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Refrigerator and Cold Storage Systems (RCSS), also known as cold chain are utilized in a wide variety of applications for the storage of sensitive goods. Consequently, there is a common need for optimal and continuous operation of the refrigerator group, including the freezing chamber and the compressor group. Therefore, under the framework of Industry 4.0, Internet of Things (IoT) enabling technologies can be utilized for the development of advanced tools which in turn can enable the remote monitoring of the refrigeration cycle as well as with the integration of intelligent algorithms to predict future malfunctions. This paper presents the design and development of a framework for the remote monitoring of RCSS based on the implementation of a Wireless Sensor Network (WSN) for data acquisition, and intelligent algorithms for Predictive Maintenance.
Dimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. Design and development of an IoT enabled platform for remote monitoring and predictive maintenance of industrial equipment. Procedia Manufacturing 2021, 54, 166 -171.
AMA StyleDimitris Mourtzis, John Angelopoulos, Nikos Panopoulos. Design and development of an IoT enabled platform for remote monitoring and predictive maintenance of industrial equipment. Procedia Manufacturing. 2021; 54 ():166-171.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. 2021. "Design and development of an IoT enabled platform for remote monitoring and predictive maintenance of industrial equipment." Procedia Manufacturing 54, no. : 166-171.
Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) can be considered as the cornerstones of the lifecycle of a manufacturing asset. Since the above-mentioned processes often involve multiple engineers, from different departments even from different companies, it is crucial to ensure the flawless communication between the different individuals as well as to make the design process more intuitive. In the era of digitalization and Internet of Things, digital technologies such as Mixed Reality (MR) are utilized from engineers in order to leverage the capabilities of existing computer aided tools (CAx). Therefore, in this research work, the design and development of a Cloud-based and Mixed Reality-based framework is presented. The purpose of the framework is to facilitate engineers during the design and manufacturing phase of new components by providing a mobile application and common design tools via MR graphical user interfaces (GUI). Further to that, taking into consideration the advanced capabilities of MR devices in terms of user interaction, special emphasis is given on the interaction of engineers with the holograms of the components via simple hand gestures. The resulting 3D geometries can be imported in CAD suites either for further development or for manufacturing the designed component. The applicability of the proposed framework is tested and validated in a laboratory-based machine shop.
Dimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. Collaborative manufacturing design: a mixed reality and cloud-based framework for part design. Procedia CIRP 2021, 100, 97 -102.
AMA StyleDimitris Mourtzis, John Angelopoulos, Nikos Panopoulos. Collaborative manufacturing design: a mixed reality and cloud-based framework for part design. Procedia CIRP. 2021; 100 ():97-102.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. 2021. "Collaborative manufacturing design: a mixed reality and cloud-based framework for part design." Procedia CIRP 100, no. : 97-102.
As the industrial requirements change rapidly due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient manufacturing system design arises more intensely than ever. Production system simulation has proven to be a powerful tool for designing and evaluating a manufacturing system due to its low cost, quick analysis, low risk and meaningful insight that it may provide, improving the understanding of the influence of each component. In this research work, the design and evaluation of a real manufacturing system using Discrete Event Simulation (DES), based on real data obtained from the copper industry is presented. The current production system is modelled, and the real production data are analyzed and connected. The impact identification of the individual parameters on the response of the system is accomplished towards the selection of the proper configurations for near-optimum outcome. Further to that, different simulation scenarios based on the Design of Experiments (DOE) are studied towards the optimization of the production, under predefined product analogies.
Dimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. Robust Engineering for the Design of Resilient Manufacturing Systems. Applied Sciences 2021, 11, 3067 .
AMA StyleDimitris Mourtzis, John Angelopoulos, Nikos Panopoulos. Robust Engineering for the Design of Resilient Manufacturing Systems. Applied Sciences. 2021; 11 (7):3067.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. 2021. "Robust Engineering for the Design of Resilient Manufacturing Systems." Applied Sciences 11, no. 7: 3067.
A digitalized Smart Factory can be considered as a data island. Moreover, engineers have focused on the development of new technologies and techniques not only for transforming information to data but also to achieve efficient data utilization to further optimize manufacturing processes. However, the Zero-Defect Manufacturing concept has emerged, where the main goal is production optimization. The cornerstone in achieving the factories of the future is to further optimize the design of new assets so as they comply with the unique requirements of the customers. Therefore, this paper proposes the conceptualization, design, and initial development of a platform for the utilization of data derived from industrial environments for the optimization of the equipment design. The main aspects of the proposed framework are the data acquisition, data processing and the simulation. The applicability of the proposed framework has been tested in a laboratory-based machine shop utilizing data from a real-life industrial scenario.
Dimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. Equipment Design Optimization Based on Digital Twin Under the Framework of Zero-Defect Manufacturing. Procedia Computer Science 2021, 180, 525 -533.
AMA StyleDimitris Mourtzis, John Angelopoulos, Nikos Panopoulos. Equipment Design Optimization Based on Digital Twin Under the Framework of Zero-Defect Manufacturing. Procedia Computer Science. 2021; 180 ():525-533.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. 2021. "Equipment Design Optimization Based on Digital Twin Under the Framework of Zero-Defect Manufacturing." Procedia Computer Science 180, no. : 525-533.
The shift of profit margins from products to services, has transformed traditional production equipment supplier industries to providers of Industrial Product-Service Systems (IPSS). IPSS is a new business model for consistent delivery of industrial products such as production equipment and manufacturing services (Manufacturing as a Service). However, procurement of IPSS between industrial companies (i.e. Business-to-Business - B2B) is more complicated compared to the case of products offered to consumers (i.e. Business-to-Consumer – B2C). The complexity in interaction between the involved B2B stakeholders, the lack of trust and high costs especially for Small Medium Enterprises have hampered the establishment of standardized e-marketplaces in a similar manner as in the business to consumer world. This research work presents an overview on the requirements to support supply-chain processes on a digital B2B platform as well as a discussion of the objectives and the benefits of this multi-sided platform.
D. Mourtzis; J. Angelopoulos; N. Panopoulos. A survey of digital B2B platforms and marketplaces for purchasing industrial product service systems: A conceptual framework. Procedia CIRP 2021, 97, 331 -336.
AMA StyleD. Mourtzis, J. Angelopoulos, N. Panopoulos. A survey of digital B2B platforms and marketplaces for purchasing industrial product service systems: A conceptual framework. Procedia CIRP. 2021; 97 ():331-336.
Chicago/Turabian StyleD. Mourtzis; J. Angelopoulos; N. Panopoulos. 2021. "A survey of digital B2B platforms and marketplaces for purchasing industrial product service systems: A conceptual framework." Procedia CIRP 97, no. : 331-336.
The evolution of production systems encloses continuous adaptation of workplaces with changing levels of technologies and automation. This research paper examines scenarios of multi-skilled operators, cooperating to accomplish a common goal. The under-consideration environment consists of a distributed network of workstations, each one assigned with a set of pending jobs. Therefore, the production engineer faces the challenging task of job allocation to suitable operators and appropriate workstations. As such, the formulation of the problem is based on the evaluation of each individual operator's job-related skills and the provision of an intelligent decision-making algorithm for the human resources allocation. The framework addresses the optimization of the allocated operators-jobs correlation and the workforce cost as the model's decision criteria. The model was implemented in a cross-platform planning application and tested in a real-life industrial scenario.
D. Mourtzis; V. Siatras; J. Angelopoulos; N. Panopoulos. An intelligent model for workforce allocation taking into consideration the operator skills. Procedia CIRP 2021, 97, 196 -201.
AMA StyleD. Mourtzis, V. Siatras, J. Angelopoulos, N. Panopoulos. An intelligent model for workforce allocation taking into consideration the operator skills. Procedia CIRP. 2021; 97 ():196-201.
Chicago/Turabian StyleD. Mourtzis; V. Siatras; J. Angelopoulos; N. Panopoulos. 2021. "An intelligent model for workforce allocation taking into consideration the operator skills." Procedia CIRP 97, no. : 196-201.
The demand response monitoring is one key energy-efficient production technique and can reduce costs contributing simultaneously to the reduction of environmental emissions and maintaining the stability of electrical grids following the policies examined from European and International organizations. Although, energy trade market effect has been investigated in the literature, the novelty of this research work lies in the design and development of an Industrial Product Service System (IPSS) business model, aiming to the collaboration between Energy Sales Companies (ESC) and manufacturing companies. ESC through an energy-demand management tool, supported by data acquisition devices connected to the Cloud, calculates and signals energy price forecasts to the manufacturers. Moreover, an adaptive production scheduling approach considering the power usage of manufacturers in response to time-varying energy prices is presented. Particularly, an intelligent algorithm is applied for the generation and examination of multi-criteria alternative assignment scenarios and an evaluation of each planning scenario against a set of user-defined criteria is made for the identification of the most efficient ones. The proposed approach is validated in an industrial case study using production and energy-consumption data from a mold making industry. The results indicate potential savings for the industrial consumer finding both actors to mutually benefit.
Dimitris Mourtzis; N. Boli; E. Xanthakis; K. Alexopoulos. Energy trade market effect on production scheduling: an Industrial Product-Service System (IPSS) approach. International Journal of Computer Integrated Manufacturing 2021, 34, 76 -94.
AMA StyleDimitris Mourtzis, N. Boli, E. Xanthakis, K. Alexopoulos. Energy trade market effect on production scheduling: an Industrial Product-Service System (IPSS) approach. International Journal of Computer Integrated Manufacturing. 2021; 34 (1):76-94.
Chicago/Turabian StyleDimitris Mourtzis; N. Boli; E. Xanthakis; K. Alexopoulos. 2021. "Energy trade market effect on production scheduling: an Industrial Product-Service System (IPSS) approach." International Journal of Computer Integrated Manufacturing 34, no. 1: 76-94.
By virtue of the Digitalization of Manufacturing Systems, the importance of interconnecting multiple digital systems is becoming a necessity. By extension, the need for connecting the shop-floor technician to the rest of the production system is highlighted more than ever. Internet and Communication technologies (ICT) may connect the technician with data collected from the machine sensors, knowledge repositories and integrated production software. Mobile devices offer highly usable interfaces that can be exploited to involve the operator in this process, digitalize their inputs and also visualize machine information on top of the physical system through Augmented Reality (AR). Towards that direction, an application that allows the operator to monitor machine status and call event-driven AR remote machine maintenance and rescheduling based on maintenance time estimation is developed. The framework is tested and validated in a laboratory-base machine shop.
Dimitris Mourtzis; John Angelopoulos; Vasilios Zogopoulos. Integrated and adaptive AR maintenance and shop-floor rescheduling. Computers in Industry 2020, 125, 103383 .
AMA StyleDimitris Mourtzis, John Angelopoulos, Vasilios Zogopoulos. Integrated and adaptive AR maintenance and shop-floor rescheduling. Computers in Industry. 2020; 125 ():103383.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Vasilios Zogopoulos. 2020. "Integrated and adaptive AR maintenance and shop-floor rescheduling." Computers in Industry 125, no. : 103383.
The currently applied maintenance strategies, including Reactive and Preventive maintenance can be considered obsolete. The constant improvements in Information and Communication Technologies as well as in Digital Technologies along with the increase of computational power, have facilitated the development of new Artificial Intelligence algorithms to integrate cognition in computational systems. This trend is posing a great challenge for engineers, as such developments will enable the creation of robust systems that can monitor the current status of the machines and by extension to predict unforeseeable situations. Furthermore, Smart Computers will be capable of examining all possible scenarios and suggest viable solutions in a fraction of time compared to humans. Therefore, in this paper, the modelling, design and development of a Predictive Maintenance and Remote Monitoring system are proposed, based on the utilization of Artificial Intelligence algorithms for data acquisition, fusion, and post-processing. In addition to that, the proposed framework will integrate a Mixed Reality application for the intuitive visualization of the data, that will ultimately facilitate production and maintenance engineers to monitor the condition of the machines, and most importantly to get an accurate prediction of the oncoming failures.
Dimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality. Frontiers in Mechanical Engineering 2020, 6, 1 .
AMA StyleDimitris Mourtzis, John Angelopoulos, Nikos Panopoulos. Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality. Frontiers in Mechanical Engineering. 2020; 6 ():1.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. 2020. "Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality." Frontiers in Mechanical Engineering 6, no. : 1.
During the last decade, the technologies related to electric vehicles (EVs) have captured both scientific and industrial interest. Specifically, the subject of the smart charging of EVs has gained significant attention, as it facilitates the managed charging of EVs to reduce disturbances to the power grid. Despite the presence of an extended literature on the topic, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The framework consists of a heuristic algorithm that facilitates the charge scheduling within a charging station (CS), and the analytic hierarchy process (AHP) to support the driver of the EV selecting the most appropriate charging station based on their needs of transportation and personal preferences. The communications are facilitated by the Open Platform Communications–Unified Architecture (OPC–UA) standard. For the selection of the scheduling algorithm, the genetic algorithm and particle swarm optimisation have been evaluated, where the latter had better performance. The performance of the charge scheduling is evaluated, in various charging tasks, compared to the exhaustive search for small problems.
Nikolaos Milas; Dimitris Mourtzis; Emmanuel Tatakis. A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver. Energies 2020, 13, 6120 .
AMA StyleNikolaos Milas, Dimitris Mourtzis, Emmanuel Tatakis. A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver. Energies. 2020; 13 (22):6120.
Chicago/Turabian StyleNikolaos Milas; Dimitris Mourtzis; Emmanuel Tatakis. 2020. "A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver." Energies 13, no. 22: 6120.
The digitalization of industry is targeting at the integration of artificial intelligence (AI) in manufacturing systems, for delivering intelligent machinery. Although AI seems a long-term target, similar enabling technologies such as artificial neural networks (ANNs) have been introduced. Despite that ANNs are inspired by the human brain’s functioning, understanding how they work and training them is a challenging task, requiring engineers with advanced math and coding skills. On the contrary, augmented reality (AR) is a cutting-edge digital technology, enabling the registration of 3D content on the physical environment, thus enhancing user’s perception in a growing variety of scientific fields. Therefore, this research work aims at the design and development of an AR-based framework that facilitates the conceptualization of an ANN through AR, assists engineers train efficient ANN and moreover share knowledge through suitable communication channels. Finally, the framework can handle datasets with the use of cloud services.
D. Mourtzis; J. Angelopoulos. An intelligent framework for modelling and simulation of artificial neural networks (ANNs) based on augmented reality. The International Journal of Advanced Manufacturing Technology 2020, 111, 1603 -1616.
AMA StyleD. Mourtzis, J. Angelopoulos. An intelligent framework for modelling and simulation of artificial neural networks (ANNs) based on augmented reality. The International Journal of Advanced Manufacturing Technology. 2020; 111 (5-6):1603-1616.
Chicago/Turabian StyleD. Mourtzis; J. Angelopoulos. 2020. "An intelligent framework for modelling and simulation of artificial neural networks (ANNs) based on augmented reality." The International Journal of Advanced Manufacturing Technology 111, no. 5-6: 1603-1616.
The Fourth Industrial Revolution has made efficient and adaptive manufacturing a prominent research topic. The necessity for personalized products leads to complex production lines. Due to the increased complexity, planning and scheduling of manufacturing processes seeks to identify near-optimum solutions to ensure fast and precise decision making. This research paper aims to contribute to the field of adaptive scheduling by proposing an algorithm that enables near real-time cooperation among machines, workforce and the production manager. A framework for self-adaptive scheduling in a real-world manufacturing case deriving from an SME that produces solar panels will be presented.
D. Mourtzis; V. Siatras; G. Synodinos; J. Angelopoulos; N. Panopoulos. A Framework for Adaptive Scheduling in Cellular Manufacturing Systems. Procedia CIRP 2020, 93, 989 -994.
AMA StyleD. Mourtzis, V. Siatras, G. Synodinos, J. Angelopoulos, N. Panopoulos. A Framework for Adaptive Scheduling in Cellular Manufacturing Systems. Procedia CIRP. 2020; 93 ():989-994.
Chicago/Turabian StyleD. Mourtzis; V. Siatras; G. Synodinos; J. Angelopoulos; N. Panopoulos. 2020. "A Framework for Adaptive Scheduling in Cellular Manufacturing Systems." Procedia CIRP 93, no. : 989-994.
In modern manufacturing world, MRO (Maintenance and Repair Operations) are the cornerstone for keeping industrial equipment in near-optimum condition. Successful completion of MRO has been benefited from Augmented Reality (AR), by considerably decreasing MTTR (Mean Time to Repair). To that end, AR delivers the digital tools that help on-the-field technicians to perform MRO easily and intuitively, however intense development is required for generating AR instructions. The latest advances in computer technologies, concretely in Convolutional Neural Networks, have enabled advanced computer vision. This research paper presents a framework for generating AR maintenance instructions, based on advanced computer vision and Convolutional Neural Networks (CNN). The applicability of the framework is tested in-vitro in a lab-based machine shop.
Dimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. A Framework for Automatic Generation of Augmented Reality Maintenance & Repair Instructions based on Convolutional Neural Networks. Procedia CIRP 2020, 93, 977 -982.
AMA StyleDimitris Mourtzis, John Angelopoulos, Nikos Panopoulos. A Framework for Automatic Generation of Augmented Reality Maintenance & Repair Instructions based on Convolutional Neural Networks. Procedia CIRP. 2020; 93 ():977-982.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. 2020. "A Framework for Automatic Generation of Augmented Reality Maintenance & Repair Instructions based on Convolutional Neural Networks." Procedia CIRP 93, no. : 977-982.
The Industry 4.0 paradigm has led to the creation of new opportunities for taking advantage of a series of diverse technologies in the manufacturing domain, including Internet of Things, Augmented and Virtual Reality, Machine Learning, Advanced Robotics, Additive Manufacturing, System and Process Simulation, Computer‐Aided Design/Engineering/Manufacturing/Process Planning systems as well as Product Lifecycle Management platforms.
Nikolaos Papakostas; Carmen Constantinescu; Dimitris Mourtzis. Novel Industry 4.0 Technologies and Applications. Applied Sciences 2020, 10, 6498 .
AMA StyleNikolaos Papakostas, Carmen Constantinescu, Dimitris Mourtzis. Novel Industry 4.0 Technologies and Applications. Applied Sciences. 2020; 10 (18):6498.
Chicago/Turabian StyleNikolaos Papakostas; Carmen Constantinescu; Dimitris Mourtzis. 2020. "Novel Industry 4.0 Technologies and Applications." Applied Sciences 10, no. 18: 6498.
Moving towards factories of the future, Human-Robot Interfaces (HRIs) have come to the foreground. HRIs, offer extended potential in terms of flexibility, time and cost reduction, ergonomics, and the overall company’s sustainability. What is needed, is the provision of digital tools that will accelerate HRI integration to the existing manufacturing plants as well as render their complex behavior, predictable. Following this rationale, this paper presents, the design of a prediction model, for robot moves in hybrid assembly stations, based on the robot’s Digital Twin and a statistical regression model. In addition to that, respect is given to safety standards as well as to robot capabilities. The resulting model is validated against a simulation software, and further implemented in a pilot case derived from the automotive industry.
Dimitris Mourtzis; John Angelopoulos; Vasileios Siatras. Cycle Time Estimation Model for Hybrid Assembly Stations Based on Digital Twin. Collaboration in a Hyperconnected World 2020, 169 -175.
AMA StyleDimitris Mourtzis, John Angelopoulos, Vasileios Siatras. Cycle Time Estimation Model for Hybrid Assembly Stations Based on Digital Twin. Collaboration in a Hyperconnected World. 2020; ():169-175.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Vasileios Siatras. 2020. "Cycle Time Estimation Model for Hybrid Assembly Stations Based on Digital Twin." Collaboration in a Hyperconnected World , no. : 169-175.
In the competitive era of industrial automation, enterprises are struggling to maintain their competitiveness, through modernization of their manufacturing systems, while trying to meet market demands. Modernization of manufacturing systems refers to the replacement of manufacturing equipment with state-of-the-art machinery. A good alternative is the act of retrofitting existing machinery, by adding new features. The challenge arising by retrofitting is the selection of the suitable features to be added. This paper presents a conceptual framework to assist the decision-making process, supported by an online network and based on Augmented Reality for retrofitting and recycling machinery, aiming at increasing components’ lifecycle.
Dimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. Recycling and retrofitting for industrial equipment based on augmented reality. Procedia CIRP 2020, 90, 606 -610.
AMA StyleDimitris Mourtzis, John Angelopoulos, Nikos Panopoulos. Recycling and retrofitting for industrial equipment based on augmented reality. Procedia CIRP. 2020; 90 ():606-610.
Chicago/Turabian StyleDimitris Mourtzis; John Angelopoulos; Nikos Panopoulos. 2020. "Recycling and retrofitting for industrial equipment based on augmented reality." Procedia CIRP 90, no. : 606-610.
Integrating the Internet of Things in Industry 4.0 demands the combination of existing practises with new technologies. Augmented Reality (AR) is a cutting-edge technology of the new manufacturing era. The fourth industrial revolution challenges and AR technology advances, promise to improve productiveness, working quality, user experience and allow better use of resources. AR combined with mass customization could fulfil rising market demands and customer functional requirements. This work presents the development of an AR application to integrate customers in the designing process with product customization. The application is to be validated and used in the industry of Robotic Cell Manufacturing.
D. Mourtzis; G. Synodinos; John Angelopoulos; Nikos Panopoulos. An augmented reality application for robotic cell customization. Procedia CIRP 2020, 90, 654 -659.
AMA StyleD. Mourtzis, G. Synodinos, John Angelopoulos, Nikos Panopoulos. An augmented reality application for robotic cell customization. Procedia CIRP. 2020; 90 ():654-659.
Chicago/Turabian StyleD. Mourtzis; G. Synodinos; John Angelopoulos; Nikos Panopoulos. 2020. "An augmented reality application for robotic cell customization." Procedia CIRP 90, no. : 654-659.
This paper aims at performing an overview of the Innovation notion and the selection and discussion of appropriate innovation metrics and challenges, mainly in the context of manufacturing. This review has been performed through literature, on the notion of Innovation and its classification, according to various dimensions/aspects affecting manufacturing.
Aristidis Mamasioulas; Dimitris Mourtzis; George Chryssolouris. A manufacturing innovation overview: concepts, models and metrics. International Journal of Computer Integrated Manufacturing 2020, 33, 769 -791.
AMA StyleAristidis Mamasioulas, Dimitris Mourtzis, George Chryssolouris. A manufacturing innovation overview: concepts, models and metrics. International Journal of Computer Integrated Manufacturing. 2020; 33 (8):769-791.
Chicago/Turabian StyleAristidis Mamasioulas; Dimitris Mourtzis; George Chryssolouris. 2020. "A manufacturing innovation overview: concepts, models and metrics." International Journal of Computer Integrated Manufacturing 33, no. 8: 769-791.
Scheduling and monitoring are two of the main components of production management that are crucial for the seamless function of production. Moreover, following the recent technological achievements, Augmented Reality (AR) is an upcoming way to display a variety of data in a more easily perceivable and intractable way. The proposed approach aims to couple production scheduling and monitoring along with augmented reality, in order to allow the user to interact with those components in a more realistic way. The proposed approach is validated in a case study of a real workshop.
Dimitris Mourtzis; Vasilis Siatras; Vasilios Zogopoulos. Augmented reality visualization of production scheduling and monitoring. Procedia CIRP 2020, 88, 151 -156.
AMA StyleDimitris Mourtzis, Vasilis Siatras, Vasilios Zogopoulos. Augmented reality visualization of production scheduling and monitoring. Procedia CIRP. 2020; 88 ():151-156.
Chicago/Turabian StyleDimitris Mourtzis; Vasilis Siatras; Vasilios Zogopoulos. 2020. "Augmented reality visualization of production scheduling and monitoring." Procedia CIRP 88, no. : 151-156.
Industry 4.0 enables the transition of traditional manufacturing models to the digitalized paradigm, creating significant economic opportunities through market reshaping. Scheduling is a key field of manufacturing systems. Academia and industry are closely collaborating for producing enhanced solutions, taking advantage of multiple criteria. Initially, the scheduling problem was dealt with more simplistic methods resulting in static solutions; however, with the evolution of digital technologies, scheduling became more dynamic to the company’s environmental changes. As Information and Communication Technologies (ICT) became mainstream and systems were integrated, rescheduling and adaptive scheduling became the cornerstones of Smart Manufacturing. These technologies have been further advanced to yield more reliable results in a shorter period of time. The efficient design, planning, and operation of manufacturing systems and networks can be achieved with the adoption of cyber physical systems (CPS) in conjunction with the Internet of Things (IoT) and cloud computing. The transition to Smart Manufacturing is achieved with the adoption of cutting-edge digital technologies and the integration state-of-the-art manufacturing assets. Consequently, this chapter presents an opportunity for tracking the evolution of scheduling techniques during the last decade, as well as for extracting insightful and meaningful inferences from the application of innovative solutions in industrial use cases.
D. Mourtzis. Adaptive Scheduling in the Era of Cloud Manufacturing. Handbook of Healthcare Logistics 2020, 61 -85.
AMA StyleD. Mourtzis. Adaptive Scheduling in the Era of Cloud Manufacturing. Handbook of Healthcare Logistics. 2020; ():61-85.
Chicago/Turabian StyleD. Mourtzis. 2020. "Adaptive Scheduling in the Era of Cloud Manufacturing." Handbook of Healthcare Logistics , no. : 61-85.