The ability to estimate the internal weather conditions of shipping containers globally without using sensors will enable new monitoring and risk assessment solutions. We tackle this problem by developing both linear and nonlinear regression models to predict the internal temperatures and relative humidity of containers from meteorological data. Our training data consists of sensor measurements from 85 shipments across 3 continents over 4 months - the largest data ever collected in an academic publication. We extract features from 761 gigabytes of weather data and incorporate physics (psychrometry) models to engineer new features that boost the performance of our models. Our best models make temperature and relative humidity predictions with mean absolute errors of $1.8 ^{\circ}\mathrm{C}$ and 5.0% - measurements well within the uncertainty range of the sensors - and thereby demonstrate accurate sensorless monitoring.

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.

S.241-248