Smart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to traditional automation. It is underpinned by the deployment of cyberphysical systems that, through the application of Artificial Intelligence, integrate predictive capabilities and foster rapid decision-making. Deep Learning (DL) is a key enabler for the development of smart factories. However, the implementation of DL in smart factories is hindered by its reliance on large amounts of data and extreme computational demand. To address this challenge, Transfer Learning (TL) has been proposed to promote the efficient training of models by enabling the reuse of previously trained models. In this paper, by means of a specific example in aluminium can manufacturing, an empirical study is presented, which demonstrates the potential of TL to achieve fast deployment of scalable and reusable predictive models for Cyber Manufacturing Systems. Through extensive experiments, the value of TL is demonstrated to achieve better generalisation and model performance, especially with limited datasets. This research provides a pragmatic approach towards predictive model building for cyber twins, paving the way towards the realisation of smart factories.

智能工厂是智能、全连接和灵活的系统，可以持续监控和分析来自互连系统的数据流，以做出决策并动态适应新环境。与传统自动化相比，智能工厂的实施代表了一个飞跃。它以网络物理系统的部署为基础，通过应用人工智能，整合预测能力并促进快速决策。深度学习 (DL) 是智能工厂发展的关键推动因素。然而，深度学习在智能工厂中的实施因其依赖大量数据和极端计算需求而受到阻碍。为了应对这一挑战，已提出迁移学习 (TL) 以通过重用先前训练过的模型来促进模型的有效训练。在本文中，通过铝罐制造中的一个具体示例，展示了一项实证研究，该研究展示了 TL 为网络制造系统实现可扩展和可重复使用的预测模型的快速部署的潜力。通过大量实验，证明了 TL 的价值可以实现更好的泛化和模型性能，尤其是在数据集有限的情况下。这项研究为网络双胞胎的预测模型构建提供了一种务实的方法，为实现智能工厂铺平了道路。这展示了 TL 为网络制造系统实现可扩展和可重用预测模型的快速部署的潜力。通过大量实验，证明了 TL 的价值可以实现更好的泛化和模型性能，尤其是在数据集有限的情况下。这项研究为网络双胞胎的预测模型构建提供了一种务实的方法，为实现智能工厂铺平了道路。这展示了 TL 为网络制造系统实现可扩展和可重用预测模型的快速部署的潜力。通过大量实验，证明了 TL 的价值可以实现更好的泛化和模型性能，尤其是在数据集有限的情况下。这项研究为网络双胞胎的预测模型构建提供了一种务实的方法，为实现智能工厂铺平了道路。