Abstract With the widespread application of new digital and information technologies, manufacturing patterns have been reformed in different industries. The digital twin shop-floor (DTS), based on digital twins, has gradually become an advanced data-driven manufacturing model for modern manufacturing companies. The DTS integrates physical and virtual manufacturing processes through simulation and optimisation to achieve real-time mapping of data, thereby aiding managers in making more accurate and timely production decisions. However, the existing scheduling models and algorithms cannot effectively satisfy the accuracy and timeliness requirements of simulation and optimisation in DTS. Therefore, to create effective and rapid manufacturing resource (MR) recommendations for production services, this study established a framework for manufacturing task (MT) semantic modelling and MR dynamic recommendation (MT&MR) for DTS. Our model offers an effective approach to the description and conception of MTs based on ontology, MT semantic indexing and retrieval, and MR recommendation for DTS. Finally, a case analysis demonstrated that the method is effective and feasible.

中文翻译：

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数字孪生车间的制造任务语义建模和制造资源推荐框架

摘要 随着新型数字化和信息化技术的广泛应用，不同行业的制造模式发生了变革。基于数字孪生的数字孪生车间（DTS）已逐渐成为现代制造企业的先进数据驱动制造模式。DTS通过模拟和优化整合物理和虚拟制造过程，实现数据的实时映射，从而帮助管理人员做出更准确、及时的生产决策。然而，现有的调度模型和算法不能有效满足DTS仿真优化的准确性和及时性要求。因此，要为生产服务创建有效且快速的制造资源 (MR) 建议，本研究为 DTS 建立了制造任务 (MT) 语义建模和 MR 动态推荐 (MT&MR) 框架。我们的模型提供了一种基于本体、MT 语义索引和检索以及 DTS MR 推荐的 MT 描述和概念的有效方法。最后通过案例分析证明了该方法的有效性和可行性。