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Industrial smart product-service system (ISPSS), as an emerging industrial digital servitisation paradigm, has attracted ever-increasing attentions from both industries and academics recently. The prevailing adoption of cutting-edge information and communication technologies, digital technologies and artificial intelligence has enabled the engineering lifecycle management in an ever-smarter manner with context awareness. Nevertheless, little study has provided any systematic process to prescribe its sustainable development throughout the engineering lifecycle, let alone any to consider sustainability in the cyber space other than the physical components. Aiming to fill the gap, this work outlines the definition, key features and social, economic and environmental concerns of the proposed sustainable ISPSS, and further proposes a systematic four-phase cyber-physical development framework by referring to the ISO14001:2015 standards. As a short communication, it is hoped this research can attract more open discussion and in-depth investigation towards sustainable ISPSS development in the near future.
Defect detection is now an active research area for production quality assurance. Traditional visual inspection systems are developed by human beings, which is a time-consuming, labour-intensive, and highly error-prone process, and are therefore unreliable. To overcome these problems, the authors proposed a new method for detecting defects when printing on a 3D micro-textured surface. They utilise an orientation code as the basis to resist the fluctuations in illumination. Based on the consistency of the pixel pairs, they developed a model called multiple paired pixel consistency to represent the statistical relationship between each pixel pair in defect-free images. Finally, based on this model, they designed a defect detection method. Even with different defect sizes, illumination conditions, noise intensities, and other characteristics, the performance of the proposed algorithm is extremely stable and highly accurate, and the recall, precision, and F-measure in most of the results can reach 0.85,0.93, and 0.9, respectively. In addition, the defect detection rate can reach almost 100%. This demonstrates that the authors' approach can achieve state-of-the-art accuracy in real industrial applications.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
Cone pyrolysis liquefaction reactor is the key core device of biomass energy production equipment. The study of the dynamics of the cone is of great significance to the bioenergy conversion technology. Proper control of the movement time of the heat carrier in the cone can prevent it. Biomass superheated carbonisation can increase the ratio of gas and liquid formation. It is a difficult and key technology in the design of the cone pyrolysis liquefaction reactor. The kinetic differential equation of the relative motion of heat carrier particles can be obtained by kinetic analysis and calculation. It can play a key role in the in-depth study of the structural size design of the cone and the optimisation of the structural size parameters. This study conducts a dynamic simulation of the physical simplification model of biomass energy production equipment. The computer simulation software was used to simulate the running process of the simplified cone mechanism model. A reasonable physical prototype that meets the process parameters is designed. In view of the above complex dynamic phenomena, it is necessary to comprehensively analyse various methods of dynamics, and use appropriate numerical analysis and computer-aided design methods to carry out system equipment research and design.
Biomass fuel production has a variety of methods, and the use of biomass cracking to produce biofuels is an advanced technology used in many countries. In the control of the biofuel production process, how to get the motion law of the heat carrier in the cone relative to the cone becomes a problem that must be solved. This study uses the basic theory of dynamics and computer simulation calculation function to carry out dynamic analysis of each of the above contents, and analyses the response problem of comprehensive factors, and obtains the simulation results in accordance with the experimental results. This analysis provides a detailed design rationale for the optimal design of biomass cone pyrolysis equipment.