Framework for manufacturing-tasks semantic modelling and manufacturing-resource recommendation for digital twin shop-floor

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.

Automated summary

With the widespread application of new digital and information technologies, manufacturing patterns in different industries have been reformed. The digital twin shop-floor (DTS) has become an advanced data-driven manufacturing model for modern manufacturing companies. This study established a framework for manufacturing task semantic modelling and MR dynamic recommendation to meet the needs of simulation and optimization in DTS.