1 Introduction

Intelligent manufacturing is a core technology of the new industrial revolution that includes the digitization, networking,and intelligentization of the manufacturing industry. “Made in China 2025,” “German Industry 4.0,” and the Industrial Internet in the US all focus on intelligent manufacturing and a deeper integration of information and manufacturing technologies in order to advance the next industrial the strategic priorities are different for each country, the core technologies converge at cyber-physical system(CPS) [1].

CPSs are the foundation for the realization of intelligent manufacturing systems that integrate computing, communication, and control, on the basis of sensor technology. The system architecture is usually composed of the equipment layer, sensing layer, network layer, cognitive layer, and control layer. After sensing, collecting, transmitting, storing, mining,and analyzing the information about the machine in physical space (PS), a digitalized machine (i-Machine) mirroring the physical machine is set up in cyber space (CS) and referred to as the digital model of the physical machine on the CPS cognitive layer (or the “CPS model of the machine,” in short).

The key of intelligent manufacturing is to set up CPS models of the machines on the cognitive layer. Using these models, people can estimate the work performances of a machine for pre-determined tasks, establish an integrated environment combining information, machines, and humans, and determine an intelligent-control strategy; realize coordination, interaction, and dynamic control; and finally, achieve intelligent manufacturing.

Computer numerical control (CNC) machine tools are the most fundamental and important manufacturing equipments and the most important physical resource for manufacturing enterprises. In order to realize intelligent manufacturing, it is important to establish CPS models of CNC machine that a CNC machine tool is a complex dynamic system that consists of machine tool, cutting tool, fixture, workpieces, and work tasks, creating a CPS model of a CNC machine tool is a tremendous challenge.

Several recent studies focused on CPS modeling methods based on mathematical and physical computation, centered on the forward theoretical modeling method. Jensen et al. [2]proposed ten steps for establishing a CPS based on a physical model and systematically described and evaluated the CPS that was established in this way. Derler et al. [3] analyzed the intrinsic heterogeneity, concurrency, and sensitivity to timing of CPS model, and proposed to build a CPS model by means of hybrid system modeling, concurrent and heterogeneous model of computation, domain-specific ontology, and the joint modeling of functionality and implementation architecture. Wu and Chen [4] established a multi-domain physical system simulation and optimization platform utilizing the multi-domain modeling language Modelica in order to realize the expression, modeling, computation, and optimization of a multi-physics model.

The process system that contains machine tool, cutting tool, fixture, and workpieces is a complex dynamic system with mechanical, electromagnetic, fluid, thermal, material,and control components. It is therefore very difficult to describe the CPS model of a CNC machine tool in a complete and accurate manner with the use of any single mathematical or physical method. In addition, the massive quantities of parameters in the theoretical model (e.g., friction, rigidity,and the material properties of the machine tool) have high dispersion due to the differences in assembly quality and processing conditions. With the emergence of big data technology, the integration of theoretic modeling with the big data approach makes it possible to improve the completeness and accuracy of the CPS models of CNC machine tools.

In recent years, studies on the CPS modeling method based on big data have gained extensive attentions. In 2006, the Association for Manufacturing Technology (AMT) and the National Institute of Standards and Technology (NIST), both in the US, proposed a communication standard, MTConnectTM[5], for data collection and transmission for CNC machine tools. Kao et al. [6] suggested establishing a CS by providing services via Watchdog Agent? tools and establishing a prognostics and health management (PHM) technology directly on the data thus obtained. Wang [7] proposed a CPS scheme in which a plant is set up with a distributed process-planning system, a dynamic resource-planning system, a real-time process-monitoring system, a remote control system, and so on. Lee et al. [8, 9] pointed out that collecting and analyzing industrial big data is the key to the establishment of CPS as well as future intelligent-manufacturing equipments. They proposed a 5C (configure, cognition, cyber, conversion, connection) system structure for establishing the CPS of CNC machine tools, under which the status data for CNC machine tools could be collected using radio-frequency identification(RFID) technology, and the machining process and degradation of the CNC machine tools and their components could be identified based on control and inspection data. Wan et al.[10] used the Internet of Things and a multi-sensory network technique to enhance the machine-to-machine (M2M) system for information exchange in order to realize intelligent decision-making and the automatic control of a system, thus upgrading from an M2M system to a CPS of machine tools.