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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
FACTORIES OF THE FUTURE The book provides insight into various technologies adopted and to be adopted in the future by industries and measures the impact of these technologies on manufacturing performance and their sustainability. Businesses and manufacturers face a slew of demands beyond the usual issues of staying agile and surviving in a competitive landscape within a rapidly changing world. Factories of the Future deftly takes the reader through the continuous technology changes and looks ten years down the road at what manufacturing will mostly look like. The book is divided into two parts: Emerging technologies and advancements in existing technologies. Emerging technologies consist of Industry 4.0 and 5.0 themes, machine learning, intelligent machining, advanced maintenance, reliability, and green manufacturing. The advances of existing technologies consist of digital manufacturing, artificial intelligence in machine learning, Internet of Things, product life cycle, and the impact of factories on the future of manufacturing performance of the manufacturing industries. Readers will find in this illuminating book: * A comprehensive discussion of almost all emerging technologies, including "green" manufacturing; * An overview of the social, economic, and technical aspects of these technologies; * An explanation of these technological advancements on manufacturing performance, through case studies and other analytical tools.
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Seitenzahl: 432
Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
1 Factories of the Future
1.0 Introduction
1.1 Factory of the Future
1.2 Current Manufacturing Environment
1.3 Driving Technologies and Market Readiness
1.4 Connected Factory, Smart Factory, and Smart Manufacturing
1.5 Digital and Virtual Factory
1.6 Advanced Manufacturing Technologies
1.7 Role of Factories of the Future (FoF) in Manufacturing Performance
1.8 Socio-Econo-Techno Justification of Factories of the Future
References
2 Industry 5.0
2.1 Introduction
2.2 Individualized Human-Machine-Interaction
2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them
2.4 Concerns in Industry 5.0
2.5 Humans Closer to the Design Process of Manufacturing
2.6 Challenges and Enablers (Socio-Econo-Techno Justification)
2.7 Concluding Remarks
References
3 Machine Learning – A Survey
3.1 Introduction
3.2 Machine Learning
3.3 Reinforcement Machine Learning
3.4 Importance of Dimensionality Reduction in Machine Learning
3.5 Distance Measures
3.6 Clustering
3.7 Hierarchical Model
3.8 Density-Based Clustering
3.9 Role of Machine Learning in Factories of the Future
3.10 Identification of the Probable Customers
3.11 Conclusion
References
4 Understanding Neural Networks
4.1 Introduction
4.2 Components of Neural Networks
4.3 Back-Propagation
4.4 Activation Function (AF)
4.5 Comparison of Activation Functions
4.6 Machine Learning
4.7 Conclusion
References
5 Intelligent Machining
5.1 Introduction
5.2 Requirements for the Developments of Intelligent Machining
5.3 Components of Intelligent Machining
5.4 Conclusion
References
6 Advanced Maintenance and Reliability
6.1 Introduction
6.2 Condition-Based Maintenance
6.3 Computerized Maintenance Management Systems (CMMS)
6.4 Preventive Maintenance (PM)
6.5 Predictive Maintenance (PdM)
6.6 Reliability Centered Maintenance (RCM)
6.7 Condition Monitoring and Residual Life Prediction
6.8 Sustainability
6.9 Concluding Remarks
References
7 Digital Manufacturing
7.1 Introduction
7.2 Product Life Cycle and Transition
7.3 Digital Thread
7.4 Digital Manufacturing Security
7.5 Role of Digital Manufacturing in Future Factories
7.6 Digital Manufacturing and CNC Machining
7.7 Additive Manufacturing
7.8 Role of Digital Manufacturing for Implementation of Green Manufacturing in Future Industries
7.9 Conclusion
References
8 Artificial Intelligence in Machine Learning
8.1 Introduction
8.2 Case Studies
8.3 Advantages of A.I. in ML
8.4 Artificial Intelligence – Basics
8.5 Application of Artificial Intelligence
8.6 Neural Networks (N.N.) Basics
8.7 Convolution Neural Networks
8.8 Image Classification
8.9 Text Classification
8.10 Recurrent Neural Network
8.11 Building Recurrent Neural Network
References
9 Internet of Things
9.1 Introduction
9.2 M2M and Web of Things
9.3 Wireless Networks
9.4 Service Oriented Architecture
9.5 Complexity of Networks
9.6 Wireless Sensor Networks
9.7 Cloud Computing
9.8 Cloud Simulators
9.9 Fog Computing
9.10 Applications of IoT
9.11 Research Gaps and Challenges in IoT
9.12 Concluding Remarks
References
10 Product Life Cycle
10.1 Introduction
10.2 Product Lifecycle Management (PLM)
10.3 High and Low-Level Skimming Strategies/Rapid or Slow Skimming Strategies
10.4 How Do Product Lifecycle Management Work?
10.5 Application Process of Product Lifecycle Management (PLM)
10.6 Role of Unified Modelling Language (UML)
10.7 Management of Product Information Throughout the Entire Product Lifecycle
10.8 PDM System in an Organization
10.9 System Architecture
10.10 Concepts of Model-Based System Engineering (MBSE)
10.11 Challenges of Post-COVID 19 in Manufacturing Sector
10.12 Recent Updates in Product Life Cycle
10.13 Conclusion
References
11 Case Studies
11.1 Case Study in a Two-Wheeler Manufacturing Industry
11.2 Case Study in a Four-Wheeler Manufacturing Unit
11.3 Conclusions
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Current manufacturing environments.
Chapter 4
Table 4.1 Comparison of activation functions [15].
Chapter 9
Table 9.1 The essential characteristics of cloud computing [31] and its relate...
Table 9.2 IoT applications [2].
Chapter 11
Table 11.1 Recycling (3R).
Table 11.2 Quality tools.
List of Illustrations
Chapter 1
Figure 1.1 Industrial revolution [1].
Figure 1.2 Factory of the future: Fully integrated plant [5].
Figure 1.3 Technology gap between the current manufacturing system and Industr...
Figure 1.4 Comparison of today’s factory and the factory of the future [14].
Figure 1.5 Connected industry or smart factory [7].
Chapter 2
Figure 2.1 Framework depicting Industry 5.0.
Chapter 3
Figure 3.1 Unsupervised learning [1].
Figure 3.2 Supervised learning [3].
Figure 3.3 Actor-critic architecture [6].
Figure 3.4 Game of PacMan.
Figure 3.5 Components of dimensionality reduction [8].
Figure 3.6 Principal Component Analysis [8].
Figure 3.7 LDA: maximizing the component axes for class-separation [10].
Figure 3.8 Euclidean distance [13].
Figure 3.9 Manhattan distance [14].
Figure 3.10 Minkowski distance [12].
Figure 3.11 Haversine distance [12].
Figure 3.12 Clustering the data points on the basis of density.
Figure 3.13 Iterative distance-based clustering [20].
Figure 3.14 Parts of dendrogram [24].
Figure 3.15 Types of hierarchical clustering [22].
Figure 3.16 DBSCAN [19].
Figure 3.17 Results obtained using KNN technique (KFold).
Chapter 4
Figure 4.1 Neural network [4].
Figure 4.2 Parts of neurons and their functions [5].
Figure 4.3 Synapses and weights [5].
Figure 4.4 Bias [5].
Figure 4.5 Architecture of neural networks [17].
Figure 4.6 Working of neural networks [22].
Figure 4.7 Artificial neural network [18].
Figure 4.8 Convolutional neural network [7].
Figure 4.9 Backpropagation [8].
Figure 4.10 Graphical representation of sigmoid active function [15].
Figure 4.11 Graphical representation of RELU active function [16].
Figure 4.12 Graphical representation of TANH function [16].
Figure 4.13 Machine learning [14].
Chapter 5
Figure 5.1 How smart sensors make the difference.
Figure 5.2 Structure of PLC.
Figure 5.3 Sub-problems in artificial intelligence.
Figure 5.4 Knowledge graph’s technical architecture.
Chapter 7
Figure 7.1 The vision of the digital factory.
Chapter 8
Figure 8.1 Artificial intelligence system [21].
Figure 8.2 Relationship between AI, ML, and DL [3].
Figure 8.3 Architecture of a neural network [6].
Figure 8.4 Flow of data in a neural network [6].
Figure 8.5 Feature detector image [7].
Figure 8.6 Layout of CNN [8].
Figure 8.7 3-D layout of CNN [8].
Figure 8.8 Layout of text classification [10].
Figure 8.9 Phases of text classification [10].
Figure 8.10 Recurrent neural network vs. feed forward neural network [14].
Figure 8.11 One-to-one [14].
Figure 8.12 One-to-many [14].
Figure 8.13 Many-to-one [14].
Figure 8.14 Many-to-many [14].
Figure 8.15 Recurrent NN [15].
Figure 8.16 Trained model of RNN [15].
Figure 8.17 RNN flow [15].
Figure 8.18 LSTM network [15].
Figure 8.19 Flow chart of LSTM [15].
Figure 8.20 LSTM memory cell [15].
Chapter 9
Figure 9.1 Main enabling technologies of cloud computing [34].
Figure 9.2 Range of applications benefiting from fog computing [47].
Chapter 11
Figure 11.1 Corporate governance structure.
Figure 11.2 Its philosophy.
Figure 11.3 Principle initiatives in product development.
Figure 11.4 Principle initiatives in production.
Figure 11.5 Principle initiatives in recycling.
Figure 11.6 Idle-stop system.
Figure 11.7 Principles of personnel management.
Figure 11.8 Quality circle.
Figure 11.9 Operating revenue for last 5 years.
Figure 11.10 Total income of IT for last 5 years.
Figure 11.11 Profit for last 5 years.
Figure 11.12 Customer satisfaction initiatives.
Figure 11.13 Triple Zero + coexistence with local communities.
Figure 11.14 Materiality matrix.
Figure 11.15 Total income for the last 5 years.
Figure 11.16 Sales for the last 5 years.
Figure 11.17 Profit for the last 5 years.
Figure 11.18 Growth for the last 5 years.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Factories of the Future
Technological Advancements in the Manufacturing Industry
Edited by
Chandan Deep Singh
and
Harleen Kaur
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-86494-3
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
As the title suggests, this book covers the factories of the future and other technological advancements in the manufacturing industry. Basically, the book is divided into two parts: emerging technologies and advancements in existing technologies. The chapters on emerging technologies consist of topics on Industry 5.0, machine learning, intelligent machining, advanced maintenance and reliability; whereas the chapters on advancements in existing technologies cover digital manufacturing, artificial intelligence in machine learning, the internet of things, product life cycle, and the impact of factories of the future on the performance of manufacturing industries. Since these technical advancements are not currently available in a single book, an attempt has been made to cover all these topics in one book.
Presently, a major concern of all manufacturing industries is to remain agile in order to be competitive in the market. To achieve this goal, industries have to adopt new technologies in order to provide customers with better quality products and reduce lead time. In addition to this, a subsequent issue facing manufacturing is helping to save the environment with the introduction of “green” practices, otherwise known as “green manufacturing.”
Nowadays, even research is concentrating on these technical advancements; thus, the intended audience of this book will find material regarding these advancements collected in a single volume. Furthermore, they will develop an understanding of the social, economic and technical justifications for these advancements. In addition to this, the role different technologies play with each other is investigated.
This book will be useful to researchers, academics and faculty in industrial production, mechanical engineering, electronics and other allied branches of manufacturing, and even those in business analytics programs, as case studies and analytical tools related to manufacturing performance are provided. It will also be helpful to those in industrial R&D departments, as industries are always adopting new technologies as advancements are continually being made in this sector. So, on the whole, it will be helpful to both academicians and industrialists.
Technological advancements in manufacturing is a pressing topic, as industries have to sustain their performance when adopting advanced production techniques, and while doing so, they have to keep green issues in mind. Therefore, this is a vastly important book as it involves improving the performance of businesses while creating technical advancements, thus making them more competitive. Since this book contains detailed information on the technological advancements made in manufacturing production and maintenance, it will provide insights into the various technologies currently adopted by industries and those yet to be adopted, that will impact the future performance of industrial manufacturing.
Dr. Chandan Deep Singh and Dr. Harleen Kaur
December 2022
1Factories of the Future
Talwinder Singh* and Davinder Singh
Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India
Abstract
Rapid progress of smart technologies result in drastic changes in industrial production processes thereby building a roadmap for “The Factories of the Future” with a clear vision of how producers should improve productivity through advancement in plant structure, plant digitization, and plant processes in order to establish production systems that are more flexible and adaptable to external changes with high sustainability. The full integration of different support systems in the digital factory will strengthen communication across all R&D, production, marketing, and other organizational activities and thus facilitate customers to view the production of their products in real time and can suggests last minute modifications. This chapter presents the scenario of current manufacturing facilities which are still lacking in predictable maintenance, decision performance, early awareness, self-optimization and self-organizing features of the Industry 4.0. The Factories of the Future (Industry 4.0) will produce products in a smarter and more integrated, flexible and efficient way. Integrated sensors and IT systems can share and analyze data to predict failures, redesign, and trigger automatic repair processes and thus resulting new levels of performance to produce quality goods at reduced cost. The key technologies of the future industry such as virtual reality, simulation, augmented reality, cyber physical systems (CPS), artificial intelligence (AI), Internet of Things (IoT) and Industrial Internet of Things (IIoT), cloud computing, big data, and additive manufacturing have been highlighted in the chapter. In addition, the chapter also discussed state-of-the-art production technologies to meet quality improvement, reduce costs, and reduced lead time challenges owing to global competition, rapidly changing customer needs and low domestic productivity. At the end of chapter, socio-econo-techno justification of the Factories of the Future has been presented.
Keywords: Industrial revolution, plant digitization, driving technologies, Internet of Things (IoT), smart factory, advanced manufacturing technologies
1.0 Introduction
Industrial change is the transition to advanced production strategies. These changes include shifting from manual manufacturing to machinery, production of new chemicals, increased use of unconventional production processes, development of machine tools and the growth of the digital industry system. Industries are currently aiming to move from mass production to customized production. The step towards entry into production strategies, which were completely different from the past, is called industrial revolution (Figure 1.1).
First Industrial Revolution
The First Industrial Revolution began in the 18th century and focused on the strength of steam and textile industries. During this time revolutionists from Europe and the United States built tools and machinery for the production of machinery. The original fibers were produced in simple spinning wheels, and a new mechanical version resulted in eight times higher production. Steam power was already known. Its use for industrial purposes was a great achievement for increasing human productivity. Steam engines could be used for weaving looms instead of man power. Developments such as steam locomotives brought about major changes because people and goods could travel long distances in just a few hours [2].
Second Industrial Revolution
The Second Industrial Revolution, which began in the 19th century, focused on the steel industry, the automotive industry, the production line, and the development of electricity. Henry Ford (1863-1947) introduced the concept of integration in the production of cars. In the assembly line, the complex work of assembling the many parts into a finished product was divided into a series of smaller tasks, resulting in higher productivity as each worker was required to assemble one or two parts in its place in the assembly line.
Figure 1.1 Industrial revolution [1].
Third Industrial Revolution
The Third Industrial Revolution, also known as the “Digital Transformation” began in the 20th century and used electronic and information technology (IT) to produce production using programmable logic control (PLC) and computers. This technology is able to automate the entire production process without human intervention using programmed robots.
Fourth Industrial Revolution
Today, the Fourth Industrial Revolution, also known as “Industry 4.0” is based on the development of the Third Industrial Revolution. The Federal Government of Germany introduced Industry 4.0 as an emerging structure where production systems and goods in the form of Cyber Physical Systems (CPS) make extensive use of global information and communication network for automated information exchange for production and business procedures [3]. The four main drivers of Industrial 4.0 are Internet of Things (IoT), Industrial Internet of Things (IIoT), cloud-based production and intelligent production that helps transform the production process into a complete and intelligent digital [1, 4]. When these resources come together, Industry 4.0 has the potential to bring about dramatic improvements in the factory environment. Examples include machines that can predict failure and trigger automatic repair processes or self-programming that respond to unexpected changes in production [2].
1.1 Factory of the Future
The development of new technologies is making drastic changes in industrial production processes, resulting in “the factory of the future.” The industry of the future is a vision of how producers should improve productivity by making progress in three phases: plant structure, plant digitization, and plant processes to establish production systems that are more flexible, adaptable to external changes and high sustainability [5, 6].
1.1.1 Plant Structure
The future factory plant structure has a flexible, multi-dimensional structure, with the setting of modular lines and environmentally sustainable production processes. The multidirectional structure employs driverless transport methods and is individually controlled by production in conjunction with production equipment. Such transport systems are guided by a laser scanner and the technology to detect radio frequency instead of a fixed transmitter thus making the integration structure fully flexible with flexible line modules. The future factory is designed for environmentally sustainable production, which combines energy efficiency with building materials for example enabling all LED lighting in the industry resulting in very low energy consumption.
1.1.2 Plant Digitization
Smart automation or Plant digitization can be done in a variety of ways as listed below for product development:
Using robots that will ensure repetition and reproduction in complex tasks compared to workers. Robots can also collect information on each piece of work produced and automatically adjust their actions to their features. Robots can also support people in completing tasks in hard-to-reach areas.
Using additive manufacturing or 3D printing, a computer-controlled process that creates three-dimensional objects by inserting objects, usually in layers. With additive manufacturing, design changes can be made quickly and efficiently during the production process with minimal or no damage.
With augmented reality, such as smart mirrors, enables employees to see information as the overlay of their viewing field. This information is especially useful, for example, in assembling, maintenance and repairing things.
Implementing simulations using real-time data, 3D production presentations to improve processes and flow of goods. 3D flow simulation simplifies dynamic responses to changes and allows operators to see work flow before adjusting the production line [
5
].
Training methods have been developed that use 3D simulations to help staff learn in a real-world environment.
1.1.3 Plant Processes
Through the use of new digital technologies, manufacturers further develop their product designs and production processes continuously according to customer requirements.
1.1.4 Industry of the Future: A Fully Integrated Industry
Figure 1.2 shows the full integration of value chain with different support systems in the future factory. The value chain on the left side consists of suppliers, a manufacturing component, a press shop, a body shop, a paint store, a final assembly line and a customer, while, the support systems on the right side incorporate digital logistics, production simulation, and various auxiliary programs. Throughout the value chain, production will be facilitated by the full integration of IT systems such as intelligent robots, modular line configurations, data-driven quality control etc. This integration will strengthen communication across all R&D, production, marketing, and other organizational activities. Customers will be able to view the production of their products (cars in this case) in real time and request changes at the last minute.
Figure 1.2 Factory of the future: Fully integrated plant [5].
1.2 Current Manufacturing Environment
Figure 1.3 shows that the current manufacturing facility does not have many components and functions compared to the Factory of the Future. The various current production environments (Table 1.1) such as single station automated cells, Automated assembly system, Flexible manufacturing system (FMS), Computer-integrated manufacturing system (CIMS) and Reconfigurable manufacturing system (RMS) are still lacking in predictable maintenance, decision performance, early awareness, self-optimization and self-organizing features of Industry 4.0 [7, 8].
Figure 1.4 shows the basic differences between today’s factory and the factory of the future. Today’s industry is concerned with the integration of people into the production process, sustainable development and focuses on value-added activities through a soft management approach that reduces complexity and cost by eliminating waste. Provides strategies to engage all employees in continuous review and improve efficiency.
Figure 1.3 Technology gap between the current manufacturing system and Industry 4.0 [8].
Table 1.1 Current manufacturing environments.
Single station automated cells
A fully automatic production machine unattended for more than one cycle of operation. This system is an automated system that is simple and inexpensive to use with low labor costs and high productivity levels compared to the human-controlled cellular system.
Automated assembly system
The automated assembly system employs mechanical and automated devices to perform different assembly functions in an assembly line or cell. Automatic integration systems are usually designed to perform a consistent sequence of steps on a specific product that is produced in very large quantities.
Flexible manufacturing system (FMS)
A Flexible Manufacturing System (FMS) is a production system designed to easily adapt to changes in the type and quantity of a product being produced. Computer equipment and systems can be configured to produce various components and to adapt to changing production standards. Example: a NC machine, a pallet changer and a part buffer.
Computer integrated manufacturing system (CIMS)
CIMS refers to the use of computer-controlled equipment and automated production systems. CIMS integrates operations, marketing, design, development, production management, process management and other business processes to provide a seamless production process that reduces manual labor and creates repetitive tasks. The CIMS method accelerates the production process and uses real-time sensors and closed loop control systems to perform the production process automatically. It is widely used in the automotive, aviation, space and shipbuilding industries [
9
,
10
].
Reconfigurable manufacturing system (RMS)
RMS has the ability to redesign hardware and manage resources at all operational and organizational levels, in order to quickly adjust production capacity and performance in response to sudden market changes or regulatory requirements [
11
].
Figure 1.4 Comparison of today’s factory and the factory of the future [14].
However, the future factory (Industry 4.0) will produce products in a smarter and more integrated, flexible and efficient way. Integrated sensors and IT systems can share and analyze data to predict failures, redesign, and adapt to change. Individual segregation occurs in decision-making processes and enables real-time independent decisions at the machine level as well as flexible decisions regarding production processes based on timely data. Manufacturers can reach new levels of performance. They can, for example, move forward from prevention to predictable correction, which means that corrective actions are performed only when necessary. Better monitoring of products and production processes can also increase relationships with suppliers, produce quality goods and reduce costs [12, 13]. By increasing clarity, improving forecasting, and, finally, enabling automated systems, Industry 4.0 promotes faster, more flexible, and more efficient processes.
1.3 Driving Technologies and Market Readiness
Communication, automation, and optimization are the driving technologies of Industry 4.0 digital transformation. The key technologies of the future industry are discussed below:
Virtual Reality (VR)
: A virtual reality space where people can do things and participate in that environment using VR glasses. VR has been used in the design process to provide a more precise display and immersive creation of 3D models. VR has also been used in productive training programs, problem solving and remediation programs [
7
,
15
,
16
].
Simulation
: Simulation model demonstrates the performance of an existing or proposed system such as running an assembly line, production planning and scheduling. Simulation can also used to prepare the pre-production machine tool settings in a visible area without physical examination and thus resulted in saving time and money during testing of the production system.
Augmented Reality (AR)
: Augmented Reality (AR) technology integrates virtual reality with real world using multimedia, 3D-Modeling, Real-time Tracking, Intelligent Interaction, Sensor and more. AR uses computer-generated visual information, such as text, images, 3D models, music, video, etc., in the real world after imitation [
17
]. This integration of simulated computer simulations in real-world contexts helps to identify a product in an existing environment. The training of new staff and product testing by showing the various conditions in the developed area have been found to be effective and save time.
Cyber Physical Systems (CPS)
: A Cyber Physical System (CPS) or intelligent system is a computer system in which a machine is controlled or monitored by computer-based algorithms. In CPS, sensors, actuators etc. (physically) are closely integrated with computing, storage, communication and control systems (cyber) [
1
,
18
]. CPS sensors are able to detect mechanical failures and automatically configure error correction actions. CPS is also used for the efficient use of each work station with the help of operational cycle time for that station [
19
]. Some of the major CPS features are listed below:
Intelligent Grid: Cyber Physical Systems is used in the production, transmission, distribution and operation of power generation components, thereby providing dual and control communication between the power grid and users [
20
].
Smart Transport Systems: CPS is used in the transport system to improve traffic management performance.
Public Infrastructure Monitoring: Different CPS sensors are used for accurate and continuous monitoring of buildings, dams, and bridges etc.
Aeronautic Applications: Cyber-Physical Systems used for aircraft inspection equipment, communications with Pilot, Structural Health Monitoring, In-flight testing, and aircraft maintenance etc.
Artificial Intelligence (AI)
: Artificial intelligence (AI) is the ability of a computer or computer-controlled robot to perform tasks that are normally performed by humans because it requires human ingenuity and judgment. The artificial intelligence system is able to self-determine, optimize and automatically respond to physical changes such as changing production schedules, suspension or operation of any machine units, automatic machine tools and automatic warning of uncontrolled conditions [
7
,
21
,
22
].
Examples of Artificial Intelligence:
Production robots
Self-driving cars
Smart helpers
Effective health care management
Automatic investment
Visible travel booking agent
Social media monitoring, etc.
Cloud Computing
: Cloud computing means storing and accessing data and programs online instead of your computer’s hard drive. Cloud computing brings a variety of computer services such as servers, storage, websites, network, software, statistics, and online intelligence (“cloud”) to provide faster innovation, flexible resources, and scale economy. In the future industry, different plant machinery and devices are connected to the same cloud to share information with each other in digital production facilities [
1
,
23
].
Big Data
: Big data is a combination of formal and informal data collected by organizations for information and use in machine learning projects, predictable modeling and other advanced mathematical applications.
Big data is usually seen with three V’s [24]:
Large
volume
of data in many areas;
Wide
variety
of data types that are usually stored in large data systems; and
The
velocity
at which the data is collected and processed.
Big data analysis helps in real-time intelligent production decisions by considering customer feedback and their own ideas on the products they use or intend to use, and from that knowledge, manufacturers focus on their product design to attract more and more customers.
Internet of Things (IoT) and Industrial Internet of Things (IIoT)
: IoT is used for common home applications such as starting a coffee machine with your phone, adjusting your air temperature, car tracking apps, and so on. Household items or everyday items connected to the internet and are therefore controllable from a distance.
IIoT refers to the IoT branch which focuses on the manufacturing and agricultural industry, which connects everything that is visible through the internet. This collaboration between each component ensures that production facilities run smoothly and at low cost. With the IIoT system, data and information flow is faster and efficient; staff can work safely and at high speed. IIoT also assists in production planning, predictable correction and error detection, improved human machine interaction, efficient use of resources.
Additive Manufacturing (AM)
: Additive manufacturing is a specific 3D printing process. This process creates layers in layers by inserting materials according to 3D digital design data. Additive manufacturing technologies such as selective laser melting (SLM), fused deposition method (FDM), and selective laser sintering (SLS) result in faster and more economical production [
25
]. AM is also employed in prototyping testing and design of parts/structures for low cost and customer satisfaction.
1.4 Connected Factory, Smart Factory, and Smart Manufacturing
A connected industry or intellectual industry is a manufacturing facility that uses digital technology to allow seamless sharing of information between people, machines, and sensors.
There are two main principles for allowing communication in the industry or factory. The first is to reach the right level of continuous production, self-improvement, and quality. This leads to higher profits. The second goal of the connected factory is to empower staff. The combination of control, visibility, and flexibility offered by the new digital solutions makes it possible for production workers to make further, impactful improvements. Figure 1.5 shows intelligent or integrated production including various digital technologies such as virtual reality, simulation, additive manufacturing, IoT, CPS, AI, cloud computing, etc. A smart or “connected” factory is one where almost every aspect of the factory is visible and available for analysis. Using data and updates, digital processes, and tools enable the entire organization, from management to shop floor staff reach to a new level of efficiency and profitability.
The connected industry uses consistent data distribution to adapt to the changing needs of intelligent production within an organization in a fully integrated and flexible system. Automatic workflow, real-time tracking and scheduling, as well as energy efficiency result in reduced costs and wastage [26].
Figure 1.5 Connected industry or smart factory [7].
1.4.1 Potential Benefits of a Connected Factory
High Productivity – Connected industries can perform jobs at a faster rate and run more efficiently leading to improved productivity and lower labor costs.
Advanced Flexibility – Intelligent industries are designed for different production settings and demand flexibility. This provides complete flexibility of operation.
Better Safety – The automation of tasks such as sorting, picking, packing, transporting, and delivering allows people to focus on safer jobs.
Better Quality – The connected industry can detect quality problems quickly and can identify the cause.
Lower Cost – More cost-effective processes, including asset management, better decisions, and improved service delivery.
Flexible and effective communication as the factory floor and front and back offices share a single powerful data source [
26
,
27
].
1.5 Digital and Virtual Factory
1.5.1 Digital Factory
Digital industry comprises network of digital models that replicate the features of a virtual factory. Digital Factory includes a list of methods and tools such as simulation and 3D visualization, all managed by integrated data management systems. The main goal of the digital industry is complete planning, continuous testing, and the development of a real productive factory [28]. The digital industry focuses on the following:
Improved quality of planning and economic efficiency
Shorter go-to market time
Clear communication
Similar planning standards
Managing competent information
What are the business benefits of a digital industry?
Complete and real-time data generated by digital factories promotes efficiency, productivity, safety and compliance. It also improves the flow control of production work and the mobility of everything from immature items to continuous work and to finished goods. It also provides real-time access to operational data, so that managers can quickly overcome roadblocks and inefficiencies [29].
1.5.2 Virtual Factory
Virtual factory is based on integrated model that incorporates various software, tools, and solutions to any real-time production system problem. This model sees the real industry as a combination of various subsystems and integrates them. In practice, it creates a visual simulation work that helps to replicate the real life situation and that helps in design and implementation.
Virtual factory benefits include [30]:
It assists in building skills to support rapid development in the manufacturing sector by bringing together professionals.
It helps to provide solutions in a fast and inexpensive way.
Eliminates the need for testing or pilot studies and replaces it with virtual simulation through software.
It helps in optimum decision making.
1.6 Advanced Manufacturing Technologies
Manufacturing organizations use state-of-the-art production technology to meet quality improvement, reduce costs, and reduced lead time challenges posed by global competition, rapidly changing customer needs and low domestic productivity [31]. Advanced production technologies include:
Computer Technology (e.g., CAD, CAE, CAM)
– CAD, computer-assisted design, computer use to design 2D and 3D models. CAD provides a preview of the final product with digital visibility of the final product and its components and thus improves the quality of the design with greater accuracy and minor errors. Computer-assisted engineering (CAE) is the use of computer software to simulate performance in order to improve product designs or to help solve engineering problems in many industries. This includes the imitation, validation and efficiency of products, processes, and production tools. CAM, a computer-assisted production, provides an intelligent way to generate code using software such as GUIs (Graphical User Interfaces) to control the equipment involved in the production process.
High-Performance Computing (HPC)
– HPC uses information communication technology in all production and transportation systems. This program focuses on the creation and implementation of programs to improve production facilities with effective monitoring that is in line with the constantly updated and remedied plans.
Rapid prototyping (additive manufacturing)
– 3D Printing or Rapid prototyping is the rapid construction of a visual component, model or combination using computer-assisted design (CAD). Rapid Prototyping helps designers present new ideas to board members, clients or investors to understand and approve a development or product. These displays can also allow designers to get the right feedback from customers based on the actual body product rather than the concept.
High precision technology
– such as NC (Numerical Controller), CNC Machine (Computer Numerical Controller) increases production capacity and reduces set switching time.
Network and IT integration
– With internet access to all aspects of production, there is an instant notification of any potential problems, allowing blockchain adjustment and saving time and money.
Advanced robots and other intelligent production systems
– Designed to automate processes that include precise movement, lifting heavy objects, and consistent integration of features into production systems. Additionally, robots minimize the risks involved in hazardous activities such as the automotive industry and aerospace.
Automated technology
– Integrates all the processes and equipment that make plants and systems work automatically. These include Interactive Robots, Artificial Intelligence (AI), Internet of Things (IoT), etc. automatically performs work procedures correctly and with a negligible error rate.
Monitoring systems and Control systems
– Monitoring systems such as sensor used to view and record industrial process data. However, control systems continuously maintain or change the state of the system through actuators. These control systems help maintain quality, yield and energy efficiency and ensure that operations are carried out safely and profitably.
New industrial field technologies (e.g., composite materi
-
als)
– Advanced materials enable more precise integration of specific applications such as metal, plastic, glass, and ceramics. Materials that require precision in its chemical and physical properties are used to empower business success.
1.6.1 Advantages of Advanced Manufacturing Technologies
The benefits of using state-of-the-art production technology are given below:
Improved Quality Standards
The potential benefit of advanced production technology is quality improvement. Switching to robots and automation in the production process leads to almost zero human error. The number of accidents, errors, and resulting cost inefficiencies are also reduced.
Improved Production
Advanced production technology improves productivity in many ways. It helps producers to increase or decrease depending on market demand. From creating custom products in small batches to large-scale production, productivity is improved and can be customized as well.
Encourages Innovation
Being able to measure productivity gives manufacturers the ability to create new products in a less expensive way. A small, personalized product can be created without compromising on normal production times.
Reduced Production Time
Digital production uses virtualization to create digital industries that mimic the production process. Such simulation helps engineers design a seamless factory structure, production sequence, and model output. Any potential obstacles can be resolved before production can begin.
1.7 Role of Factories of the Future (FoF) in Manufacturing Performance
Production process improvement is one of the most effective ways to increase quality, efficiency, and foundation. Developing processes that contribute to the final product is an effective way to impart scalable and sustainable changes. Proper upgrades can reduce errors, reduce production time, and increase customer satisfaction. The role of Factories of the Future in improving production performance is highlighted below:
FOF promotes the productivity and efficiency of the organization, better flexibility and profits. FoF also improves customer self-awareness.
FOF technology allows for faster and faster production while cost-effective and efficient distribution of resources.
Intelligent technology improves automation, machine-to-machine communication, and decision-making.
FoF provides fast batch switching, automatic tracking, tracing and reporting for better productivity.
FOF plays an important role in information sharing and collaboration that allows production lines, business processes, and departments to communicate regardless of location, time zone, field, or anything else.
FOF uses automatic tracking and tracking capabilities to resolve problems quickly leading to improved customer service and information.
1.8 Socio-Econo-Techno Justification of Factories of the Future
Industry 4.0 or Factories of the Future (FoF) is changing the industry in the way we know it since the industrial revolution. Imagine you have personalized products and services, fully customized for you. With FoF, this becomes a reality because it drives new inventions and introduces new technologies to such an extent that it will affect our entire society [32]. The following points illustrate the ways in which FoF will impact on society-economy-technology:
New production processes will make it possible to make products the way you want at the new level. You will be able to customize the products according to your needs at a detailed level, covering everything from cars to personal medicine.
Industry 4.0, especially when combined with machine learning and practical skills, will drastically change the working conditions of workers. Many jobs will disappear while we get more new jobs, and most duplicate jobs will go from manual to automation.
The high growth in demand for information and communication technology (ITC) by industrial organizations can build the future of Industrial 4.0 and may have positive effects on the various sectors of the firm [
33
].
New solutions can reduce energy consumption and thus help organizations to strengthen their business with existing and new business models.
FoF makes the world more digital, more connected, more flexible, and more responsive. Well-known public relations are changing beyond recognition; from business relationships to consumer to peer-to-peer approaches [
34
].
Industry 4.0 introduces new opportunities for health care, the ability to empower more people around the world to become entrepreneurs, and increased access to education [
35
].
In the fourth industrial revolution, the social impact of technological changes in the economic sphere, labor market, and innovation is better understood now than during the previous industrial revolution. Meanwhile, governments and policymakers need to adapt and respond quickly to the immediate emergence of the Industrial 4.0 landscape by providing an environment and policies that can guide the future of sustainable economic and social development and implement the technologies for Industry 4.0 on behalf of individuals and communities [33, 36].
References
1. Vaidya, S., Ambad, P., Bhosle, S., Industry 4.0 – A glimpse.
Proc. Manuf.
, 20, 233–238, 2018.
2. See
https://www.desouttertools.com/industry-4-0/news/503/industrial-revolution-from-industry-1-0-to-industry-4-0
.
3. Kamarul Bahrin, M.A., Othman, M.F., Nor Azli, N.H., Talib, M.F., Industry 4.0: A review on industrial automation and robotic.
J. Teknol.
, 78, 6-13, 137– 143, 2016.
4. Erol, S., Jäger, A., Hold, P., Ott, K., Sihn, W., Tangible Industry 4.0: A scenario-based approach to learning for the future of production.
Proc. CIRP
, 54, 13–18, 2016.
5. See
https://www.bcg.com/publications/2016/leaning-manufacturing-operations-factory-of-future
.
6. de Asis Marti Nieto, F., Goepp, V., Caillaud, E., From factory of the future to future of the factory: Integration approaches.
IFAC-PapersOnLine
, 50, 1, 11695–11700, 2017.
7. Phuyal, S., Bista, D., Bista, R., Challenges, opportunities and future directions of smart manufacturing: A state of art review.
Sustainable Futures
, 2, 100023, 2020.
8. Qin, J., Liu, Y., Grosvenor, R., A categorical framework of manufacturing for Industry 4.0 and beyond.
Proc. CIRP
, 52, 173–178, 2016.
9. See
https://www.techopedia.com/definition/30965/computer-integrated-manufacturing-cim
.
10. Sheng, Y.K. and Lan, W.L., Research on computer integrated manufacturing system based on integrated logistics.
Adv. Mat. Res.
, 271–273, 738–741, 2011.
11. Bi, Z.M., Lang, S.Y.T., Shen, W., Wang, L., Reconfigurable manufacturing systems: The state of the art.
Int. J. Prod. Res.
, 46, 4, 967–992, 2008.
12. Nagy, J., Industry 4.0: Definition, elements and effect on corporate value chain, Workshop studies - Institute of Business Economics, Corvinus University of Budapest, Hungary, 2017.
http://unipub.lib.uni-corvinus.hu/3115/
.
13. Skapinyecz, R., Illés, B., Bányai, Á., Logistic aspects of Industry 4.0.
IOP Conf. Ser.: Mater. Sci. Eng.
, 448, 012014, 2018.
14. Lee, J., Bagheri, B., Kao, H.A., Recent advances and trends of cyber-physical systems and big data analytics in industrial informatics.
Proc. Int. Conf. on Industrial Informatics (Porto Alegre)
, IEEE, 2014.
15. Salah, B., Abidi, M.H., Mian, S.H., Krid, M., Alkhalefah, H., Abdo, A., Virtual reality based engineering education to enhance manufacturing sustainability in Industry 4.0.
Sustainability
, 11, 5, 1477, 2019.
16. Berg, L.P. and Vance, J.M., Industry use of virtual reality in product design and manufacturing: A survey.
Virtual Real.
, 21, 1, 1–17, 2017.
17. Chen, Y., Wang, Q., Chen, H., Song, X., Tang, H., Tian, M., An overview of augmented reality technology.
J. Phys.: Conf. Ser.
, 1237, 022082, 2019.
18. Bagheri, B., Yang, S., Kao, H.A., Lee, J., Cyber-physical systems architecture for self-aware machines in Industry 4.0 environment.
IFAC Conf.
, 38, 30, 1622–1627, 2015.
19. Kolberg, D. and Zühlke, D., Lean automation enabled by Industry 4.0 technologies.
IFAC Conf.
, 38, 3, 1870–1875, 2015.
20. Bhrugubanda, M., A review on applications of cyber physical systems.
Int. J. Innov. Sci. Eng. Technol.
, 2, 6, 728–730, 2015.
21. Lee, J., Davari, H., Singh, J., Pandhare, V., Industrial artificial intelligence for Industry 4.0-based manufacturing systems.
Manuf. Lett.
, 18, 20–23, 2018.
22. Genge, B., Nai Fovino, I., Siaterlis, C., Masera, M.,
Analyzing cyber-physical attacks on networked industrial control systems
, pp. 167–183, Springer Berlin Heidelberg, Berlin, 2011.
23. Marilungo, E., Papetti, A., Germani, M., Peruzzini, M., From PSS to CPS design: A real industrial use case toward Industry 4.0, The 9th CIRP IPSS Conference: Circular perspectives on product/service-systems.
Proc. CIRP
, 64, 357–362, 2017.
24. See
https://searchdatamanagement.techtarget.com/definition/big-data
.
25. Landherr, M., Schneider, U., Bauernhansl, T., The Application Centre Industrie 4.0 - Industry-driven manufacturing, research and development, 49th CIRP Conference on Manufacturing Systems (CIRP-CMS).
Proc. CIRP
, 57, 26–31, 2016.
26. See
https://ottomotors.com/blog/connected-factory-manufacturing
.
27. Depince, P., Chablat, D., Noel, E., Woelk, P.O., The virtual manufacturing concept: Scope, socio-economic aspects and future trends.
Proceedings of DETC’2004, ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conferences
, 2004.
28. See
https://www.tibco.com/reference-center/what-is-a-digital-factory
.
29. See
https://www.cognizant.com/us/en/glossary/virtual-reality
.
30. See
https://www.mbaskool.com/business-concepts/operations-logistics-supply-chain-terms/13306-virtual-factory.html#google_vignette
.
31. Saraph, J.V. and Sebastian, R.J., Human resource strategies for effective introduction of Advanced Manufacturing Technologies (AMT).
Prod. Inventory Manage. J.
, 33, 1, 64, 1992.
32. See
https://it-voices.com/en/articles-en/5-life-changing-ways-industry-4-0-will-change-society
.
33. Morrar, R., Arman, H., Mousa, S., The Fourth Industrial Revolution (Industry 4.0): A social innovation perspective.
Technol. Innov. Manage. Rev.
, 7, 11, 12–20, 2017.
34. Arroyo, L., Murillo, D., Val, E.,
Trustful and trustworthy: Manufacturing trust in the digital era
, ESADE Roman Llull University Institute for Social Innovation; EY Fundacion Espana, Barcelona, 2017.
35. Schreiber, U., EY women. Fast forward. An interview with Uschi Schreiber.
Leaders
, 39, 1, 40, 2017.
36. Schwab, K.,
The Fourth Industrial Revolution
, World Economic Forum, Geneva, 2015.
Note
*
Corresponding author
:
2Industry 5.0
Talwinder Singh1, Davinder Singh1, Chandan Deep Singh1 and Kanwaljit Singh2*
1Department of Mechanical Engineering, Punjabi University, Patiala, Punjab, India
2Department of Mechanical Engineering, Guru Kashi University, Talwandi Sabo, Punjab, India
