PERCEPTION SYSTEM OF GARMENT MACHINERY PRODUCTION LINE BASED ON THE INTERNET OF THINGS

- The vigorous development of the Internet of Things and information technology promotes the transformation of the manufacturing industry from traditional management to intelligent informatization. In order to discuss the intelligent production line monitoring and sensing system, this paper selects the garment mechanical parts processing production line as the object. Firstly, on the production line data collection, the characteristics and existing problems of the garment mechanical parts processing production line are analyzed. Then, the production line's awareness monitoring demand is summarized. Secondly, the production line's data sources and acquisition methods are discussed. Based on the configurable idea, a real-time data acquisition system for the garment mechanical parts processing production line is proposed. In addition, in terms of quality prediction, based on the Back Propagation Neural Network (BPNN), the Multiple Population Genetic Algorithm (MPGA) is introduced to build the MPGA-BPNNNN quality prediction algorithm. Finally, the performance of the system and algorithm is tested based on simulation experiments. The results show that the system's data acquisition and concurrent client pressures are 1.86ms and 650 users, respectively, meeting the requirements. Compared with the traditional BPNN, the MPGA-BPNNNN algorithm has a more accurate prediction result, with a root mean square error of 0.0645, which is also relatively small. The design of a perceptual data acquisition system and the construction of a quality prediction algorithm for garment mechanical parts processing production lines can provide a path for transforming traditional manufacturing production lines into intelligent digital perceptual systems.