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.

尽管由于向工业 4.0 的过渡以及数字孪生技术的发展，数字模拟在工业世界中变得越来越重要，但它们的计算强度也越来越大。许多作者提出使用机器学习 (ML) 元模型来降低这种成本并利用目前工业中可用的大量数据。在工业环境中，有必要不断地训练集成到决策支持系统中的预测模型，以确保其预测质量随时间的一致性。这促使作者在锯木业的特定背景下研究主动学习 (AL) 概念。在本文中，提出了一种基于 AL 的方法，将模拟和 ML 元模型相结合，该元模型仅使用选定的数据（智能数据）进行增量训练。展示了基于锯木业和实验的案例研究，其结果证明了这种方法的可能优势。