On integrating multisensor data for quality

This paper explores advantages arising from properly combining information provided by two sensors (contact and non-contact one) when measuring the same feature. When both of the metrology devices are used in cooperation, datasets of different resolution (a.k.a. multi-resolution data) have to be properly integrated in order to reconstruct the measured surface (or any geometric feature of interest) in both the sampled and the unsampled locations. To this aim, we propose a two-stage model, which consists of a low-resolution data model and a linkage model connecting the lowand the high-resolution data. The low-resolution data model is a spatial statistics model, specifically a Gaussian Process (GP). The linkage model has been adapted from the literature on calibrating computer simulation models of different accuracies. The newly developed two-stage model is used for quality inspection, showing that a model that properly combines multisensor information produces better results in terms of form error assessment when compared to models based on each single-resolution dataset, or with both but without structuring an appropriate data fusion model.