The development of intelligent and data-driven product quality control system are emerging as key engineering technologies for industrial manufacturing process. And many studies have been made to investigate the application of quality control of industrial valve manufacturing process in cyber–physical systems (CPS). The purpose of this article is to provide a quality control and management system by using the modern electronics technology, information technology and network technology. Firstly, we propose an intelligent and data-driven framework model of product quality based on the advanced technology of digital twin (DT) and simulation methods for CPS. Secondly, we emphasize the manufacturing enterprise should hold a data accumulation, and give some useful advises on how to carry out a successful quality analysis system of industrial valve manufacturing process in CPS. Then, as a case, the intelligent method of BP neural network is constructed according to lots of quality characteristics (QCs) of the mechanical and electrical product of industrial valve, and the BP network is trained by using many quality failures of manufacturing process. Finally, the results show that the new quality control system has good accuracy and practicability by the practical example.

智能和数据驱动的产品质量控制系统的开发正在成为工业制造过程中的关键工程技术。为了研究工业阀门制造过程的质量控制在网络物理系统（CPS）中的应用，已经进行了许多研究。本文的目的是通过使用现代电子技术，信息技术和网络技术来提供质量控制和管理系统。首先，我们基于数字孪生（DT）的先进技术和CPS仿真方法，提出了一种智能的，数据驱动的产品质量框架模型。其次，我们强调制造企业应保持数据积累，并就如何在CPS中成功进行工业阀门制造过程的质量分析系统提供一些有用的建议。然后，根据工业阀门机电产品的许多质量特征（QC），构造了BP神经网络的智能方法，并利用制造过程中的许多质量缺陷对BP网络进行了训练。最后，算例表明该新的质量控制体系具有良好的准确性和实用性。