Under the background of intelligent manufacturing, the modeling and scheduling of an intelligent manufacturing system driven by big data have attracted increasing attention from all walks of life. Deep learning can find more hidden knowledge in the process of feature extraction of the hierarchical structure and has good data adaptability in domain adaptation. From the perspective of the manufacturing system, intelligent scheduling is irreplaceable in intelligent production when the manufacturing quantity of workpieces is small or products are constantly changing. This paper expounds the outstanding advantages of deep learning in intelligent manufacturing system modeling, which provides an effective way and powerful tool for intelligent manufacturing system design, performance analysis, and running status monitoring and provides a clear direction for selecting, designing, or implementing the deep learning architecture in the field of intelligent manufacturing system modeling and scheduling. The scheduling of the intelligent manufacturing system should integrate intelligent scheduling of part processing and intelligent planning of product assembly, which is suitable for intelligent scheduling of any kind and quantity of products and resources.

中文翻译：

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基于深度学习的智能制造系统建模与调度方法研究

在智能制造背景下，以大数据为驱动的智能制造系统的建模与调度越来越受到社会各界的关注。深度学习在层次结构的特征提取过程中可以发现更多的隐藏知识，在领域适应中具有良好的数据适应性。从制造系统的角度来看，当工件的制造数量很少或产品不断变化时，智能调度在智能生产中是不可替代的。本文阐述了深度学习在智能制造系统建模中的突出优势，为智能制造系统设计、性能分析、和运行状态监控，为智能制造系统建模与调度领域深度学习架构的选择、设计或实现提供了明确的方向。智能制造系统的调度应该将零件加工的智能调度和产品装配的智能规划相结合，适用于任何种类和数量的产品和资源的智能调度。