Abstract Modern manufacturing enterprises are shifting toward multi-variety and small-batch production. By optimizing scheduling, both transit and waiting times within the production process can be shortened. This study integrates the advantages of a digital twin and supernetwork to develop an intelligent scheduling method for workshops to rapidly and efficiently generate process plans. By establishing the supernetwork model of a feature-process-machine tool in the digital twin workshop, the centralized and classified management of multiple data types can be realized. A feature similarity matrix is used to cluster similar attribute data in the feature layer subnetwork to realize rapid correspondence of multi-source association information among feature-process-machine tools. Through similarity calculations of decomposed features and the mapping relationships of the supernetwork, production scheduling schemes can be rapidly and efficiently formulated. A virtual workshop is also used to simulate and optimize the scheduling scheme to realize intelligent workshop scheduling. Finally, the efficiency of the proposed intelligent scheduling strategy is verified by using a case study of an aeroengine gear production workshop.

中文翻译：

---

基于数字孪生车间的特征-过程-机床超网络智能调度

摘要 现代制造企业正在向多品种、小批量生产转变。通过优化调度，可以缩短生产过程中的运输和等待时间。本研究结合数字孪生和超级网络的优势，开发了一种车间智能调度方法，以快速高效地生成工艺计划。通过在数字孪生车间建立特征加工机床的超网络模型，可以实现多种数据类型的集中分类管理。利用特征相似矩阵对特征层子网中的相似属性数据进行聚类，实现特征-加工-机床间多源关联信息的快速对应。通过分解特征的相似性计算和超网络的映射关系，可以快速高效地制定生产调度方案。还利用虚拟车间对调度方案进行模拟优化，实现车间智能化调度。最后，以某航空发动机齿轮生产车间为例，验证了所提出的智能调度策略的有效性。