<figure><img src="image_1.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
Cyber-Physical System Platform and Applications for
Smart Manufacturing in Global Automotive Industry

Jinho Yang¹[0000-0002-9946-4432], Jonghwan Choi¹[0000-0001-9193-9379], Joohee Lym¹[0000-0002-6135-529X], Sang Do Noh¹*[0000-0002-5610-5560], Yong-Shin Kang², Sang Hyun Lee³, Hyung Sun Kim⁴, Je-Hoon Lee⁵, and Hyun-Jung Kim⁵

¹ Sungkyunkwan University, Suwon 16419, Republic of Korea

² Advanced Institutes of Convergence Technology, Suwon 16229, Republic of Korea

³ Yura, Seongnam 13494, Republic of Korea

⁴ Dexta Inc., Suwon 16677, Republic of Korea

⁵ Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea

sdnoh@skku.edu

**Abstract.** The modern manufacturing industry ought to solve various problems amid increasingly fierce competition. Particularly, the supply chain network of the manufacturing industry is expanding globally. Therefore, there is an increasing necessity for smart manufacturing. Smart manufacturing is an integrated manufacturing system that applies information and communication technologies to manufacturing, rendering the entire manufacturing process smart, for obtaining real-time responses to internal and external variations in factories, supply networks, and customer requirements. Although there are several technologies that promote smart manufacturing, the cyber-physical system (CPS), introduced into the manufacturing environment is the primary technology. Global manufacturing enterprises have limitations in that it is difficult to collect information generated at distributed manufacturing sites with independent applications, and it is impossible to make quick decisions. To build smart manufacturing at such global manufacturing scales, a platform that integrates information and provides various applications in a distributed environment using the CPS ought to be built. This paper proposes an integrated platform for smart manufacturing implementation. The primary components and functions of the platform are defined. Finally, the effectiveness of the proposed platform is verified through a case study.

**Keywords:** Cyber-Physical System (CPS), Platform, Smart Manufacturing.

# 1 Introduction

The modern manufacturing industry is required to solve various problems, such as dynamic global market demand, short product life cycles, greater product variety, and lower production costs [1, 2]. These requirements increase the complexity of manufacturing systems and promote the introduction of various information and communication technologies (ICTs) into the existing manufacturing system. Starting with industry 4.0, which was proposed by Germany, various advanced manufacturing countries, such as
the United States, Korea, and Sweden, are trying to attain a competitive edge in manu-
facturing [3-5]. Various technologies, such as smart manufacturing [3], the cyber-phys-
ical system (CPS), internet of things, and big data, are commonly considered essential
[3, 5] and believed to promote the fourth industrial revolution in the manufacturing
field. When these technologies are applied to manufacturing, the entire manufacturing
process can become smart and respond to the internal and external variations in systems
such as factories, supply networks, and customer requirements in real time [4, 6]. The
CPS is a core technology of the industry 4.0 architecture. It is a concept for managing
systems connected to physical resources in the cyber world through communication
networks [5, 7]. The cyber-physical production system (CPPS), which corresponds to
the CPS in the manufacturing environment, constitutes autonomous and cooperative
elements and subsystems, based on information obtained from the overall production
site and logistics system, intelligence, connectedness, and responsiveness toward inter-
nal and external variations as its primary features [8].

Global manufacturing companies dispersed in several countries ought to overcome obstacles such as differences in cultures, languages, regulations, policies, and manufacturing units [9]. Particularly, the automotive parts industry is a representative assembly industry in which a small number of automakers procure tens of thousands of parts from a large number of automotive part makers to produce automobiles. It constitutes highly complex and extensive value chains [10, 11]. Therefore, the collection of information in a distributed field with an independent application is difficult. Moreover, localized platforms exhibit limitations toward acceptance and distribution in the industry [12]. To facilitate cooperation among different enterprises and responsiveness to global competition, a standardized open platform, in lieu of existing independent platforms, is needed.

This paper proposes an integrated platform for the efficient implementation of smart
manufacturing for global manufacturing companies. Various ICTs used in the CPS plat-
form are discussed, and the applications provided through this platform are introduced.
Finally, industrial case studies are performed by applying this platform to the assembly
and production line of a Korean automotive part manufacturing company.

## 2 Research Background

The CPS is an innovative technology that enables data access, processing, analysis, and utilization between physical assets and computational entities through an interconnected system [8, 13, 14]. The CPPS can configure the production environment more flexibly, in addition to providing more differentiated management and control processes than conventional manufacturing systems do [8, 15]. To effectively implement the CPPS, the most important consideration is the integration of analytics technology and a simulation-based approach for big data collected in large quantities at the manufacturing site [8]. Particularly, the most core technology among the various elements constituting the CPPS is the digital twin (DT), defined as a virtual model that enables the analysis, evaluation, optimization, and prediction of the current situation by syn-
chronizing and simulating real-time information and functions collected at the produc-
tion site [16]. The core technical functionality of the DT is simulation. Additionally, it
is essential for deriving the performance indicators of various production control tech-
niques [17]. To provide various services by integrating information in a distributed en-
vironment using the CPS and DT technologies discussed, a platform that provides a
large-scale service system ought to be constructed [18]. In a smart manufacturing envi-
ronment, this platform is a middleware technology that delivers information collected
from devices to services and applications. The platform builds various services and
applications by converging technologies such as CPS, big data, and cloud computing.
Through this platform, people, things, and systems can be connected [5]. In particular,
the CPS platform, with ICT technologies, provides intelligent capabilities to predict
various situation at production sites, and encapsulate and virtualize distributed manu-
facturing resources into cloud manufacturing services to support tasks such as product
design, simulation, testing, and management [19–21]. Accordingly, we propose an in-
tegrated platform using the DT and various ICTs to construct an efficient CPS environ-
ment.

3 CPS Platform and Applications

3.1 CPS Platform

The proposed technology in this study is a CPS platform for building a smart assembly
system for automotive part makers through converging with legacy systems, such as
the manufacturing execution system, or enterprise performance management using var-
ious ICT and applications for production site application. The purpose of this platform
is to support various services such as production quality prediction and production man-
agement for the development of a smart manufacturing system. Fig. 1 depicts the ar-
chitecture of the proposed platform.

The proposed CPS was classified into the automotive part assembly line (corresponding
to the physical world) and the platform (corresponding to the virtual world). The defini-
nitions of the components composing the CPS are as follows.

* Automotive part assembly line: It is an assembly line that produces various automo-
bile parts and is a manufacturing site comprising resources such as workers and as-
sembly parts. Information with respect to workers, equipment, and products on the
assembly line are transmitted to the CPS platform and legacy system through media.

* Core technologies of the platform: It is the core basis of basic functions and application services for engineering in the platform. The functions comprise the DT, which can perform various functions by configuring a virtual model using real-time data collected from the manufacturing site, and can perform advanced analytics to analyze and utilize the collected data. Finally, it comprises big data management for manufacturing, which processes and refines the data collected at the manufacturing site.
* Basic functions: These are provided by the platforms utilizing core functions. The basic functions available on the platform were real-time monitoring, simulation, visualization, prediction, and analysis.

* Application services: These are services that can be utilized according to the characteristics and circumstances of the manufacturing site using the basic functions described previously.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Conceptual architecture of the proposed platform.</figcaption></figure>

Fig. 2 shows the driving screens of the basic functions of a platform implemented in a cloud computing environment. The CPS platform dashboard allows service clients to check and respond to the status of the system based on real-time data from various manufacturing sites. Using this data, real-time monitoring can generate a DT model at the manufacturing site and monitor the situation of a particular production line or factory. The simulation on this platform provides more accurate results than existing simulation models by utilizing the generated DT model. Additionally, the platform facilitates the visualization of various production-related data such as simulation, prediction, and diagnosis results. This function includes charts, reports, and visualization of 3D models. Table 1 summarizes the relationship between the core technologies used in basic functions. The basic functions of the platform are built by combining one or more core functions. The application services can be built using these basic functions. Although various application services can be configured on the platform, based on the requirements of a global manufacturer, this paper introduces smart production management and smart quality prediction systems.
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Snapshots of the CPS platform (a) dashboard [22] (b) real-time monitoring [22], (c) simulation, and (d) visualization.</figcaption></figure>

<table><caption>**Table 1.** Relationship between basic functions and core technologies.</caption><thead><tr><td>Basic function</td><td>Core technologies</td></tr></thead><tbody><tr><td>Real-time monitoring</td><td>Digital twin; big data management</td></tr><tr><td>Simulation</td><td>Digital twin; big data management; advanced analytics</td></tr><tr><td>Prediction and analysis</td><td>Big data management; advanced analytics</td></tr><tr><td>Visualization</td><td>Digital twin; big data management; advanced analytics</td></tr></tbody></table>

The platform shown in Fig. 2 applied a standardized information model for data collection from the distributed manufacturing sites to ensure versatility. The information model was defined based on international industry standards [22]. We have omitted the detailed descriptions in this paper for conciseness.

## 3.2 Applications of the CPS Platform

Various service applications can be implemented using the functions of the proposed platform. To build a smart assembly line, this platform primarily comprises two service applications. The first application is a smart quality prediction system that can predict the quality of products in advance by analyzing the quality impact analysis between the processes. The core function of this system is advanced analytics; the algorithms are implemented by preprocessing and training data using various machine learning models. The second application is a smart production management system, which utilizes an optimization algorithm to provide an optimal task allocation function considering the skill of workers at the production site and the amount of work required for each production line. The periodic verification of the working status of workers on the production line facilitates the relocation and optimization of the work process for problems
caused by workers for various reasons. Fig. 3 is a screen for these service applications
provided by the platform.

<figure><img src="image_4.png" /><figcaption>Fig. 3. (a) Smart production management and (b) smart quality prediction systems.</figcaption></figure>

4 Industrial Case Study

To validate the proposed CPS platform and applications, they were applied to an actual
industrial site of a global automotive parts manufacturer in Korea. The target factories
located in Vietnam, China, and Korea were virtualized using the platform. Research is
currently underway toward applying the developed technologies to the smart factory
test bed located in Sweden. This study was conducted as a part of the international joint
research of Korea and Sweden. Fig. 4 shows the DT models of the target factories for
which the proposed technology was applied. The proposed platform and services appli-cation results for the three target production lines were as follows.

* The average performance calculation accuracy of the DT model virtualized on the platform agreed with more than 90% of the observations in the real production line.

* With optimized assembly process control, the productivity of the assembly line was improved by more than 3%.

* The average quality prediction accuracy was more than 90% on average, for the identification of defects and responding in advance, contributing toward productivity improvement.

The details of the application results are not completely disclosed, owing to the con-
fidentiality conditions of the manufacturer applying the technologies.

<figure><img src="image_5.png" /></figure>
Fig. 4. Digital twin models of target factories.

**5 Conclusion**

This paper proposed a CPS platform and application for the systematic management and efficient operation of a Korean-based global automotive part manufacturer. For the smartization of the manufacturing system, we discussed advanced technologies, such as the DT and advanced analytics. The contributions of this study are as follows. First, the architecture of the CPS platform was presented, and the primary components were defined. Based on this, the practically implemented technologies were introduced. In addition, service applications that can be utilized in the CPS environment were explored. By applying the proposed technology to the actual manufacturing site, improvements over the existing manufacturing system were derived. However, the proposed platform exhibited the following limitation: there is a large gap in the technology that can be applied depending on the required level of virtualization and automation of the production line. Therefore, in the future, it is necessary to identify a method that can reflect the various levels of the production site and to develop a platform technology tailored for the site.

**Acknowledgement**

This research was financially supported by the MOTIE and KIAT through the International Cooperative R&D program [P0009839] and supported under the Smart Manufacturing Innovation R&D program funded by the Korea Ministry of SMEs and Startups in 2022 [RS-2022-00140261].

**References**

1. Koren, Y., Gu, X., Guo, W.: Reconfigurable manufacturing systems: Principles, design, and future trends. Front. Mech. Eng. 13(2), 121–136 (2018). doi: 10.1007/s11465-018-0483-0.

2. Bortolini, M., Francesco G.G., Cristina M.: Reconfigurable manufacturing systems: Literature review and research trend. J. Manuf. Syst. 49, 93–106 (2018). 10.1016/j.jmsy.2018.09.005.

3. Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H., Noh, S.D.: Smart manufacturing: Past research, present findings, and future directions. Int. J. Precis. Eng. Manufacturing-green Technol. 3(1), 111–128 (2016). doi: 10.1007/s40684-016-0015-5.

4. Wiktorsson, M., Noh, S.D., Bellgran, M., Hanson, L.: Smart Factories: South Korean and Swedish examples on manufacturing settings. Procedia Manuf. 25, 471–478 (2018). doi: 10.1016/j.promfg.2018.06.128.

5. Kagermann, H., Wahlster, W., Helbig, J.: Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 (2013).

6. Kusiak, A.: Smart manufacturing. Int. J. Prod. Res. 56(2), 508–517 (2018). doi: 10.1080/00207543.2017.1351644.
7. Schroeder, G.N., Steinmetz, C., Pereira C.E., Espindola D.B.: Digital twin data modeling with automationml and a communication methodology for data exchange. IFAC-PapersOnLine 49(30), 12–17 (2016). doi:10.1016/j.ifacol.2016.11.115.

8. Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Schuh, G. Sihn, W., Ueda, K.: Cyber-physical systems in manufacturing, Cirp Ann. 65(2), 621–641 (2016). doi: 10.1016/j.cirp.2016.06.005.

9. Ivanov, D., Sethi, S., Dolgui, A., Sokolov, B.: A survey on control theory applications to operational systems, supply chain management, and Industry 4.0. Annu. Rev. Control 46, 134–147 (2018). doi: 10.1016/j.arcontrol.2018.10.014.

10. Yang, J., Lee, S., Kang, Y.-S., Noh, S.D., Choi, S.S., Jung, B.R., Lee, S.H., Kangg, J.T., Lee, D.Y., Kim, S.H.: Integrated Platform and Digital Twin Application for Global Automotive Part Suppliers. In: IFIP International Conference on Advances in Production Management Systems. Springer, Cham (2020).

11. Jung, J.S.: Logistics Innovation in the Automotive Industry. Research Report, Korea Institute for Industrial Economics & Trade, Sejong (2001).

12. Yun, S., Jun-Hong P., and Won-Tae K.: Data-centric middleware based digital twin platform for dependable cyber-physical systems. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), 17063600. IEEE, Milan, Italy (2017). doi: 10.1109/ICUFN.2017.7993933.

13. Lee, J., Behrad B., Hung-An K.: A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015). doi: 10.1016/j.mfglet.2014.12.001.

14. Rajkumar, R., Lee, I., Sha, L., Stankovic, J.: Cyber-physical systems: the next computing revolution. In: Design Automation Conference, pp. 731–736. IEEE, (2010). doi: 10.1145/1837274.1837461.

15. Ribeiro, L., Mats Björkman. Transitioning from standard automation solutions to cyber-physical production systems: an assessment of critical conceptual and technical challenges. IEEE Syst. J. 12(4) (2017): 3816–3827.

16. Tao, F., Zhang, M., Nee, A.Y.C.: Digital twin driven smart manufacturing. Technology & Engineering. Academic Press (2019).

17. Rosen, R., von Wichert, G., Lo, G., Bettenhausen, K.D. About the importance of autonomy and digital twins for the future of manufacturing. Ifac-PapersOnline 48(3), 567–572 (2015). doi: 10.1016/j.ifacol.2015.06.141.

18. Yun, S., Park, J.H., Kim, W.T.: Data-centric middleware based digital twin platform for dependable cyber-physical systems. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 922–926, IEEE (2017). doi: 10.1109/ICUFN.2017.7993933.

19. Lin, Y. C., Hung, M. H., Huang, H. C., Chen, C. C., Yang, H. C., Hsieh, Y. S., Cheng, F. T.: Development of advanced manufacturing cloud of things (AMCoT)—A smart manufacturing platform. IEEE Robotics and Automation Letters, 2(3), 1809–1816 (2017).

20. Xu, X.: From cloud computing to cloud manufacturing. Robotics and Computer-Integrated Manufacturing, 28(1), 75-86 (2012).

21. Hung, M. H., Li, Y. Y., Lin, Y. C., Wei, C. F., Yang, H. C., Cheng, F. T.: Development of a novel cloud-based multi-tenant model creation service for automatic virtual metatology. Robotics and Computer-Integrated Manufacturing, 44, 174–189 (2017).

22. Choi, J., Yang, J., Lym, J., Noh, S.D., Kang, Y.-S., Joe, Y.L., Lee, S.H., Kang, J.T., Song, J., Lee, D.Y., Kim, H.S.: Design and Implementation of Digital Twin-Based Application for Global Manufacturing Enterprises. IFIP International Conference on Advances in Production Management Systems. Springer, Cham (2021). doi: 10.1007/978-3-030-85914-5_2.