ABSTRACT Digital Twin (DT) implementations can contribute to smart manufacturing by integrating the physical and the cyber space. Artificial Intelligence (AI) applications based on Machine Learning (ML) are widely accepted as promising technologies in manufacturing. However, ML methods require large volumes of quality training datasets and in the case of supervised ML, manual input is usually required for labelling those datasets. Such an approach is expensive, prone to errors and labour as well as time-intensive, especially in a highly complex and dynamic production environment. DT models can be utilized for accelerating the training phase in ML by creating suitable training datasets as well as by automatic labelling via the simulation tools chain and thus alleviating user’s involvement during training. These synthetic datasets can be enhanced and cross-validated with real-world information which is not required to be extensive. A framework for implementing the proposed DT-driven approach for developing ML models is presented. The proposed framework has been implemented in an industrially relevant use case. The use case has provided evidence that the proposed concept can be used for training vision-based recognition of parts’ orientation using simulation of DT models, which in turn can be used for adaptively controlling the production process.

中文翻译：

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数字孪生驱动的监督机器学习，用于开发制造业中的人工智能应用

摘要 数字孪生 (DT) 实施可以通过整合物理空间和网络空间来促进智能制造。基于机器学习 (ML) 的人工智能 (AI) 应用程序被广泛接受为制造业中的有前途的技术。然而，ML 方法需要大量高质量的训练数据集，在有监督的 ML 的情况下，通常需要手动输入来标记这些数据集。这种方法成本高昂，容易出错，而且费时费力，尤其是在高度复杂和动态的生产环境中。DT 模型可用于通过创建合适的训练数据集以及通过模拟工具链自动标记来加速 ML 的训练阶段，从而减轻用户在训练过程中的参与。这些合成数据集可以使用不需要广泛的真实世界信息进行增强和交叉验证。提出了一个框架，用于实施所提出的用于开发 ML 模型的 DT 驱动方法。提议的框架已在工业相关用例中实施。该用例提供的证据表明，所提出的概念可用于使用 DT 模型模拟训练基于视觉的零件方向识别，进而可用于自适应控制生产过程。