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.
In response to the policy development, the manufacturing industry is undergoing intelligent technology transformation. Online orders are characterized by multiple varieties and small batches. Therefore, in order to meet the personalized needs of more customers, it is necessary to transform the traditional production mode into the intelligent factory mode. Intelligent factories can realize green and sustainable development. Using intelligent robot technology to complete programming to design and process is an important research direction in related fields. In this context, this study strives to design a unitary production scheduling algorithm, which is implemented based on artificial intelligence technology. After testing, this algorithm has the best performance, the shortest running time, relatively low power consumption and short product processing cycle. The system design framework includes three parts: the communication design between physical control equipment and PC, the interactive control software design of PC, and the virtual controlled object model design. From the research results, it can be concluded that the realization of production scheduling algorithm design for intelligent manufacturing cells can help enterprises to make rational allocation of order size and resources, so as to improve production efficiency while taking into account the low-carbon production concept widely promoted by the international community. In this paper, a kind of effective production scheduling algorithm is studied by introducing AI technology into the field of intelligent manufacturing cell.
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