Research On The Key Technologies Of Internet Of Things-based Quality Control And System Implementation

Manufacturing enterprises remain many problems and demands in qualityinformation collection and quality data analysis, such as the low degree ofautomation in quality information collection and synthetic high cost of control chartetc. With the development of the Internet of Things (IoT) technology such as RFIDand wireless sensor networks (WSN), we have new methods for manufacturingquality control. This paper mainly studies on the key quality control technologiesbased on IoT.At first, quality information is collected automatically by IoT technology.Considering about the characteristics and demands of manufacturing quality controland IoT technology, an isomerism data communication network is constructed, andmulti-source quality information transformation is realized. Based on the isomerismdata communication network, the paper proposes and designs an IoT-based qualityinformation collection scheme. Taking digital caliper as the object of study, thepaper has tested the mobile data collection based on Zigbee technology, and verifiedthe quality information collection scheme based on IoT.Then the process quality is analyzed and controlled based on the collectedreal-time information from the above IoT. Under the assumption that the processmean shift is a random variable, an economic design method of the EWMA controlchart is proposed, and the quality loss of the defective products is quantified bycalculating the oversize rate of the quality characteristics. In this paper, the optimaldesign model of the EWMA control chart is set up to minimize the expected cost ina time unit, and we can obtain the optimal EWMA control chart parameters such asthe control limit, the smoothing factor, the sample size and the sampling interval. Meanwhile, a comparative example shows that the proposed model is superior to theShewhart mean control chart.The proposed quality control system has successfully implemented in JingweiFrame Company: the quality detection time and defective rate are decreased by15%and8%respectively, and the products have gained a good customer satisfaction.