviation (RBSD) model to effectively identify local surface changes. Residuals are obtained through the fit surface prediction model, which characterizes the generic behavior of defect-free surfaces, and binarized by the RBSD model to distinguish the defective region where residuals are autocorrelated. A spatial randomness-based monitoring statistic is introduced to evaluate binary patterns in order to detect surface anomalies. Numerical simulation and a case study of the coated paper surface monitoring are provided to demonstrate the effectiveness of the proposed approach.Note to Practitioners—An increasing number of optical measuring instruments provide quality engineers with surface topographic data of the product, and enable precise and accurate inspection of surface variations during the manufacturing process. However, spatial and arbitrary geometric features on topographic data often make monitoring the surface changes a challenging task. This article proposes a new monitoring approach for detecting topographic variations in the surface. We mathematically characterize the generic behavior of the surface under the normal process, and present a surface segmentation model to detect local variations in the surface. In practice, our approach is applicable to real-life paper surface variations monitoring. The proposed approach is expected to provide a new idea to practitioners who work on surface topography inspection in various fields as well as the pulp industry.