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Nikolaos Milas was born in 12/04/1990 in Patras. He received his diploma (Master degree equivalent) from the Department of Electrical and Computer Engineering of the University of Patras in 2014. He possesses an MSc degree in the subject of Design and Production received from the Department of Mechanical Engineering and Aeronautics of the University of Patras in 2016. Since 2016 he is a PhD candidate on the subject of the “Smart Energy Management of Electric Vehicles”. His main research activities include the Power Electronics for Electric Vehicles, the Microcomputer Systems for Electric Vehicles, the Internet of Things for Production Systems, and Production Automation. He is co-author of several scientific papers in his field, published in International Journals and Conferences. He has been involved in National and European research projects, as well as in Industrial research projects.
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.
Nikolaos Milas; Dimitris A. Mourtzis; Panagiotis I. Giotakos; Emmanuel C. Tatakis. Two-Layer Genetic Algorithm for the Charge Scheduling of Electric Vehicles. 2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe) 2020, 1 .
AMA StyleNikolaos Milas, Dimitris A. Mourtzis, Panagiotis I. Giotakos, Emmanuel C. Tatakis. Two-Layer Genetic Algorithm for the Charge Scheduling of Electric Vehicles. 2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe). 2020; ():1.
Chicago/Turabian StyleNikolaos Milas; Dimitris A. Mourtzis; Panagiotis I. Giotakos; Emmanuel C. Tatakis. 2020. "Two-Layer Genetic Algorithm for the Charge Scheduling of Electric Vehicles." 2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe) , no. : 1.
Nikolaos Milas; Emmanuel C. Tatakis. Charging Station Selection through the Analytic Hierarchy Process enabled by OPC-UA for Vehicle-to-Grid Communications. 2019 21st European Conference on Power Electronics and Applications (EPE '19 ECCE Europe) 2019, 1 .
AMA StyleNikolaos Milas, Emmanuel C. Tatakis. Charging Station Selection through the Analytic Hierarchy Process enabled by OPC-UA for Vehicle-to-Grid Communications. 2019 21st European Conference on Power Electronics and Applications (EPE '19 ECCE Europe). 2019; ():1.
Chicago/Turabian StyleNikolaos Milas; Emmanuel C. Tatakis. 2019. "Charging Station Selection through the Analytic Hierarchy Process enabled by OPC-UA for Vehicle-to-Grid Communications." 2019 21st European Conference on Power Electronics and Applications (EPE '19 ECCE Europe) , no. : 1.
The Industry 4.0 philosophy promotes the digitalisation of manufacturing systems. This results in a transformation of the traditional manufacturing systems and creates new capabilities in the subjects of monitoring and control. To assist this shift, new modelling practices and communication standards should be employed. Towards this end, this paper presents a framework for the modelling of milling and lathe CNC machine-tools, through a general machine model. The framework is based on the Open Platform Communications – Unified Architecture (OPC-UA) communications standard to provide a macroscopic and microscopic view of machine shops, towards the Machine Shop 4.0 concept. The use of OPC-UA enables the integration of different systems, following semantic modelling. Moreover, to advance legacy machine-tools into the digitalised era, a data acquisition device is developed. Therefore, machine-tools without connectivity capabilities can be integrated in this holistic framework. The proposed system is validated in a Laboratory case study.
Dimitris Mourtzis; Nikolaos Milas; Nikolaos Athinaios. Towards Machine Shop 4.0: A General Machine Model for CNC machine-tools through OPC-UA. Procedia CIRP 2018, 78, 301 -306.
AMA StyleDimitris Mourtzis, Nikolaos Milas, Nikolaos Athinaios. Towards Machine Shop 4.0: A General Machine Model for CNC machine-tools through OPC-UA. Procedia CIRP. 2018; 78 ():301-306.
Chicago/Turabian StyleDimitris Mourtzis; Nikolaos Milas; Nikolaos Athinaios. 2018. "Towards Machine Shop 4.0: A General Machine Model for CNC machine-tools through OPC-UA." Procedia CIRP 78, no. : 301-306.
The ever-increasing demands for timely product deliveries in structural steel manufacturing, require the evolution of traditional production practices. This can be achieved through the Internet of Things, Cyber-physical Systems, and other emerging technologies. Nevertheless, changes in the workflow of the production, due to the use of modern technologies, can be disruptive and cause complications. Towards this evolution of the industry, this paper proposes a method for monitoring the production in structural steel manufacturing considering Internet of Things and analyzing the data aiming to calculate product assembly complexity and reuse data to retrieve similar past orders. The main architecture, the software design, as well as the Internet of Things based monitoring system is presented following the main requirements of the industrial case study.
Dimitris Mourtzis; Nikolaos Milas; Katerina Vlachou; Ioannis Liaromatis. Digital transformation of structural steel manufacturing enabled by IoT-based monitoring and knowledge reuse. 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT) 2018, 295 -301.
AMA StyleDimitris Mourtzis, Nikolaos Milas, Katerina Vlachou, Ioannis Liaromatis. Digital transformation of structural steel manufacturing enabled by IoT-based monitoring and knowledge reuse. 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT). 2018; ():295-301.
Chicago/Turabian StyleDimitris Mourtzis; Nikolaos Milas; Katerina Vlachou; Ioannis Liaromatis. 2018. "Digital transformation of structural steel manufacturing enabled by IoT-based monitoring and knowledge reuse." 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT) , no. : 295-301.
With the advent of the fourth industrial revolution (Industry 4.0), manufacturing systems are transformed into digital ecosystems. In this transformation, the internet of things (IoT) and other emerging technologies pose a major role. To shift manufacturing companies toward IoT, smart sensor systems are required to connect their resources into the digital world. To address this issue, the proposed work presents a monitoring system for shop-floor control following the IoT paradigm. The proposed monitoring system consists of a data acquisition device (DAQ) capable of capturing quickly and efficiently the data from the machine tools, and transmits these data to a cloud gateway via a wireless sensor topology. The monitored data are transferred to a cloud server for further processing and visualization. The data transmission is performed in two levels, i.e., locally in the shop-floor using a star wireless sensor network (WSN) topology with a microcomputer gateway and from the microcomputer to Cloud using Internet protocols. The developed system follows the loT paradigm in terms of connecting the physical with the cyber world and offering integration capabilities with existing industrial systems. In addition, the open platform communication—unified architecture (OPC-UA) standard is employed to support the connectivity of the proposed monitoring system with other IT tools in an enterprise. The proposed monitoring system is validated in a laboratory as well as in machining and mold-making small and medium-sized enterprises (SMEs).
Dimitris Mourtzis; Nikolaos Milas; Aikaterini Vlachou. An Internet of Things-Based Monitoring System for Shop-Floor Control. Journal of Computing and Information Science in Engineering 2018, 18, 021005 .
AMA StyleDimitris Mourtzis, Nikolaos Milas, Aikaterini Vlachou. An Internet of Things-Based Monitoring System for Shop-Floor Control. Journal of Computing and Information Science in Engineering. 2018; 18 (2):021005.
Chicago/Turabian StyleDimitris Mourtzis; Nikolaos Milas; Aikaterini Vlachou. 2018. "An Internet of Things-Based Monitoring System for Shop-Floor Control." Journal of Computing and Information Science in Engineering 18, no. 2: 021005.
Following the rapid advances in the technologies for green transportations, this paper investigates the operation of an electric city car during steering conditions. The conventional drivetrain is improved using an electronic differential to select the appropriate torque for the driving wheels. The results of the Ackermann geometry and the effects due to the relocation of the center of gravity are examined. For this reason, a microelectronic framework that consists of five nodes for monitoring and controlling the drivetrain based on sensor measurements and CAN communications is introduced. Experimental tests are performed and discussed.
Nikolaos Milas; Emmanuel C. Tatakis; Epaminondas D. Mitronikas. Investigation of the operation of an electric city car equipped with electronic differential using CAN-enabled monitoring. 2017 Panhellenic Conference on Electronics and Telecommunications (PACET) 2017, 1 -4.
AMA StyleNikolaos Milas, Emmanuel C. Tatakis, Epaminondas D. Mitronikas. Investigation of the operation of an electric city car equipped with electronic differential using CAN-enabled monitoring. 2017 Panhellenic Conference on Electronics and Telecommunications (PACET). 2017; ():1-4.
Chicago/Turabian StyleNikolaos Milas; Emmanuel C. Tatakis; Epaminondas D. Mitronikas. 2017. "Investigation of the operation of an electric city car equipped with electronic differential using CAN-enabled monitoring." 2017 Panhellenic Conference on Electronics and Telecommunications (PACET) , no. : 1-4.
The ever-increasing complexity in manufacturing systems caused by the fluctuating customer demands has highly affected the contemporary shop-floors. The selection of the appropriate cutting parameters is becoming more and more challenging due to the increasing complexity of products. Until now, the knowledge of the machine operators concerning the modification of the machining parameters and the monitoring information is not sufficiently exploited by the optimization systems. Web and Cloud technologies together with wireless sensor networks are required to capture the shop-floor data and enable the ubiquitous access from multiple IT tools. For addressing these challenges, this research work proposes a Cloud-based, knowledge-enriched framework for machining efficiency based on machine tool monitoring. More precisely, it focuses on the optimization of the machining parameters and moves through an event-driven optimization algorithm, utilizing the existing machining knowledge captured by the monitoring system. Based on the features of a new part, a similarity mechanism retrieves the cutting parameters of successfully executed past parts that have been machined. Afterwards, the optimization module, using event-driven function blocks, adapts these parameters to efficiently optimize the moves and the cutting parameters. The monitoring system uses a wireless sensor network and a human operator input via mobile devices. A case study from the mould-making industry is used for validating the proposed framework.
Dimitris Mourtzis; Ekaterini Vlachou; Nikolaos Milas; Nikolaos Tapoglou; Jorn Mehnen. A cloud-based, knowledge-enriched framework for increasing machining efficiency based on machine tool monitoring. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2017, 233, 278 -292.
AMA StyleDimitris Mourtzis, Ekaterini Vlachou, Nikolaos Milas, Nikolaos Tapoglou, Jorn Mehnen. A cloud-based, knowledge-enriched framework for increasing machining efficiency based on machine tool monitoring. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2017; 233 (1):278-292.
Chicago/Turabian StyleDimitris Mourtzis; Ekaterini Vlachou; Nikolaos Milas; Nikolaos Tapoglou; Jorn Mehnen. 2017. "A cloud-based, knowledge-enriched framework for increasing machining efficiency based on machine tool monitoring." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 1: 278-292.
Dimitris Mourtzis; Ekaterini Vlachou; Nikolaos Milas; George Dimitrakopoulos. Energy Consumption Estimation for Machining Processes Based on Real-time Shop Floor Monitoring via Wireless Sensor Networks. Procedia CIRP 2016, 57, 637 -642.
AMA StyleDimitris Mourtzis, Ekaterini Vlachou, Nikolaos Milas, George Dimitrakopoulos. Energy Consumption Estimation for Machining Processes Based on Real-time Shop Floor Monitoring via Wireless Sensor Networks. Procedia CIRP. 2016; 57 ():637-642.
Chicago/Turabian StyleDimitris Mourtzis; Ekaterini Vlachou; Nikolaos Milas; George Dimitrakopoulos. 2016. "Energy Consumption Estimation for Machining Processes Based on Real-time Shop Floor Monitoring via Wireless Sensor Networks." Procedia CIRP 57, no. : 637-642.
Maintenance and its cost continue, over the years, to draw the attention of production management since the unplanned failures decrease the reliability of the system and also the return of investments. Advanced maintenance techniques that capture and process shop-floor information can reduce costs and increase the sustainability of an enterprise. This paper presents a condition-based preventive maintenance approach integrated into a machine monitoring framework. The latter acquires data from shop-floor machine tools and analyses them through an information fusion technique to support the condition-based preventive maintenance operations. The proposed approach is developed into a software service, deployed on a Cloud environment. The service gathers and processes data, such as their actual processing time and machining time per tool, related to the operation of machine tools and equipment and calculates the expected remaining useful life of components. Moreover, it provides notifications to machine tool operators and maintenance departments, as well as it enables the communication among them using mobile technology. The framework is applied to a case study with data obtained from a machining SME.
Dimitris Mourtzis; Ekaterini Vlachou; Nikolaos Milas; Nikitas Xanthopoulos. A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring. Procedia CIRP 2016, 41, 655 -660.
AMA StyleDimitris Mourtzis, Ekaterini Vlachou, Nikolaos Milas, Nikitas Xanthopoulos. A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring. Procedia CIRP. 2016; 41 ():655-660.
Chicago/Turabian StyleDimitris Mourtzis; Ekaterini Vlachou; Nikolaos Milas; Nikitas Xanthopoulos. 2016. "A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring." Procedia CIRP 41, no. : 655-660.
The way machining operations have been running has changed over the years. Nowadays, machine utilization and availability monitoring are becoming increasingly important for the smooth operation of modern workshops. Moreover, the nature of jobs undertaken by manufacturing small and medium enterprises (SMEs) has shifted from a mass production to small batch. To address the challenges caused by modern fast changing environments, a new cloud-based approach for monitoring the use of manufacturing equipment, dispatching jobs to the selected computer numerical control (CNC) machines, and creating the optimum machining code is presented. In this approach the manufacturing equipment is monitored using a sensor network and though an information fusion technique it derives and broadcasts the data of available tools and machines through the internet to a cloud-based platform. On the manufacturing equipment event driven function blocks with embedded optimization algorithms are responsible for selecting the optimal cutting parameters and generating the moves required for machining the parts while considering the latest information regarding the available machines and cutting tools. A case study based on scenario from a shop floor that undertakes machining jobs is used to demonstrate the developed methods and tools.
Nikolaos Tapoglou; Jörn Mehnen; Aikaterini Vlachou; Michael Doukas; Nikolaos Milas; Dimitris Mourtzis. Cloud-Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real-Time Monitoring. Journal of Manufacturing Science and Engineering 2015, 137, 040909 .
AMA StyleNikolaos Tapoglou, Jörn Mehnen, Aikaterini Vlachou, Michael Doukas, Nikolaos Milas, Dimitris Mourtzis. Cloud-Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real-Time Monitoring. Journal of Manufacturing Science and Engineering. 2015; 137 (4):040909.
Chicago/Turabian StyleNikolaos Tapoglou; Jörn Mehnen; Aikaterini Vlachou; Michael Doukas; Nikolaos Milas; Dimitris Mourtzis. 2015. "Cloud-Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real-Time Monitoring." Journal of Manufacturing Science and Engineering 137, no. 4: 040909.