Efficient Modeling of Digital Shadows for Production Processes: A Case Study for Quality Prediction in High Pressure Die Casting Processes

The advent of Industry 4.0 has led a wide variety of engineering fields to incorporate more automation into their existing work processes. Various engineering sectors intend to imbibe aspects of Industry 4.0 technologies by leveraging Internet of Things coupled with Machine Learning and Artificial Intelligence for process optimization. This, in turn, has led to the surge of cross-domain data integration strategies which when enriched with domain specific knowledge creates dynamic models, termed as Digital Shadows. In this paper, we present the adaptation of the Digital Shadow modeling approach to die casting processes. We propose a generic pipeline for the creation of the model and test the efficacy of such an approach by transforming a predictive analytics model into a digital shadow model. For the predictive modeling, we present a novel approach of image based pixel classification which accurately predicts the occurrence as well as the location of damages on the cast object surfaces.