Hyperspectral Image Analysis for Automatic Detection and Discrimination of Residual Manufacturing Contaminants

In modern manufacturing, divergent market dynamics impel companies to move toward a zero-defect production by reducing the risk of errors and defects down to zero. Paint-coating of metal surfaces is one of such process steps and most prominent as consumers will be animated to buy based on their first impression. Despite significant advances in automation and precision engineering of paint-coating, the presence of process contaminants as residual of different stages of production may compromise the process. In this contribution, we focus on the paint-coating of washing machine cabinets as a representative. Within the last decade, hyperspectral imaging technology has shown promising potentials in a variety of applications that aim at detecting objects and discriminating materials. In this work, we present a hyperspectral acquisition and analysis system that verifies the feasibility of detection and discrimination of process contaminants smeared on the washing machine cabinet based on spectral information. The acquisition system, aided by a robot arm, collects hyperspectral images based on two scenarios: contaminants on flat steel sheets and contaminants on washing machine chassis. This dataset, which is published publicly, is calibrated, analysed, and segmented through the proposed analysis models. The results for both flat base and structured washing machine surfaces indicate the great capacity of this technology for being integrated into the pre-treatment stage before painting metal parts