Industry 4.0 refers to automation and data exchange in manufacturing technologies. From innovative research, challenges, solutions and strategies to real-world case studies, the aim of this edited book is to focus on the nine pillars of technology that are supporting the transition to Industry 4.0 and smart manufacturing. The nine pillars include the internet of things, cloud computing, autonomous and robotics systems, big data analytics, augmented reality, cyber security, simulation, system integration, and additive manufacturing. A key role is played by the industrial IoTs and state-of-the-art technologies such as fog and edge computing, advanced data analytics, innovative data exchange models, artificial intelligence, machine learning, mobile and network technologies, robotics and sensors. This book is a useful resource for an audience of academic and industry researchers and engineers, as well as advanced students in the fields of information and communication technologies, robotics and automation, big data analytics and data mining, machine learning, artificial intelligence, AR/VR/ER, cybersecurity, cyber physical systems, sensing and robotics with a focus on Industry 4.0, and smart manufacturing.
Inspec keywords: rapid prototyping (industrial); computer simulation; data analysis; cloud computing; production engineering computing; three-dimensional printing; virtual reality; industrial robots; Big Data; security of data; Internet of Things
Other keywords: IIoT; deep learning; building air conditioning mechanical ventilation; Project Dragonfly; industrial robots; Industry 4.0; simulation; Internet of Things; public sector performance; IoT-based data acquisition; artificial intelligence; cloud computing; big data; cyber security; global standardisation; expert fault diagnosis system; augmented reality; lean government; lean green integration; building intelligent energy management system; 5G network; smart manufacturing; additive manufacturing; smart factories; sustainable development; solar photovoltaic panel; smart industries; system integration; face recognition; service industry; virtual reality; data acquisition monitoring system; parallel database server; trust; round-robin algorithm; SME; solar panel theft; cybersecurity; GPS tracking; big data analytics; load adjusted-load informed algorithm
Subjects: Information networks; Control applications in manufacturing processes; General topics in manufacturing and production engineering; Other manufacturing processes; General and management topics; Robotics; Internet software; Information resources and networks; Control in industrial production systems; Robotics; Production engineering computing; Mobile, ubiquitous and pervasive computing; Industrial applications of IT; Data handling techniques; Manufacturing systems; Manufacturing and industrial administration
Industry 4.0 is a strategic initiative introduced by the German government during early 2010s to transform industrial manufacturing through digitalisation and exploitation of the potentials of new technologies. It is an effort to increase productivity and efficiency mainly in the manufacturing sector. Industry 4.0 production system aims to be highly flexible and should be able to produce individualised and customised products. In fact, it is an exciting employment of automation within manufacturing, covering the use of robotics, data management, cloud computing and the intemet of things (IoT). It has started to show that artificial intelligence, robotics, smart sensors and integrated systems are an important part of a normal manufacturing process. In interaction with machines, it needs horizontal integration at every step in the production process. The Americans have the same concept for Industry 4.0 but prefer to call it Smart Factory. The nine pillars of Industry 4.0 transforms isolated cell production into a fully optimised, integrated and automated production flow.
The Fourth Industrial Revolution or Industry 4.0 encompasses production/manu-facturing-based industries, marrying advanced manufacturing techniques with digital transformation, driven by connected technologies to create intelligent manufacturing systems that not only are interconnected but also have the ability to communicate, analyse, forecast and use this information to drive further intelligent actions. New business models and technologies such as the internet of things (IoT), big data, artificial intelligence (Al) and additive manufacturing are driving the change of current business models and shifting the global economics and market structures. Industry 4.0 involves global economic transformation. Hence, national standardisation activities need to be harmonised with the international level to focus on stipulating the international collaboration and cooperation mechanisms and exchange of information.
Industry 4.0 encompasses various technologies but centres on highly auto-mated, digitalised manufacturing processes and advanced information communication technology. The digitalisation of manufacturing processes enabled different devices and sub-processes within manufacturing facilities to be interconnected into a large digital platform. The modern SM challenge for complex decision-making corresponds to four main aspects: (1) The ability to realistically model the actual manufacturing systems for information and data gathering; (2) the ability to integrate consistent, reliable and valid manufacturing plant data; (3) the ability to process the gathered data to obtain required information within reasonable computational efforts; and (4) the ability to incorporate feedback systems into the manufacturing process for continuous improvement and to facilitate continuous decision-making procedures over time.
The human and machine work interference in industry should be flexible and adaptive. Because of this, several industries started to adopt using AR and VR to train their workers. With this training, they can speed up the work or reconfigure the work, support operators, execute augmented virtuality (AV) training for compiling or constructing parts, administer depository or stockroom effectively, support diagnostics in the assembly, and minimize the risk in the work setting. The key technologies on AV used in the industries are display interaction, tracking positioning and registration, human-computer interaction, object detection and recognition, calibration, model rendering, analysis on 3D space, and collision detection.
In the present day, the development industry is facing sophisticated demand amidst increasing competition. Fast-paced technological advancement has led to the emergence of the Industry Revolution 4.0. Integrating information technology and business operations presents several challenges, of which cyber security is of primary importance. Cyber security refers to aspects of technology that address the confidentiality, availability, and integrity of data in cyberspace and is considered to be associated with other security aspects. Several industries regard cyber security as crucial since this aspect helps protect confidential information specific to people or systems against abuse, attacks, theft, and misuse in the digital space. Network connectivity is growing steadily; therefore, there is a chance of data being more prone to cyber-attacks, where the data may be abused for financial or strategic gain. A majority of the organisations consider cyber security as a part of the tech-nology domain. Even though organisations know the potential risks and how those risks might affect the business, the typical propensity is not to pinpoint security vulnerabilities. Substantial capital is required to formulate new strategies to facilitate security-specific technological advancement in information technology (IT) to contain the risk and impact of cyber-attacks. Cyber security is primarily considered by most organizations as a technology issue.
Economic growth is the backbone of any country, which is mainly linked with industrial power, production and efficiency. Industries are changing from old fashioned to new technological perspectives with new era requirements. Industries equipped fully with technology are known as smart industries. The Industrial Internet of Things (lloT) is the source that makes regular industries into smart industries by providing them cost cutting, remote access, production management, supply chain and monitoring, as well as reducing energy consumption cost, etc.
Simulation itself is not a new notion; it has been there since the invention of computers (before the 4th Industrial Revolution). However, with the arrival of the 4th Industrial Revolution, where things change at breakneck speed, the importance of simulation is even now more amplified. In this chapter, we will give examples of the various types of simulations available as well as some examples, and describe the benefits of simulation, especially in the context of the 4th Industrial Revolution. The following examples are covered: controller design, mechanical systems, manufacturing systems, transport systems, physically responding simulations, and virtual reality simulation.
The establishment of Industry 4.0 brings along a new concept called Smart Cities, which refers to cities that utilize large amounts of data and communication technologies available to improve the performance and quality of life and urban services to ultimately reduce resource consumption, wastage and overall costs. According to the summary of the Belt and Road initiative session on urban industrial solutions under the United Nations (UN) Industrial Development Organization, the smart city concept is an urban development paradigm integrating new innovative ideas, concepts and emerging technologies such as IoT, the Internet of Services (IoS), the Internet of People (IoP) and the Internet of Energy (IoE), which ultimately form the concept of the Internet of Everything (IoE), also known as Industry 4.0 or the Fourth Industrial Revolution. This concept aims to provide effective and high-quality public services and infrastructure in real time and, thereby, a better quality of life for citizens and a move towards sustainable cities.
For many years, industrial robots have been utilised to change mass manufacturing and register automation to carry out unique procedures more quickly and safely without any human lapse. The mass manufacturing produced from industrial robots lowers the cost of the product and increases the speed of delivery without much error. Production procedures, technology development, smart supply chain and trade aspects have been changed by Industry 4.0. Artificial intelligence (Al) and machine learning transformed the older ways of executing processes and operating industrial robots. To this end, novel research has been carried out and plans have been made for future robot generation, automation lines and smart factories. Although Industry 4.0 is not a widely used term and known idea, it is outstandingly capable of enhancing human life. All manufacturing procedures and supply levels are prognosticated to be impacted in the production. A relation of Industry 4.0 and smart factories in terms of industrial robots is described in this chapter and the benefits and applications are addressed. The influence of industrial robots on smart factories and the future expectations are later discussed in the text.
Artificial intelligence, machine learning, robotics and blockchain are all products of the Fourth Industrial Revolution. Although it is difficult to say which has a wider impact on the world of work and present status of the society, each is contributing in its own significant manner to changing the way we view and interact with technology. Blockchain, in this sense, can be characterised as a trust-building mechanism, which has eased the way we conduct transactions bringing in more reliability, transparency and trust to the system. The technology, which once formed the basis of bitcoin and digital currencies, has gone beyond its remit to create new fields. More important, more than developed economies, blockchain is to benefit developing countries by helping to reduce the instances of corruption and leakages with its efficient check and balances system. The chapter would focus on the impact of blockchain on different areas to build what we call trust economies and its limitations. We also focus on the nature of technology, and how marginalised sections may be left out if the governments do not take initiative to educate them about new technologies and systems.
System integration is a process commonly implemented in the fields of engineering and information technology. It involves the combination of various computing systems and software packages in order to create a larger system, and this is what drives Industry 4.0 to work at its optimum. System integration increases the value of a system by creating new functionalities through the combination of subsystems and software applications. The world is currently experiencing a fourth iteration of the Industrial Revolution, Industry 4.0, which merges computers and automation to enhance efficiency in the manufacturing industry and also includes cyber-physical systems, the Internet of Things, and cloud computing. Industry 4.0 takes into account all kinds of technologies and machines, from smartphones and tablets to cars, whitegoods, web-enabled televisions, and more. Also, software development is not left out of his process for the effective and efficient development of software products. Software development and applications are increasingly spreading in all areas of human endeavors. It therefore means that, to meet the needs of the world population, Industry 4.0 principles must be applied.
AM opens new opportunities for design and manufacturing cross-wise over various enterprises. Contrasted with traditional techniques, increasingly complex structures and geometries can be accomplished using customized design, greater efficiencies, higher performance, and better environmental sustainability. AM plays an important role in industries. AM technologies will soon be leading to the next major industrial revolution. AM plays a key role in Industry 4.0, saving time and costs, being decisive for process efficiency and reducing its complexity, allowing for rapid prototyping and highly decentralized production processes. Therefore, the innovation is seeing expanded reception past prototyping and tooling into the end and extra part generation. Therefore, AM has a significant task to carry out in the scope of assembling techniques. Companies can deploy to evolve their products in response to market demands. Importantly, as the innovation keeps on improving, AM changes from a problematic innovation utilized distinctly by trailblazers to a typical strategy for center creation.
Industries also evolved much since 1700. There have been four industrial revolutions since the 1700s. The first industrial revolution in 1780 was about steam engines, textile industries, and mechanical engineering. The second in 1840 was about steel industries. The third in 1900 was about electricity and automobiles, whereas, the fourth industrial revolution was about the IT industry, and it is generally accepted that the fourth industrial revolution has just begun [1]. As such, the term “Industry 4.0” was pinned by the German government in 2011 [2]. Industry 4.0 or Fourth Industrial Revolution is all about the Internet of Things and services (IoTS), cyber-physical systems (CPSs), and interaction and exchange of data through the Internet or cloud computing. During 1960, one system can perform only one task at a time. Multiple systems needed to run multiple tasks simultaneously [3]. Now moving forward to the present 2020, the single system can perform multiple tasks within a few seconds. Such technology is achieved by scientific advancements like the Internet, web services, Internet of things, and cloud computing. Like the industrial revolutions, there have been several improvements and developments in computing, processing, and accessing the stored data. The evolution of computing is provided in Figure 13.1.
A new revolution called Industry 4.0 (I4.0) is emerging and trending, in which industrial systems comprised of numerous sensors, actuators, and intelligent elements are interfaced and integrated into the smart factories with Internet communication technologies. I4.0 is currently driven by disruptive innovations that promise to provide opportunities for new value creations in all major market sectors. Cybersecurity is a common requirement in any Internet technology, thus it remains a major challenge to adopters of I4.0. This chapter provides a brief overview of a number of key components, principles, and paradigms of I4.0 technologies pertaining to cybersecurity. In addition, this chapter introduces industry-relevant cybersecurity vulnerabilities, risks, threats, and countermeasures with high-profile attack examples (e.g. BlackEnergy, Stuxnet) to help readers to appreciate and understand the state of the art. Finally, the chapter attempts to highlight the open issues and future directions of the system components in the context of cybersecurity for I4.0.
This research explains about the IoT-based data acquisition monitoring system for solar photovoltaic panel for a solar system. The IoT-based data acquisition monitoring system for solar photovoltaic panel consists of four units of thermocouple (TC) sensors integrated with MAX31855 amplifier, one unit of INA 219 DC current/voltage sensor and Raspberry Pi Zero Wireless device as device manager. The proposed IoT-based data acquisition monitoring system is developed to sense, measure and calculate the current, voltage, power and temperature. This information is stored into the SD card of the Raspberry Pi Zero Wireless which is known as the device manager. The stored current, voltage, power and temperature information in SD card Raspberry Pi Zero Wireless is then transferred to the cloud storage wirelessly. This information is then extracted into self-developed website. The information can be easily viewed at the website and helps the consumers to monitor the installed solar system, especially the performances of solar photo-voltaic panel. Monitoring the solar photovoltaic panel in real time using the IoT-based data acquisition monitoring system can effectively facilitate a system-level maintenance and immediate fault-detection can be performed.
IoT-driven data centre information system as the hub is crucial for addressing the issues related to building energy management. IoT technology-based monitoring mechanisms for the indoor environment of buildings, as well as energy consumption, bring together all energy consumption equipment for making the construction energy-efficient. The mechanism is apt for various prevailing and new constructions. It is the most appropriate system for transferring data in the indoor environment of buildings and monitoring mechanisms for the energy consumption.As a huge amount of data is produced from IoT devices, intelligent hardware and processing devices are needed to interpret and use the data. This requirement is even greater when we are dealing with real-time computations.
The rapid energy consumption around the world has resulted in the shortage of fuel supply and overconsumption of the energy sources, thus worsening the environmental impacts. The overall energy consumption of residential and commercial buildings in the developed countries has reached between 20% and 40%. This figure has exceeded those in the industrial and transportation sectors. Growth in population, increasing demanding for building services and comfort levels and increasing time spent in the buildings indicate that energy demand will continue to increase in the future. For this reason, energy efficiency in buildings is the main goal for energy policies globally.
In recent years, programs intended to develop effective lean manufacturing systems have been implemented in many of the world's leading companies. Many of them have been highly successful in increasing efficiency, reducing costs, improving customer response time, and contributing to improved quality, greater profitability, and enhanced public image. Some companies have committed to reducing negative impacts of their operations on the environment. The “green” systems have created huge reductions in energy consumption, waste generation, and hazardous materials used. In addition, companies' images as socially responsible organizations are also highlighted. Several research efforts indicate that lean companies show significant environmental improvements by being more resourceful and energy efficient. Some studies also show how lean and green systems share many of the same best practices to reduce their respective wastes. Yet, the consensus view is that these two systems tend to operate independently, administered by distinctly different personnel, even within the same manufacturing plant.
In today's competitive world, the success of social development depends on a competent, well-functioning government and public sector. A good governance has always been important in organizations, where people are one of the few influential assets. The quality of governance institutions also has a significant impact on economic growth. Unfortunately, most of the developing countries are finding centrally regulated public service policy a hindrance to effectively deliver public services in modern globally competitive scenarios. Making the government function better implies not only improving efficiency and cost-effectiveness of public sector functions and operations but also improving all of the public sector effectiveness so that government policies and programs work smoothly, achieve the stated desired objectives, treat recipients with respect and dignity, and positively affect people in which they are designated to minimize any negative distortionary side effects.
Although Lean manufacturing techniques are not yet in place in every shop floor production, the so-called Smart Factory with the very promising German-coined label “Industry 4.0” is already making its tour. While the Toyota Production System (TPS) has shown to be the most performant manufacturing system, the Industry 4.0 initiative is still in the scoping phase with the demanding goal to become a highly integrated cyber production system. The partial and often limited knowledge about Lean production leads to distorted ideas that the two approaches are incompatible. In order to eradicate wrong statements, this paper tries to explain what Lean really is and how it has to be considered in the context of the Industry 4.0 initiative. Further, it discusses the existing contradiction within the Industry 4.0 goals regarding manufacturing performance and break-even point.
Security system is important to protect the objects, including solar panel modules. In this study, an integrated system that combines image processing and object tracking is proposed as a security system of solar panel. Face recognition using deep learning is used to detect unknown face. Then, the stolen object can be tracked using Global Positioning System (GPS) that works using General Packet Radio Service and Global System for Mobile communication system. The results show that the integrated security system is able to find the suspect and track the stolen object. Using the combination of FaceNet and deep belief network, unknown face can be recognized with an accuracy of 94.4% and 87.5% for offline and online testing, respectively. Meanwhile, the GPS tracking system is able to track the coordinate data of the stolen object with an error of 2.5 m and the average sending time is 4.64 s. The duration of sending and receiving data is affected by the signal strength. The proposed method works well in real-time manner and they can be monitored through a website for both recorded unknown face and coordinate data location.
Project Dragonfly is a two-in-one industrial wastewater and air toxicity monitoring solution that is environmentally friendly, noninvasive, and cost-effective. The project presents remarkable significance when regulation of industrial emissions becomes crucial.The project utilizes Microsoft Azure platform along with Microsoft's proprietary cloud products and services, and Android mobile application for its software components and database: Lolin D32 Pro, Neffos Y5i, multiple sensors and a quadrotor helicopter (quadcopter) are among its main hardware components. The sensors are packed into two functional units: air monitoring unit (AMU) and water monitoring unit (WMU).
Industry 4.0 has recently become an important topic in the software development context. This standard-based strategy integrates physical systems, the Internet of Things, and the Internet of Services with the aim of extending the capacity of software development process. Although many software development experts have presented the advantages of different software development models and approach, software development refers to an architecture that allows the correct implementation of Industry 4.0 applications using the load-balancing approach model (LBAM 4.0). This study exposes the essential characteristics that allow software to be retrofitted to become Industry 4.0 applications. Specifically, an intelligent software system based on a load-balancing approach was developed and implemented using equal and unequal clustering processing capabilities. To evaluate the performance of LBAM 4.0, implementation was carried out on a cluster with equal and unequal nodes using round-robin algorithm. It was discovered that the performance of the algorithm is quite good when the nodes are of equal capacities, but very poor when the capacities of the nodes are not equal. Load adjusted-load informed algorithm was used to improve the worst case of the round-robin algorithm to prevent the worst situations of using nodes of different capacities of which the results showed remarkable improvement.
5G network becomes a reality and it is expected to bring a wealth of new opportunities in various vertical fields. Operators demand for 5G network technologies. The reasons are that 4G network services become saturated and revenue is flattening or declining every year. 5G is expected to enable further economic growth and pervasive digitalization of a hyperconnected society with all people and devices/things are connected to the network virtually. 5G will provide enterprises and consumers with plenty of new use cases, including Virtual Reality (VR) application, telemedicine by using ultra-reliable communications and large-scale Internet of Things (IoT) such as smart cities. Through 5G technology revolution, it is able to provide low latency, large bandwidth, high reliability and capacity and it also brings better user experience. The operator needs to develop new network infrastructure (5G New Radio) or restructure the current 4G network infrastructure to adopt the 5G system. Hence, this chapter reveals the foundation of 5G network in terms of key capabilities, use cases and network structure in detail. In addition, the 5G network deployment is also considered in this chapter which reveals the deployment option for non-standalone (NSA) and standalone (SA) network architectures, spectrum specification and 5G key technologies such as massive multiple input multiple output (MIMO) and uplink and downlink (UL/DL) decoupling mechanisms. The key challenges of 5G network which are faced by worldwide operators are also included in this chapter. The future aim of 5G mobile network communication while moving is made possible, which brings digital to every person, home and organization for a fully connected, intelligent world.
Small- and medium-sized enterprises (SMEs) are the most significant contributors to the manufacturing economy in the UK and Europe. However, unlike the big multinational companies, they typically have limited resources and usually lack the capability to invest in new and emerging technologies. Studies indicate that UK industries, especially SMEs, face challenges and also opportunities in their quest to adopting technologies leading to the fourth industrial revolution, Industry 4.0. For the UK manufacturing industry to remain competitive, it is essential that the potential of Industry 4.0 for growth and increased productivity is seriously embraced by SMEs. As the rate of adoption of new technologies accelerates, the UK SMEs cannot afford to lose their competitive advantage to the more advanced competitors. The UK already has high-performing manufacturing sectors in the application of digitization that it has the potential to be a leader in Europe in digital manufacturing. As an example, the UK has the strongest artificial intelligence and machine learning market in Europe, with over 200 SMEs in the field [Made Smarter Review (2017), Department for business, energy & industrial strategy, available at https://www.gov.uk/government/publications/ made-smarter-review]. Therefore, there is a strong need for developing smart methods and tools that could support SMEs in their transformation towards Industry 4.0. This chapter looks at the opportunities and challenges that SMEs face in adopting Industry 4.0 and their readiness for this fourth-generation industry. It also highlights some potential tools that could help SMEs on their move towards Industry 4.0.