Toward a Sawmill Digital Shadow Based on Coupled Simulation and Supervised Learning Models

Abstract

Digital Twins (DT) have been introduced as promising decision support tools in many different settings and to serve a variety of purposes. Many challenges are raised by their development, including an efficient usage of their computational resources to balance performance on precision, computational cost and speed. This study is, in particular, concerned with Digital Shadows (DS), a concept derived from DT, applied to sawmills sawing production units. A method to combine a computationally intensive sawmill simulation model with a machine learning model is proposed to predict set of lumbers sawed from logs. Numeric experiments are exposed, and the proposed method demonstrate improvements from 11% to 18% of the monitored couple regret from its baseline.