1 CYBER-PHYSICAL MANUFACTURING SYSTEM – A REVIEW Vinayaka Narayan Raju Petlae – 118220393 Supervised By: Prof.Dirk Pesch I. INTRODUCTION The project focuses on a review of Cyber-Physical Manufacturing System(CPMS). This paper presents the research areas in the field of CPMS and its interconnection with Industry 4.0 or smart manufacturing. Cyber manufacturing is an idea derived from Cyber-Physical System (CPS) that refers to the modern manufacturing system which helps to facilitate asset control, provide reconfigurability, and keep productiveness. Many countries including Germany and US are supporting industry 4.0 to transform Cyber-Manufacturing Systems as the next generation of smart manufacturing. Furthermore, Using CPS, both real and virtual worlds are integrated into the internet environment that leads to a foundation for Cyber-Physical Manufacturing. It is also defined as a future in which the machines work 24/7, and all humans and devices are connected to the internet which controls manufacturing processes and services.Finally, at the end of the paper, all the findings are summarized in the conclusion section of the review and provide some remarks regarding future research in the field of CPMS. The literature review also presents the background, key terminologies, research areas, comparison of traditional and modern approaches,current solutions and future scope of Cyber-Manufacturing . A. Goal and Motivation The goal of the thesis is to review the key innovations in the area of CPMS and identify the open issues and problems under consideration of Cyber-Physical Systems. Internet-enabled services which are adding business values to various sectors like retail, healthcare, and transportation can be considered as a key motivation in stemming of Cyber-Manufacturing.There are ample functionalities of Cyber-Manufacturing such as machine connectivity, manufacturing reconfigurability, machine health prognostics, and asset management. 2 II. SCOPE AND DEFINATIONS The scope of this review comprises an overview of smart manufacturing initiatives, application examples, and research issues. The study also focuses on different architectural frameworks in cyber-physical systems(CPS). The related terms and the initiatives in CPS and smart manufacturing are described below: A. Industry 4.0 Germany is one of the most competitive manufacturing industries in the world and it plays a crucial role is mastering the challenges of the fourth industry revolution. Industry 4.0 comprises paradigm of shifting from automated manufacturing to intelligent manufacturing. In the growth of the physical and virtual world, the objects including machines are equipped with actuators and sensors. Implementation of intelligent manufacturing makes use of IoT concepts to facilitate change. Industry 4.0 comprises of various technologies and paradigms, which includes Enterprise Resource Planning (ERP), Radio Frequency Identification (RFID), Internet of Things (IoT), cloud-based manufacturing, and social product development . B. Cyber-Physical Systems(CPS) The increased importance in the interaction between physical and virtual world, in 2006 the US coined the new concept called Cyber-physical systems (CPS). CPS is considered to be a multidisciplinary system that handles feedback control on distributed embedded computing systems by the combination of communication, computation, and various control technologies . Initially, CPS has been driven from the directions of computer science and electrical engineering. In recent developments, an increase in the availability and affordability of sensors, actuators and computer networks, today’s competitive natures forces factories to implement high-tech methodologies.CPS can be monitored by computer-based algorithms. Furthermore, integrating CPS with manufacturing in modern industrial practices would increase economic potential by transforming factories into Industry 4.0. Examples of CPS may include autonomous vehicle, robotics, smart home, automative pilot and evolution of smart devices. CPS applications extends in various fields like digital medical instruments and systems adopting automatic acquisition and control technology, distributed energy systems, aerospace and aircraft control, industrial control and so on. Change in the function of existing engineering physical systems; CPS brings huge economic benefits , , . 3 Figure 1. Fourth Industry Revolution  C. Smart manufacturing Smart manufacturing enable to access information of the manufacturing process when it is needed, where it is needed, and in the form that it is needed across entire manufacturing supply chains, product lifecycle and large enterprises. It is a collection of numerous technologies, including but not limited to cyber manufacturing, IoT, robotics, and big data analytics. Considering the comprehensive scope of smart manufacturing, its three main objectives are sustainable production, plantwide optimization, and agile supply chains , . III. BACKGROUND So far the three industrial revolutions have led to the significant changes in the manufacturing domain – water and steam power, mass work production and automation. Over the past years, many industries and researches together advocated a new industrial revolution called Industry 4.0 or fourth Industry revolution(see Fig.1). A. Evolution of Computer Science and Manufacturing Automation Over the past years, there is a parallel development in the field of computer science and manufacturing technology.Looking at the Fig.2, a kind of convergence can be observed between the physical and virtual worlds.The computer’s development leads to the numerical control 4 Figure 2. Interplay of Computer science and manufacturing technology  of machine tools. The microprocessor, computer graphics, and computer networks constitute and results in the development of computer numerical control (CNC), computer-aided design (CAD) and manufacturing systems respectively. Databases, Artificial Intelligence(AI) and machine learning revolutionized computer-integrated manufacturing (CIM), intelligent manufacturing systems(IMS) and robotics. Evolution of embedded systems helped in the development of smart manufacturing. Summarizing the above interplay, undoubtedly computer science contributed to development in manufacturing , . B. Development of Cyber-Physical Manufacturing Systems(CPMS) Over the last decades, extensive development and research activities in the embedded system result in the generation of smart sensors and actuators which is capable of communication and computation. Nowadays, these are implemented in manufacturing industries. However, this kind of CPS devices offers high potential through the interconnection of the cyber and physical world; they are yet to be exploited in the field of manufacturing. CPMS can be referred to as the highest level of manufacturing application in the field of CPS. The real and virtual manufacturing system representations are fused into one CPS by exchanging of data.To implement the CPMS reliable networks of smart resources with common data semantics are necessary. Smart sensors are used to monitor the physical environment, and smart actuators are used to change the parameters of 5 Figure 3. 5C Architecture  the physical systems. The are other issues which are important like data protection and security, new methods for big data analysis. To reach the world-wide industry implementation, numerous research projects have conducted on Cyber-Physical Manufacturing Systems , . IV. CPMS ARCHITECTURAL FRAMEWORKS Most of the Cyber-physical manufacturing system architectures will use four or five levels. The lowest level includes physical devices such as sensors, robots, etc. The middle levels connect to process-level control like coordination among machines and material, production plans and possible simulation. The Top-most level indicates the enterprise-wide connectivity which may include supply-chain interconnectivity. A. 5C-Architecture 5C architecture is one of the frameworks for implementation of the cyber-physical manufacturing system(CPMS). The five levels cyber-physical system(CPS) structure is shown in Fig.3, which guide step-by-step for developing and deploying CPS in the smart factory. The 5C-level functions are described below: Level 1(Smart-Connection): It involves acquiring reliable and accurate data from the machines and their components. These data can be from add-on sensors, enterprise management system 6 like ERP, maintenance logs and IoT machines. Data streaming and proper sensor selection are key importances in this level. Level 2(Data-to-information conversion):Here, the data collection in the level-1 is processed and converted to some meaningful information. This level of architecture also bring self-awareness to machines and focuses on developing algorithms specifically for applications like prognostics and health management. Level 3(Cyber):This mid-layer acts as a center information hub in the architecture where all information is processed. Information sharing, time machine records of machines health history and peer-to-peer comparisons are analyzed. This analyzation provides machines self-comparison ability where a machine can compare itself to another machine and also similarities between machine performances can be measured. Level 4(Cognition):The goal of this level is to generate sufficient knowledge of the system monitored and provides consistent information to compare the effect of different components within the system. Having proper Info-graphics in this level helps to transfer complete information to the users. Level 5(Configuration):It is the feedback from the cyberspace to physical space where actions can be taken as supervisory control so that machines can self-adaptive, self-maintained and selfconfigured. This level also acts as a resilience control system to infer preventive and corrective decisions that were made in level 4. This 5C architecture helps the manufacturing industries to implement CPS for better product quality and system reliability with more intelligent and resilient manufacturing equipment , . B. RAMI 4.0 Architecture For the first time, Reference Architectural Model Industrie 4.0(RAMI 4.0) framework helps in combining all crucial elements of industry 4.0 in a three-dimensional model. Based on this framework, technologies of industry 4.0 can be furthered developed and classified. The three axis of the RAMI 4.0 is shown in Fig.4 and explained below: The “Hierarchy Levels” axis: The Hierarchy levels from IEC 62264(International standards series for Enterprise IT and control systems) is indicated on the right horizontal axis of Fig.4.The axis represents the various functionalities within factories or facilities. These functionalities 7 Figure 4. RAMI 4.0 Architecture  have been expanded so that they include different workpieces(“Product”), and the connection to IoT(“Connected world”). The “Life Cycle & Value Stream” axis: IEC 62890 represents the life cycle of facilities and products on the left horizontal axis. Furthermore, there is a distinction made between “types” and “instances.” In general, a ”type” becomes an “instance” once the design, prototyping, and manufacturing of the product is completed The “Layers” axis: The six layers on the vertical axis in Fig.4 describes the decomposition the virtual mapping of a machine. This representation comes from the information and communication technology, where properties of complex systems are shattered into layers. Thus, RAMI 4.0 helps to classify objects according to the model and allows periodic migration into the world of Industry 4.0 , , . V. CURRENT SOLUTIONS This section provides example approaches of CPS in manufacturing. Two of the proposals presented here are A. Cloud manufacturing Cloud-DPP (Cloud-based Distributed Process Planning) is an approach proposed by the research of KTH and Sandvik; they aimed for distributive and adaptive cloud-based process plan in the cyber workspace. Considering the machines capability, availability, and real-time information, 8 Figure 5. Cloud-DPP in cyber-workspace  Cloud-DPP can able to generate plans for machining/manufacturing process adaptively allowing the changes through decision making. The four modules can be seen in the loop in the flow of information in Fig.5.Generating of Cloud-DPP machining process can be achieved by linking embedded sensors to a cloud manufacturing in the cyber workspace. Furthermore, the process plans which are in the form of function blocks are delivered to the machine controller on the physical shop floor for the execution. Adaptive machining and process planning are achieved by precisely dividing process tasks and allocating them to cloud and function blocks. B. Model-driven manufacturing This approach mainly focuses on how an operator can handle the physical robot instantly via a virtual robot in the cyber -workspace. This process can be achieved using a 3D-model which helps the operator to overcome the negative constraints over the internet. The 3D-models of the manufacturing parts in the factory generated based on an electronic control unit assembly system. These generated models are integrated with the 3D-model of the robotic cell. The robot can act as a manipulator, which mimics the operator’s manufacturing operations from a standard distance. As shown in Fig.6, the operator can assemble using 3D-model in cyber-world which in turn reflects the assembling of the real ’parts’ in the physical world. This process happens more instantly and automatically leading to the virtual-to-real assembly paving towards future factories . 9 Figure 6. 3D model-driven remote assembly as a CPS  The above approaches may help to foresee the future of CPS manufacturing, but it’s not an easy task as there is no more standard solutions/strategies that help smart manufacturing.Based on these approaches, it is inferred that there is no “silver bullet” to address cyber-manufacturing needs. VI. RESEARCH ISSUES This section deals with the current research issue based on section 4 and 5: A. Technological Research Issues Many companies might choose different proprietary solutions or systems which are available in the market, where as some companies may use self-developed solutions. When these two categories of companies agree to work together, interoperability is a prominent issue that should be addressed to enable smart manufacturing. Data analytics is another aspect that is grouped under the technological issue. Besides the more algorithm-based core of data analytics, there is a requirement for the methods which can connect existing or newly based algorithms to the 10 manufacturing framework. Data security and Data quality are some of the other technological issues . B. Methodological Research Issues As we seen in the previous section reference model and standards are used to represent the complex concepts, following this, a variety of issues may arise. The issue is, there is a need to establish common definitions of fundamental concepts and to develop a social-technical evolution process for the reference model. Visualization helps in communicating complex manufacturing information. Example, sending of results of data analytics to the inside and outside stakeholders of an organization. This is quite challenging as stakeholder may have different requirement and granularity in the way results are displaying . C. Business Case Issues With the advent of smart manufacturing, detailed manufacturing data is available for advanced analytics. However, there is a privacy issue for specialized manufacturers. Investment stands out to be another issue, where implementation of smart manufacturing framework in the small and medium manufacturing enterprise may require significant investment. CPS includes various hardware and software security issues along with operational issues which should be taken into consideration for safety and dependability reasons . VII. APPLICATION SCENARIO The application scenario was selected to make a broad variety to highlight the scope of CPS and smart manufacturing in a small and medium-sized enterprise. Symbiotic human-robot collaboration is one scenario where fenceless environment which improves productivity and resource effectiveness. CPS enables such human-robot collaboration. As shown in Fig.7, robots can be instructed by humans by signs, gestures, speech and their combinations during collaborative assembly. While monitoring the assembly of the machine, the operator’s hand is tracked by the system, and the robot’s end follows the operator’s hand. The human can also specify collaboration modes by voice commands . 11 Figure 7. Human-Robot Collaboration  VIII. CONCLUSION This paper presents the current focus of cyber-physical systems and smart manufacturing in the fourth industrial revolution. The background and evolution of cyber manufacturing systems are outlined along with the goals and motivations. After the background of CPS were presented, different CPMS architecture frameworks were derived and illustrated. An application scenario was presented that highlighted the scope of manufacturing in smart industry. Furthermore, three various research issues were introduced, which outlines the problems in different sectors of smart manufacturing. 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