Variation analysis and visualization of manufacturing processes via augmented reality

In manufacturing processes and systems, many production and quality control decisions cannot be made in a timely manner due to the time delay for data collection, pre-processing and analysis. In this paper, we propose an online variation analysis and visualization method via augmented reality (AR) to operators. A regularized functional logistic regression model is constructed to predict a binary product quality indicator based on continuously measured process variables. This model is integrated with the AR platform, which provides not only the on-site visualization of raw data sets from manufacturing, but also useful information for decision support in variation reduction. The proposed method is validated in a case study of quality prediction in an additive manufacturing process.