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Augmented Reality in Supply Chain Management
Published in Turan Paksoy, Çiğdem Koçhan, Sadia Samar Ali, Logistics 4.0, 2020
Sercan Demir, Ibrahim Yilmaz, Turan Paksoy
Mechanization and harnessing mechanical power led to the transition from manual work to the first mechanical manufacturing process during the 1800s. This period was the debut of the First Industrial Revolution. The Second Industrial Revolution started as a result of electrification that led to industrialization and mass production during the late 19th century. The Third Industrial Revolution was initiated by the appearance of microelectronic devices such as transistors and microprocessors, and automated systems. In this era, flexible production was achieved by the integration of the programmable machines on flexible production lines (Rojko 2017). All industrial revolutions have brought along their unique disruptive technologies in manufacturing. Steam engine, automated electrical production line, and digital production methods were the major innovations that appeared during the first three industrial revolutions, respectively. The process of industrialization continues with the Fourth Industrial Revolution, namely Industry 4.0. The most recent industrial revolution has brought the concept of “smart products” and “smart factory”. Smart products are uniquely identifiable, can be detected anytime throughout the supply chain, and their history, current status, and alternative routes to reach their destination can be easily monitored. The emerging technologies are inseparable parts of the smart factories. For instance, cyber-physical systems (CPS) take part in monitoring manufacturing processes, creating a virtual copy of the physical world, and making decentralized decisions, while they communicate and cooperate with the Internet of Things (IoT) and humans simultaneously (Carvalho et al. 2018).
Machine Learning Algorithms for Industry Using Image Sensing
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Aakash Dhall, Hemant K Upadhyay, Sapna Juneja, Abhinav Juneja
Q. Qi et al. [4] explained, A vision of future production goods in the production process finding their path. In intelligent factories, devices and goods interact cooperatively, the Internet of Things interconnects driving raw materials and machinery. There are already factories in the future with network machines and products; however, these previously autonomous devices would be linked to a full grid. The sensors, communication technologies and cyber-physical systems link all devices, machines and materials (CPS). They converse with one another and collaborate to influence one another. The logic of Cyber-physical systems underpins Industry 4.0.
The Industry 4.0 Architecture and Cyber-Physical Systems
Published in Diego Galar Pascual, Pasquale Daponte, Uday Kumar, Handbook of Industry 4.0 and SMART Systems, 2019
Diego Galar Pascual, Pasquale Daponte, Uday Kumar
In 2010, Professor Edward A. Lee from the University of California, Berkeley, defined CPS as follows: “Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa.” On his page on the Berkeley website, Lee shows the CPS concept in the form of a mind map (see https://www2.eecs.berkeley.edu/Faculty/Homepages/lee.html).
Architecture and knowledge modelling for self-organized reconfiguration management of cyber-physical production systems
Published in International Journal of Computer Integrated Manufacturing, 2022
Timo Müller, Simon Kamm, Andreas Löcklin, Dustin White, Marius Mellinger, Nasser Jazdi, Michael Weyrich
The future of industrial automation will be shaped by the concept of cyber-physical systems (CPSs), which are physical systems with their own intelligence and cyber abilities, and will feature a high degree of intelligence (Wan et al. 2018; Grochowski et al. 2020; Vogel-Heuser et al. 2020). This is due to the promising potentials which CPSs offer for the production domain. Some of these are self-configuration or self-organization capabilities, which, e.g. lead to more cost-effective and efficient production. Furthermore, increasing demand to customize products (Zhang et al. 2016), shorter innovation and product life cycles (Köcher et al. 2020; Järvenpää, Siltala, and Lanz 2016) result in frequently changing production requirements. Therefore, objectives for production systems are becoming ever more unpredictable during the design phase of these systems. Consequently, adaptations of production systems by means of reconfigurations (i.e. adaptations during the operational phase) have to be carried out frequently. Production systems composed of multiple CPSs are also referred to as Cyber-Physical Production Systems (CPPSs). Hence, the reconfiguration of CPPSs has become an active field of research (Hengstebeck, Barthelmey, and Deuse 2018; Balzereit and Niggemann 2020; Engelsberger and Greiner 2018) that tackles the challenges of frequent changes during the operation of future production systems.
Industrial big-data-driven and CPS-based adaptive production scheduling for smart manufacturing
Published in International Journal of Production Research, 2021
The CPS notation can be traced back to October 2006, at the first NSF Workshop on cyber-physical systems, in Austin, Texas. As the name suggests, CPS represents a new generation of systems in which computing and communication capabilities are integrated with the dynamics of physical and engineered systems. According to Edward A. Lee's definition (Lee 2007), ‘Cyber-Physical Systems (CPS) are integrations of computation with physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa’. In the past decade, both theoretical and practical studies on CPSs have been actively and effectively pursused. The wide range of CPS applications includes agriculture, building controls, transportation, defense, energy, healthcare, and manufacturing and industry (CPS 2015; Monostori et al. 2016).
AGV dispatching algorithm based on deep Q-network in CNC machines environment
Published in International Journal of Computer Integrated Manufacturing, 2021
Kyuchang Chang, Seung Hwan Park, Jun-Geol Baek
The emergence of the fourth industrial revolution has prompted a paradigm change in the manufacturing process. Industry 4.0 is the phrase used to denote the fourth industrial revolution, which comprises merging different technologies to improve the organization and management of manufacturing environments (Wang et al. 2016; Chen et al. 2015). This study narrows this scope and focuses on improving the productivity of smartphones. As some of the most cutting-edge devices defining our current generation of science, smartphones offer operations as diverse as computers, while providing significantly more ease of use and portability in daily life. Their popularity has made stepping up the competitiveness of smartphone production a principal challenge for smartphone makers. A smart factory is a key entity for achieving innovation in smartphone production. A smart factory aims to enhance communication between systems and manage the facilities inside the factory (Zuehlke et al., 2010). Using sensor data from real-world systems to create a virtual counterpart, a cyber-physical system (CPS) allows for improved monitoring and control of real-world systems (Lee, Bagheri, and Kao 2015). In particular, a CPS enhances advanced scheduling as it facilitates the collection and utilization of the necessary data from manufacturing processes, and models the processes into simulations in virtual space. Therefore, a present-day factory can apply state-of-the-art reinforcement learning (RL) techniques to manufacturing processes. However, there are only extremely limited serious applications thereof in the smartphone manufacturing industry.