Uploaded by Vinayak Petlae

118220393 Petlae Literature

Vinayaka Narayan Raju Petlae – 118220393
Supervised By: Prof.Dirk Pesch
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 [1].
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.
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 [2][3].
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 [4].
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 [4], [5], [6].
Figure 1. Fourth Industry Revolution [2]
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 [2], [1].
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
Figure 2. Interplay of Computer science and manufacturing technology [8]
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 [7], [8].
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
Figure 3. 5C Architecture [5]
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 [9], [10].
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
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 [5],
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
Figure 4. RAMI 4.0 Architecture [2]
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 [2], [12], [13].
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,
Figure 5. Cloud-DPP in cyber-workspace [6]
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[6].
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 [6].
Figure 6. 3D model-driven remote assembly as a CPS [6]
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
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
manufacturing framework. Data security and Data quality are some of the other technological
issues [2].
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 [2].
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 [2].
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 [6].
Figure 7. Human-Robot Collaboration [6]
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. Currently, a lot of research efforts has been invested in developing the
framework and strengthing the R&D institutions of cyber-manufacturing. However, limited by
the existing theory and technology, CPS development is also facing big challenges. Proper
breakthrough in CPS key technology will enable any country to take the world’s leading position
in CPS development so that they can independently set country’s standard and raise national and
economic development.
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