Although digital simulations are becoming increasingly important in the industrial world owing to the transition toward Industry 4.0, as well as the development of digital twin technologies, they have become increasingly computationally intensive. Many authors have proposed the use of Machine Learning (ML) metamodels to alleviate this cost and take advantage of the enormous amount of data that are currently available in industry. In an industrial context, it is necessary to continuously train predictive models integrated into decision support systems to ensure the consistency of their prediction quality over time. This led the authors to investigate Active Learning (AL) concepts in the particular context of the sawmilling industry. In this paper, a method based on AL is proposed to combine simulation and an ML metamodel that is trained incrementally using only selected data (smart data). A case study based on the sawmilling industry and experiments are shown, the results of which prove the possible advantages of this approach.
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.
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