# Research and Implementation of CPS for Transmission Front Middle Case Assembly Line
## 1 Introduction  
With the take-off of China's macro economy, the resulting rapid development of infrastructure construction, the real estate industry, and highway logistics and transportation industry, coupled with the continuous release of domestic regulations and policies, China's heavy automobile industry achieved tremendous growth between 2010 and 2020. Although in the past two years, due to the short-term economic slowdown combined with the epidemic containment, early consumption overdraft, and other factors, the market suffered a cyclical decline. However, as the epidemic gradually alleviates, logistics demand will improve, infrastructure projects will gradually recover, and demand for heavy automobiles will increase. Therefore, in the long run, heavy automobile manufacturing enterprises need to continue to pay attention to increasing internal industry competition. Under such a background, how to improve the efficiency of the production line and improve its competitiveness is an important subject faced by these enterprises. Transmission is an indispensable part of heavy automobile transmission systems. The accurate assembly of the transmission case, especially the accurate assembly of front and middle cases, is an important way to ensure engineering safety and economy. At present, the assembly efficiency of transmission front and middle cases has been one of the bottlenecks restricting the overall assembly efficiency of transmission. The assembly of transmission front and middle cases adopts the classic assembly line mode. At present, most of the research on production optimization of assembly lines under a changing environment focuses on the solution mode of \"mathematical modeling-intelligent algorithm designing\" [1; 2]. The authors of this paper have been committed to the research in this aspect in the early stage. To solve the original operation plan failure caused by process changes and spatial-temporal coupling constraints, the corresponding mathematical models were constructed, and the improved versions of the NSGA-II and Imperial competition algorithms were designed. The algorithms can obtain the optimal solutions for internationally recognized standard cases [3; 4]. However, the construction of mathematical models was based on specific assumptions, with the data given in advance. The intelligent algorithms designed on this basis are difficult to be applied to actual production, not to mention real-time dynamic control [5]. In recent years, the extensive application of information technology and the Internet of Things technology in the manufacturing field under the environment of Industry 4.0 has provided a new way for solving such problems--the digital transformation of production and manufacturing. Considering that the automation of transmission assembly lines has reached a certain level, accelerating the digital transformation of transmission front and middle case assembly lines is an important method to solve the current problems [6]. The key technology to realize digital transformation is to establish a correlation network among physical entities to obtain all data during the production process and conduct data analysis. Producing process monitor and control, as well as the development of strategic decisions for the production, can be realized when meeting unexpected events based on this collected data, reducing downtime and maintenance costs [7; 8]. However, as the basis of the digital transformation of the production line, the process data has the characteristics of being massive, multi-source, and heterogeneous; the assembly process information is incomplete and unstructured; and correlation of process data is poor, which makes the information difficult to timely transfer to on-site assembly to effectively guide the assembly progress, and leads to the poor assembly balance rate and low one-time pass rate.