1. Introduction
With the rapid developments in science and technology, the production process in the manufacturing industry has been continuously improved and innovated. It has shifted from traditional manpower operation to automatic production. During transformation, besides the considerable investment, machine damages or delay in the production cycle may occur due to inconsistent design, interrupted working in the factory, and machine collisions when instruments are sent to institutions for testing. Using software to simulate the production line process in advance could effectively solve these problems.
The traditional development process of industrial automation systems (IASs), which was constructed with the integration of constituent parts such as mechanics, electronics and software, is criticized [1,2] for its incapacity to address the increasing complexity of original systems.
In Industry 4.0, the communication network and equipment have been built in the cyber-physical system (CPS) of the Internet of Things, bringing a new wave of revolution [3]. Cyber-physical integration technology, as the key technology in Industry 4.0, connects various devices, machines, and digital systems through computers, sensors, and network technology. The communication of machines and systems can allow the physical and virtual worlds to be integrated. The CPS adopted in this study can collect big data from entities, environments, and activities, so as to integrate cyberspace with physical space and achieve intelligent manufacturing.
Cyber-physical integration technology mainly employs 3D teaching materials to carry out situational simulation learning [4]. In the actual application, students may acquire knowledge from the situational background constructed by teachers. Situational simulation learning emphasizes that students can learn by combining situational learning with simulation teaching [5]. Students make judgments after carefully thinking through their logic, cognition, and observation, and can obtain immediate feedback on different decisions. Chau et al. [6] found that situational simulation can obtain students’ high satisfaction, and trigger their interests in courses, as compared with traditional learning modes. Safaei and Shafieiyoun [7] pointed out that the cyberlearning environment has been widely used in various fields such as medical, business, and education. Teachers can use a cyberlearning environment to induce a sense of presence in students, and the environment feels like a personal experience. Situation simulation can improve students’ interest in learning.
Cyber and physical worlds can be regarded as two networks with node interaction [8]. A cyber layer is composed of many intelligent monitoring nodes (including people, servers, information sites, or various mobile devices) and their communication links, while the physical layer contains many interrelated physical entities. Under the interaction of the cyber layer and physical layer, the system realizes information interaction and decision-making through 3C technology of calculation, communication, and control. Entities in the physical layer send information through the network to the sensing device in the environment. After obtaining the detected information, the components in the information layer automatically adjust the internal association and model, and transmit instructions to each component in the physical layer through the human–machine interface or driving device. Physical entities accept instructions and execute corresponding operations required by the system through autonomous coordination among entities. Gray-Hawkins and Lăzăroiu [9] explored cyber-physical smart manufacturing systems and made estimates regarding the link among industrial artificial intelligence, sustainable product lifecycle management, and the Internet of Things sensing networks. Cyber-physical smart manufacturing systems function in an automated, robust, and flexible manner by the use of Internet of Things-based real-time production logistics. Throughout cyber-manufacturing networks, production parts and smart sensors interconnect automatically, performing business process optimization and real-time big data analytics [10].
Heinich et al. [11] proposed that computer simulation must simplify the real situation and extract the essence of it so that students can be fully immersed in the situation. From the perspective of teaching, Lee [12] indicated that computer simulation can effectively help students to understand abstract knowledge through their active participation, thereby enhancing their learning motivation and improve their learning effectiveness. Lee et al. [13] found that the employment of information analysis and calculation can lead to more efficient execution, cooperation, and flexibility, and this trend will change the manufacturing industry.
Among the literature on the application of CPS to object classification simulation, Pai [14] adopted a potentiometer to determine the height of objects by classifying objects according to different heights and sent them to cyber factories for action simulation.
Based on the literature review, this study adopted the cyber factory software (Factory IO), programmable controller, human–machine interface, and entity mechanism to distinguish the colors of different materials. Specifically, an optical fiber amplifier was applied to detect the value of the material color. After the value was calculated and analyzed through a programmable logic controller, the system delivered the material to the virtual factory, which simulated the operations (feeding, processing, assembly, warehousing, and discharging functions).
As an extension version, this study differed from Shyr et al. [15]. This study incorporated the actual mechanism and cyber factory so that the status of the production line could be presented in the simulation software. This can prevent any unsmooth workflow that may arise later, and help in the planning and design of the production line to reduce the construction cost of the production line. Specifically, the purpose of this study is: (1) to construct a color discrimination system through which materials can be sorted according to their colors and then sent to the cyber factory for further processing; (2) to present the status of materials while they are in the physical factory by simulation software; and (3) to monitor plant equipment through human–machine interface operation.
The remainder of this study is organized as follows: Section 2 gives the cyber-physical integration technology overview and refers to related work. Section 3 presents the proposed system architecture descriptions. The operation of material color discrimination in the cyber factory is shown in Section 4. The discussion is shown in Section 5 and finally the study is concluded in the last section.
2. Cyber-Physical Integration Technology
The technical application of CPS includes five levels, and the architecture of this system is described as follows [16]: (1) smart connection level: collecting data from machines or components in an efficient, reliable, and accurate way and transmitting data to the information conversion layer; (2) data-to-information conversion level: collecting the data transmitted from the smart connection level and converting it into meaningful data for forecasting and management applications; (3) cyber level: a central information hub for the system architecture, and information from each of the connected machines forms a machinery network; after collecting a huge amount of information, specific analyses are required to extract more information in order to understand the status of each machine; these analyses can also provide machines with the ability of self-comparison with other similar machines or can compare machines at different times, which aid in a deeper understanding on system changes and the predicted statuses; (4) cognition level: after obtaining the information of other devices through the network layer and comparing with other instances, the machine is monitored; according to the historical data, some specific prediction algorithms are used to predict or estimate the time of equipment failure; and (5) configuration level: based on feedback from the cyberspace to physical space and through system supervision and control, operators or factory managers can make decisions. At the same time, the machine itself can reduce the loss of machine failure, which can be used to correct errors and take preventive measures.
The manufacturing industry has paved the way for a systematical deployment of CPS, within which, information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. At the early development phase, there is an urgent need for a clear definition of CPS. A unified 5-level architecture is proposed as a guideline for the implementation of CPS.
Rao et al. [17] proposed features of 5G technologies which list and describe how these features impact the industries and factories of the future, providing a unified communication platform needed to overcome the shortcomings of current communication technologies. Aydogan and Aras [18] designed a new basic programmable logic controller laboratory that could conduct simulation and virtual implementation. Then, a virtual programmable logic controller laboratory was introduced to students who had previously taken the programmable logic controller course theoretically, and a survey about this laboratory was administered. Based on this lab design and evaluations, the specific and general conclusions including the pros and cons were discussed. The cyber-physical spaces that can improve and help the ergonomic environment in the representation of different scenarios, which may open the field of study to develop more and better adaptive features that can better meet clients’ needs and lead to more complete studies and more information about the perception of the products and/or services [19].
Cheng [20] proposed that digital factories can solve problems, such as disconnected manufacturing sites, independent digital models, isolated data, and non-self-controlled applications. In order to push the current situation of digital factories forward towards smart manufacturing, this study presented an overview of the current digital situation of factories and proposed a systematical framework of cyber-physical integration in factories. As a reference, this paper is expected to bridge the gap in factories from the current digital situation to smart manufacturing to effectively facilitate smart production.
Cyber-physical systems are applied across a diverse number of areas including transportation, infrastructure, defense, and manufacturing process. Frolov et al. [21] established an architecture of a cyber-physical production system to provide an adaptive and autonomous system. Through their proposed system, tool wear and breakage monitoring can be assessed, as well as surface roughness monitoring.
This study applied cyber-physical integration technology to material color discrimination with its emphasis on the computing power of computers, the link between each entity device, and the computer computing network by integrating control systems. The operation mode of cyber-physical integration technology is shown in Figure 1. After being obtained by sensors, the information in the environment was sent through the physical layer. With the automatic adjustment of the internal association and model, the information layer components then sent the information to the cyber layer, which transmitted instructions to various components of the physical layer through the man–machine interface or driver.
Integration and coordination are two key elements of the proposed cyber-physical system. That is to say, to construct a system as a cyber-physical system, the rigorous integration and coordination between virtual models and the physical system facility are pivotal so that bidirectional coordination can be activated. One of the key characteristics of a cyber-physical systems approach is the presence of both a cyber-to-physical bridge and a physical-to-cyber bridge [22]. The former involves the actuation process with the sensed information processed by the system. The physical-to-cyber bridge is the sensing process in which sensing systems are implemented to recognize, distinguish, locate and apply the physical components to the virtual representation.
Generally speaking, cyber-physical systems (CPS) feature integrations of computerized network systems and physical processes [23,24]. With future technology developments [25] putting great emphasis on how to expand the capabilities of the physical world via computation, communication and control, a CPS approach takes great advantage of bi-directional coordination between the cyber-to-physical bridge and physical-to-cyber bridge.
3. System Architecture
3.1. Material Color Discrimination System
In this study, the material color discrimination system is a physical mechanism. It is composed of two sensors (FZ1-KP2 as the feed sensor and HG-C1100 as the discharge sensor), an optical fiber amplifier (E3NX-FA11AN), and a conveyor belt. The physical system architecture is shown in Figure 2.
Manual discharging was employed in the material feeding of this system. Materials with different colors and shapes were placed at the feeding position. When the material was detected by the feeding sensor, the color was identified through the optical fiber amplifier. When the programmable controller received the value from the optical fiber amplifier, the color of the material was identified. After the material color discrimination, the material was sent to the cyber factory by a conveyor belt. After the material was detected by the discharging sensor, this indicated the completion of the discharging of the physical mechanism. At this time, the programmable controller drove the corresponding relay to generate a material signal in the cyber factory for simulation action.
3.2. Cyber Factory System
This study adopted the simulation software (Factory IO) to construct the cyber factory. A Siemens programmable controller (PLC S7-1200), as the control core of the system, was matched with the internal parts of the simulation software such as a conveyor belt, processing station, and storage organization. Users can operate and monitor the internal operation of the cyber factory through the human–machine interface (HMI) and watch the internal operation animation of the cyber factory in real-time on a personal computer (PC). The cyber factory system architecture diagram of this study is shown in Figure 3.
In general, a PLC is an automation device that generates an output signal from the signals coming from the inputs according to the compiled code within, and repeats this continuously over a certain time interval. Inputs are typically buttons, switches, inductive, capacitive and optical sensors. Outputs are typically lamps, relays, contactors and motors. In order to prepare a PLC code, Ladder programming method is generally used. In terms of software or hardware, there are inputs, outputs and memory expressions, timers and counters in a PLC. In this study, the PLC S7-1200 programming panel designed in SL consists of input and output cells. When an empty cell is clicked, the coils and contacts that can be placed in that cell appear on the screen and the user makes the selection. Figure 4 shows the flowchart and symbol that can be used when programming in the designed PLC and virtual system.
After simulating the PLC program that the user has designed, when the user clicks on the double play button, the virtual implementation equipment, such as buttons, lamps, sensors and conveyor belt are activated. In order for the virtual implementation equipment to interact with the PLC first, a PLC input/output assignment must be made. For example, the motor of the conveyor belt can be clicked and associated with the output of the PLC or the sensor at the end of the belt can be clicked to associate it to the input of the PLC. This allows the user to first design the PLC program, simulate the design he or she is doing, and examine how the equipment reacts in a virtual laboratory environment using the virtual implementation equipment. The video showing the design, simulation and virtual implementation of the PLC program that enabled the conveyor belt had applied to material color discrimination and employed as the operating spindle to integrate the system of the physical organization and the automated virtual factory constructed by the simulation software of Factory IO to achieve the technical application with automation mode displayed using the link
In this study, a cyber factory was planned through the built-in industrial components of Factory IO simulation software including sensors, conveyor belts, pallets, processing stations and warehouses [26], as shown in Figure 5.
4. Operation of Material Color Discrimination in Cyber Factory
4.1. Material Color Data Collection
In this study, a color test was conducted on four different materials (square blue, square gray, circular blue, and circular gray). (1) The test on the square blue material is shown in Figure 6; (2) the test on the square gray material is shown in Figure 7; (3) the test on the circular blue material is shown in Figure 8; and (4) the test on the circular gray material is shown in Figure 9.
The human–machine interface in color detection test mode was the benchmark for setting the colors of various materials. Normal materials were divided into four types: square blue, square gray, circular blue, and circular gray. Other materials were determined as abnormal. As the light and the material surface concave and convex degree would affect the value received by the optical fiber amplifier, the color of various materials was represented in a range of values. After the optical fiber amplifier emitted red light on the material, it would receive reflected light from the material and transform it into a value.
According to the test results for material colors shown in Figure 10, the square blue color interval is from K844 to K1072, the square gray color interval is from K1981 to K1983, the circular blue color interval is from K464 to K488, and the circular gray color interval is from K723 to K793. After setting or modifying the value ranges of various material colors, the system would use these values as the basis for color discrimination in automatic mode.
The degree of receiving the reflected light from the material was converted into a numerical value that was transmitted to a programmable controller (FX3GE) for calculation. The numerical range of various material colors was then distinguished.
4.2. Operation of Cyber Factory
In this study, the cyber factory carried materials on the conveyor belt of the production line after identifying the color of materials through the physical mechanism. The solid circular materials represented the upper covers and were sent to the upper processing station for processing. The solid square materials represented the bases which were sent to the processing station below for processing and then sent to the next area for assembly after processing.
The processed materials of upper covers and bases were sent to the double-axis selector through the conveyor belt. The upper covers were moved from the upper conveyor belt to cover the bases on the lower conveyor belt by suction to complete the material assembly. Afterwards, the assembled materials were moved to the pallet through another two-axis selector and sent to the rail pallet for storage. By matching the pallet with the cart, the materials were accurately placed in the rack positions corresponding to various colors.
After the operator placed the material in the feeding area, the system automatically started to identify the color and shape of the material and displayed the color of the material in the status description box. After the identification was complete, the value of the material color was displayed on the HMI screen, and the conveyor and corresponding relay were driven at the same time to send the material to the discharging position of the physical mechanism with the information of the material being transmitted to the PLC of the virtual factory. After the material was detected by the discharge sensor of the physical organization, the virtual factory automatically generated the material and executed the next phase of the program, as shown in Figure 11.
Based on different color combinations of materials, cyber warehousing stored the materials in corresponding regional racks. The warehouse storage status page on the human–machine interface synchronously displayed the storage situation of the cyber warehouse so that users could monitor the internal operation of the cyber factory in both cyber factory software and physical organizations. Figure 12 shows the discharge mode including the status in the HMI and warehouse of the cyber factory built by simulation software.
5. Discussion
The cyber-physical systems approach has been employed in other industry sectors. According to Crenshaw et al. [27], cyber-physical systems can control and monitor the physical world with component-based, real-time systems. Lee [23] concluded that as dynamic systems, cyber-physical systems integrate physical processes with computation, with the former and the latter affecting each other in feedback loops. Rajkumar et al. [28] proposed that the operations of cyber-physical systems, which are physical and engineered systems, are activated and coordinated by a computing and communication core. Tang et al. [29] also regarded cyber-physical systems as a situation-integrated analytical system that responds intelligently to dynamic changes of the real-world scenarios with the integration of physical devices (e.g., sensors, cameras) and cyber components.
By integrating CPS with production and services in the industrial practices, it would reconstruct today’s factories to be Industry 4.0 factories with remarkable economic potential [30,31]. For instance, the Fraunhofer Institute and the industry association published a joint report, suggesting that German gross value can be enhanced by a cumulative 267 billion Euros by 2025 [32].
There exist challenges when tackling the subject of Industry 4.0, as argued in Kagermann et al. [33]. One of the challenges is the general reluctance of stakeholders to make changes. Both perspectives agree on what challenges have to be overcome in order to achieve what they pursue, and these challenges include analyses of big data information, interoperability, and scalability, among others [34]. So far, smart manufacturing approaches, analysis, virtualization, and new subjects such as Industry 4.0 and big data have been studied. Summarizing related works and developments leads to the focus on the aspects faced by Industry 4.0, such as methodologies that integrate collaborative systems. This study suggested that a well-funded methodology that integrates CPS and virtual designs is the key to achieving innovation and high productivity.
The COVID-19 pandemic has affected the operations of factories worldwide [35]. However, the impacts of the COVID-19 pandemic are not the same on all factories. In other words, the robustness of factories under the COVID-19 pandemic varies. This study applied CPS technology to material color discrimination. Specifically, an optical fiber amplifier detected the material’s color, and then, the system delivered the material to the virtual factory, simulating its operations.
Robots are expected to be used in smart manufacturing and smart factories with the help of information and communication technologies [36]. The mechatronic systems become cyber-physical systems through additional communication skills and autonomy in behaviors on the external influences and internally stored settings. Robots are integrated into the cyber-physical production system to realize smart factories and smart manufacturing. In a laboratory environment, innovative logistical systems integrated with robots were built to apply horizontal and vertical integration.
A PLC system has been produced in the virtual world for students who are trained in PLC programming. These students have been given a certain time to perform the design, simulation and implementation of a PLC question in automation system. Even if the students are out of class hours or even if they are not online, they can still complete the tasks assigned to them in this world on their own, or on a collaborative basis with other students.
During the evaluation and analysis of the existing presented solutions, several limitations can be deduced and discussed as follows: (1) The asymmetric nature of certain cryptographic work [37,38,39] leaves CPS’s real-time communication susceptible to network latency and overhead due to delays in the encryption/decryption process. (2) Lack of firewall protection: firewall solutions including [40,41] are not very applicable and suitable for applications to the CPS domain. Dynamic firewalls should be the key solution. (3) The CPS forensic’s field still faces many challenges including the lack of tools, skills and responses against any potential anti-forensics activity [42,43].
6. Conclusions
High quality hands-on practical exercises are essential for students in order to produce work-ready graduates. Due to the high cost of automation system equipment, it has always been a challenge. The material color discrimination system developed in this study can provide references for developing virtual and interactive learning platforms in related fields. In addition, for academic institutions or training organizations with limited budgets, the system can not only reduce costs but also help achieve expected learning results.
In this study, the optical fiber amplifier was used as the main component in material color discrimination. The numerical intervals of various material colors were calculated through the operation and the determination of a programmable controller. The cyber factory was controlled by a programmable controller (S7-1200), so that the simulated materials could be processed and stored.
Two programmable controllers of different brands were connected by the relay. While the output terminal of one controller controlled the relay, the input terminal of the other controller received the operation status of the relay. The reasoning behind this arrangement is that these two programmable controllers complete the function of the material entity and cyber coherence. The human–machine interface is also a part of cyber-physical integration. Cyber buttons were used to replace traditional buttons, which greatly increased the flexibility of system design. Through the human–machine interface, users can run the operating system and monitor the real-time storage status in the warehouse of the cyber factory. By doing so, the cyber-physical integration function between the physical organization and the cyber factory is achieved.
The material color discrimination system in this study is only aimed at the functional integration of physical organizations and cyber factories. Some suggestions were made for the prospect of this system. First, for manual feeding, which was employed by the material feeding mode of the physical mechanism, it is suggested that manual feeding can be matched with an electromechanical integrated gantry mechanism in the future so that the feeding part can adopt an automatic feeding mode. Second, the cyber factory has the functions of feeding, processing, and storing materials in the warehouse only. The function of material discharging is suggested to be added in the future so that materials can enter and exit the physical organizations and cyber factories. After this addition, the physical organization can then be connected with the discharging end. Third, as the Internet of Things and big data have been widely used in the manufacturing industry, it is suggested that in the future, the quantity or storage status of materials on the human–machine interface and system can be stored in the cloud through the network and real-time information can be accessed through various mobile devices. Fourth, the optical fiber amplifier for color discrimination in this study is very susceptible to the influence of external environmental factors, which leads to a fluctuation in material color values and potential errors in color discrimination. Therefore, it is suggested that the sensors for color discrimination research in the future be made with components that are less susceptible to external environmental factors.
All authors contributed meaningfully to this study. W.-J.S., Y.-J.C. and H.-C.J.—research topic; Y.-J.C., H.-C.J. and C.-Y.T.—data acquisition and analysis; H.-C.J. and W.-J.S.—methodology support; W.-J.S., C.-Y.T., Y.-J.C. and H.-C.J.—original draft preparation; H.-C.J., C.-Y.T. and W.-J.S.—writing review and editing. All authors have read and agreed to the published version of the manuscript.
This research received no external funding.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Rzevski, G. On conceptual design of intelligent mechatronic systems. Mechatronics; 2003; 13, pp. 1029-1044. [DOI: https://dx.doi.org/10.1016/S0957-4158(03)00041-2]
2. Mhenni, F.; Choley, J.Y.; Penas, O.; Plateaux, R.; Hammadi, M. A SysML-based methodology for mechatronic systems architectural design. Adv. Eng. Inform.; 2014; 28, pp. 218-231. [DOI: https://dx.doi.org/10.1016/j.aei.2014.03.006]
3. Brettel, M.; Friederichsen, N.; Keller, M.; Rosenberg, M. How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. Int. J. Inf. Commun. Eng.; 2014; 8, pp. 37-44.
4. Masahiro, T. Cyber-physical integrated analysis technology for criminal investigation support. NEC Tech. J.; 2017; 12, pp. 85-88.
5. Wang, H.; Ning, X.; Sun, J.; Pan, Y.; Xing, H. Application of situational simulation teaching method in leadership science and art course. Teach. Educ. Curric. Stud.; 2020; 5, pp. 7-13.
6. Chau, M.; Sung, W.K.; Lai, S.; Wang, M.; Wong, A.; Chan, K.W.Y.; Li, T.M.H. Evaluating students’ perception of a three-dimensional virtual world learning environment. Knowl. Manag. E-Learn; 2013; 5, pp. 323-333.
7. Safaei, A.M.; Shafieiyoun, S. Enhancing learning within the 3D virtual learning environment. J. Knowl. Manag. Econ. Inf. Technol.; 2013; 3, pp. 1-6.
8. Crowder, R. Cyber Physical Systems and Security; 2nd ed. Chapter in Electric Drives and Electromechanical Systems Elsevier: Butterworth-Heinemann, Germany, 2020; pp. 271-289.
9. Gray-Hawkins, M.; Lăzăroiu, G. Industrial artificial intelligence, sustainable product lifecycle management, and internet of things sensing networks in cyber-physical smart manufacturing systems. J. Self-Gov. Manag. Econ.; 2020; 8, pp. 19-28.
10. Andronie, M.; Lăzăroiu, G.; Ștefănescu, R.; Uță, C.; Dijmărescu, I. Sustainable, smart, and sensing technologies for cyber-physical manufacturing systems: A systematic literature review. Sustainability; 2021; 13, 5495. [DOI: https://dx.doi.org/10.3390/su13105495]
11. Heinich, R.; Molenda, M.; Russell, J.D. Instructional Media and the New Technologies of Instruction; John Wiley & Sons Press: Hoboken, NJ, USA, 1989.
12. Lee, J. Effectiveness of computer-based instruction simulation: A meta-analysis. Int. J. Instr. Media; 1999; 26, pp. 71-85.
13. Lee, J.; Bagheri, B.; Kao, H.A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett.; 2015; 3, pp. 18-23. [DOI: https://dx.doi.org/10.1016/j.mfglet.2014.12.001]
14. Pai, H.T. Object Classification Applied in Simulation Factory. Master’s Thesis; National Changhua University of Education: Changhua City, Taiwan, 2018.
15. Shyr, W.J.; Shih, F.Y.; Chang, Y.J. Applying the cyber-physical integration technology on material color discrimination. Proceedings of the International Symposium on Intelligent Signal Processing and Communication Systems, Session: Applications of Internet of Things for Automation Engineering; Hualien, Taiwan, 16–19 November 2021.
16. Shafiq, S.I.; Sanin, C.; Szczerbicki, E.; Toro, C. Virtual engineering object/virtual engineering process: A specialized form of cyber physical system for Industrie 4.0. Procedia Comput. Sci.; 2015; 60, pp. 1146-1155. [DOI: https://dx.doi.org/10.1016/j.procs.2015.08.166]
17. Rao, S.K.; Prasad, R. Impact of 5G technologies on industry 4.0. Wirel. Pers. Commun.; 2018; 100, pp. 145-159. [DOI: https://dx.doi.org/10.1007/s11277-018-5615-7]
18. Aydogan, H.; Aras, F. Design, simulation and virtual implementation of a novel fundamental PLC laboratory in a 3D virtual world. Int. J. Electr. Eng. Educ.; 2022; [DOI: https://dx.doi.org/10.1177/0020720919856249]
19. Gutiérrez-Martínez, Y.; Navarro-Tuch, S.A.; López-Aguilar, A.A.; Bustamante-Bello, R.; Gutierrez-Martinez, Y.; Navarro-Tuch, S.A.; Lopez-Aguilar, A.A.; Bustamante-Bello, M.R. Environmental impact for labor stations learning through emotional domotics analysis and workstation simulation. Proceedings of the IEEE International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE); Cuernavaca, Mexico, 26–29 November 2018; pp. 124-128.
20. Cheng, Y.; Zhang, Y.; Ji, P.; Xu, W.; Zhou, Z.; Tao, F. Cyber-physical integration for moving digital factories forward towards smart manufacturing: A survey. Int. J. Adv. Manuf. Technol.; 2018; 97, pp. 1209-1221. [DOI: https://dx.doi.org/10.1007/s00170-018-2001-2]
21. Frolov, E.; Krainev, D.; Tikhonova, Z. Cyber-physical machining systems based on commercial CNC equipment. Proceedings of the 2018 International Russian Automation Conference; Sochi, Russia, 9–16 September 2018; pp. 1-4.
22. Wu, F.J.; Kao, Y.; Tseng, Y. From wireless sensor networks towards cyber-physical systems. Pervasive Mob. Comput.; 2011; 7, pp. 397-413. [DOI: https://dx.doi.org/10.1016/j.pmcj.2011.03.003]
23. Lee, E.A. Cyber physical systems: Design challenges. Proceedings of the 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing; Orlando, FL, USA, 5–7 May 2008; pp. 363-369.
24. Akanmu, A.; Anumba, C.; Messner, J. Scenarios for cyber-physical systems integration in construction. J. Inf. Technol. Constr.; 2013; 18, pp. 240-260.
25. Baheti, R.; Gill, H. Cyber-physical Systems. Impact Control Technol.; 2011; 1, pp. 161-166.
26. Real Games. Factory Manual. Available online: https://docs.factoryio.com/manual/index.html (accessed on 30 January 2022).
27. Crenshaw, T.L.; Gunter, E.; Robinson, C.L.; Sha, L.; Kumar, P.R. The simplex reference model: Limiting fault-propagation due to unreliable components in cyber-physical system architectures. Proceedings of the 28th IEEE International Real-Time Systems Symposium; IEEE Computer Society: Washington, DC, USA, 2007; pp. 400-412.
28. Rajkumar, R.; Insup, L.; Lui, S.; Stankovic, J. Cyber-physical systems: The next computing revolution. Proceedings of the ACM/IEEE Design Automation Conference; Anaheim, CA, USA, 13–18 June 2010.
29. Tang, L.A.; Yu, X.; Kim, S.; Gu, Q.; Han, J.; Hung, C.; Leung, A.; Porta, T.L. Tru-Alarm:Trustworthiness analysis of sensor networks in cyber-physical system. Proceedings of the 2010 IEEE International Conference on Data Mining; Sydney, Australia, 14–17 December 2010.
30. Mendiratta, S.; Mathur, S.; Tanwar, D.; Sharma, S. Evaluating and exploring industry 4.0 framework. Proceedings of the Second International Conference on Information Management and Machine Intelligence; Jaipur, India, 24–25 July 2020; pp. 41-49.
31. Lee, J.; Lapira, E.; Yang, S.; Kao, H.A. Predictive manufacturing system-Trends of next generation production systems. IFAC Proc. Vol.; 2013; 46, pp. 150-156. [DOI: https://dx.doi.org/10.3182/20130522-3-BR-4036.00107]
32. Lee, J. Industry 4.0 in big data environment. Ger. Harting Mag. Technol. Newsl.; 2013; 26, pp. 8-10.
33. Kagermann, H.; Wahlster, W.; Helbig, J. Recommendations for Implementing the Strategic Initiative Industrie 4.0—Securing the Future of German Manufacturing Industry—Final Report of the Industry 4.0 Working Group; Acatech, National Academy of Science and Engineering: Munich, Germany, 2013.
34. Flores-Saldivar1, A.A.; Li, Y.; Chen, W.N.; Zhan, Z.H.; Zhang, J.; Chen, L. Industry 4.0 with cyber-physical integration: A design and manufacture perspective. Proceedings of the 21st International Conference on Automation & Computing; Glasgow, UK, 11–12 September 2015; pp. 1-6.
35. Chen, T.; Wang, Y.C.; Chiu, M.C. Assessing the robustness of a factory amid the COVID-19 pandemic: A fuzzy collaborative intelligence approach. Healthcare; 2020; 8, 481. [DOI: https://dx.doi.org/10.3390/healthcare8040481] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33198367]
36. Liu, Q.; Hua, P.; Sultan, A.; Shen, L.; Mueller, E.; Boerner, F. Study of the integration of robot in cyber-physical production systems. Proceedings of the 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery; Guilin, China, 17–19 October 2019; pp. 367-370.
37. Yaacoub, J.A.; Salman, O.; Noura, H.N.; Kaaniche, N.; Chehab, A.; Malli, M. Cyber-physical systems security: Limitations, issues and future trends. Microprocess. Microsyst.; 2020; 77, 103201. [DOI: https://dx.doi.org/10.1016/j.micpro.2020.103201] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32834204]
38. Vegh, L.; Miclea, L. Secure and efficient communication in cyber-physical systems through cryptography and complex event processing. Proceedings of the 2016 International Conference on Communications; Kuala Lampur, Malaysia, 23–27 May 2016; pp. 273-276.
39. Zhao, Y.; Li, Y.; Mu, Q.; Yang, B.; Yu, Y. Secure pub-sub: Blockchain-based fair payment with reputation for reliable cyber physical systems. IEEE Access; 2018; 6, pp. 12295-12303. [DOI: https://dx.doi.org/10.1109/ACCESS.2018.2799205]
40. Jiang, N.; Lin, H.; Yin, Z.; Xi, C. Research of paired industrial firewalls in defense-in-depth architecture of integrated manufacturing or production system. Proceedings of the 2017 IEEE International Conference on Information and Automation; Macao, China, 18–20 July 2017; pp. 523-526.
41. Nivethan, J.; Papa, M. On the use of open-source firewalls in ICS/SCADA systems. Inf. Secur. J. A Glob. Perspect.; 2016; 25, pp. 83-93. [DOI: https://dx.doi.org/10.1080/19393555.2016.1172283]
42. Ahmed, I.; Obermeier, S.; Naedele, M.; Richard, G.G. SCADA systems: Challenges for forensic investigators. Computer; 2012; 45, pp. 44-51. [DOI: https://dx.doi.org/10.1109/MC.2012.325]
43. Ahmed, I.; Obermeier, S.; Sudhakaran, S.; Roussev, V. Programmable logic controller forensics. IEEE Secur. Priv.; 2017; 15, pp. 18-24. [DOI: https://dx.doi.org/10.1109/MSP.2017.4251102]
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Hou-Chueh, Juan; Chih-Yu, Tsai; Yu-Jia, Chang