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Prof. Dario Antonelli
Politecnico di Torino

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Research Keywords & Expertise

0 Cluster Analysis
0 Discrete Event Simulation
0 Reinforcement Learning
0 Finite element analysis and simulation
0 Collaborative robots (Cobots)

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Discrete Event Simulation
Finite element analysis and simulation

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Journal article
Published: 09 June 2021 in Applied Sciences
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The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities’ real time monitoring with fallouts on the maintenance timeliness.

ACS Style

Valentina Villa; Berardo Naticchia; Giulia Bruno; Khurshid Aliev; Paolo Piantanida; Dario Antonelli. IoT Open-Source Architecture for the Maintenance of Building Facilities. Applied Sciences 2021, 11, 5374 .

AMA Style

Valentina Villa, Berardo Naticchia, Giulia Bruno, Khurshid Aliev, Paolo Piantanida, Dario Antonelli. IoT Open-Source Architecture for the Maintenance of Building Facilities. Applied Sciences. 2021; 11 (12):5374.

Chicago/Turabian Style

Valentina Villa; Berardo Naticchia; Giulia Bruno; Khurshid Aliev; Paolo Piantanida; Dario Antonelli. 2021. "IoT Open-Source Architecture for the Maintenance of Building Facilities." Applied Sciences 11, no. 12: 5374.

Journal article
Published: 27 February 2021 in Sustainability
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The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

ACS Style

Mohammed M. Mabkhot; Pedro Ferreira; Antonio Maffei; Primož Podržaj; Maksymilian Mądziel; Dario Antonelli; Michele Lanzetta; Jose Barata; Eleonora Boffa; Miha Finžgar; Łukasz Paśko; Paolo Minetola; Riccardo Chelli; Sanaz Nikghadam-Hojjati; Xi Wang; Paolo Priarone; Francesco Lupi; Paweł Litwin; Dorota Stadnicka; Niels Lohse. Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals. Sustainability 2021, 13, 2560 .

AMA Style

Mohammed M. Mabkhot, Pedro Ferreira, Antonio Maffei, Primož Podržaj, Maksymilian Mądziel, Dario Antonelli, Michele Lanzetta, Jose Barata, Eleonora Boffa, Miha Finžgar, Łukasz Paśko, Paolo Minetola, Riccardo Chelli, Sanaz Nikghadam-Hojjati, Xi Wang, Paolo Priarone, Francesco Lupi, Paweł Litwin, Dorota Stadnicka, Niels Lohse. Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals. Sustainability. 2021; 13 (5):2560.

Chicago/Turabian Style

Mohammed M. Mabkhot; Pedro Ferreira; Antonio Maffei; Primož Podržaj; Maksymilian Mądziel; Dario Antonelli; Michele Lanzetta; Jose Barata; Eleonora Boffa; Miha Finžgar; Łukasz Paśko; Paolo Minetola; Riccardo Chelli; Sanaz Nikghadam-Hojjati; Xi Wang; Paolo Priarone; Francesco Lupi; Paweł Litwin; Dorota Stadnicka; Niels Lohse. 2021. "Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals." Sustainability 13, no. 5: 2560.

Journal article
Published: 10 February 2021 in Applied Sciences
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Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot’s speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system , it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot’s lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator’ joints; to predict cobot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors.

ACS Style

Khurshid Aliev; Dario Antonelli. Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning. Applied Sciences 2021, 11, 1621 .

AMA Style

Khurshid Aliev, Dario Antonelli. Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning. Applied Sciences. 2021; 11 (4):1621.

Chicago/Turabian Style

Khurshid Aliev; Dario Antonelli. 2021. "Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning." Applied Sciences 11, no. 4: 1621.

Articles
Published: 06 March 2020 in Quality Engineering
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The Failure Mode and Effects Analysis (FMEA) is a powerful tool to design and maintain reliable systems (products, services or manufacturing processes), investigating their potential failure modes from the threefold perspective of severity, occurrence and detection. The Process FMEA, or more briefly P-FMEA, is a declination of the FMEA for manufacturing processes (or parts of them). Being progressively characterized by decentralized networks of flexible manufacturing facilities, the current scenario significantly hampers the implementation of the traditional P-FMEA, which requires the joint work of a group of experts formulating collective judgments. This paper revises the traditional P-FMEA approach and integrates it with the ZMII-technique – i.e. a recent aggregation technique based on the combination of the Thurstone’s Law of Comparative Judgment and the Generalized Least Squares method – allowing experts distributed through organizations to formulate their judgments individually. The revised approach – referred to as “distributed-Process FMEA” or more briefly dP-FMEA – allows to manage a number of experts, without requiring them to physically meet and formulate collective decisions, thus overcoming a relevant limitation of the traditional P-FMEA. The dP-FMEA approach also includes a relatively versatile response mode and overcomes several other limitations of the traditional approach, including but not limited to: (i) arbitrary formulation and aggregation of expert judgments, (ii) lack of consideration of the dispersion of these judgments, and (iii) lack of estimation of the uncertainty of results. The description is supported by a real-life application example concerning a plastic injection-molding process.

ACS Style

Domenico A. Maisano; Fiorenzo Franceschini; Dario Antonelli. dP-FMEA: An innovative Failure Mode and Effects Analysis for distributed manufacturing processes. Quality Engineering 2020, 32, 267 -285.

AMA Style

Domenico A. Maisano, Fiorenzo Franceschini, Dario Antonelli. dP-FMEA: An innovative Failure Mode and Effects Analysis for distributed manufacturing processes. Quality Engineering. 2020; 32 (3):267-285.

Chicago/Turabian Style

Domenico A. Maisano; Fiorenzo Franceschini; Dario Antonelli. 2020. "dP-FMEA: An innovative Failure Mode and Effects Analysis for distributed manufacturing processes." Quality Engineering 32, no. 3: 267-285.

Conference paper
Published: 15 August 2019 in Security Education and Critical Infrastructures
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Collaborative Human-Robot workcells introduce robot-assisted operations in small-volume production or assembly processes, where conventional automation is noncompetitive. Unfortunately, the collaborative work of humans and robots sharing the same work area and/or working on the same assembly operation may pose unprecedented problems and failure risks. Failure Mode, Effects and Criticality Analysis (FMECA) is a popular tool to design reliable processes, which investigates the potential failure modes from the perspective of severity, occurrence and detection. The traditional FMECA approach requires the assessment of failure modes to be carried out collectively by a group of experts. Nevertheless, in the field of Human-Robot collaboration, experts are often unlikely to agree in their judgements, due to the almost inexistent historical records. Additionally, the traditional approach is not appropriate for decentralized production/assembly processes. The paper revisits the traditional approach and integrates it with the ZMII-technique – i.e., a recent aggregation technique developed by the authors – which overcomes some limitations, including but not limited to: (i) arbitrary categorization and questionable aggregation of the expert judgments, (ii) disregarding the variability in these judgments, and (iii) disregarding the result uncertainty. The description is supported by a real-life application example.

ACS Style

Domenico Maisano; Dario Antonelli; Fiorenzo Franceschini. Assessment of Failures in Collaborative Human-Robot Assembly Workcells. Security Education and Critical Infrastructures 2019, 562 -571.

AMA Style

Domenico Maisano, Dario Antonelli, Fiorenzo Franceschini. Assessment of Failures in Collaborative Human-Robot Assembly Workcells. Security Education and Critical Infrastructures. 2019; ():562-571.

Chicago/Turabian Style

Domenico Maisano; Dario Antonelli; Fiorenzo Franceschini. 2019. "Assessment of Failures in Collaborative Human-Robot Assembly Workcells." Security Education and Critical Infrastructures , no. : 562-571.

Conference paper
Published: 15 August 2019 in Security Education and Critical Infrastructures
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Measuring performances of collaborative robots in Industry 4.0 applications is an open research area since the emergence of collaborative and mobile robots as a support for semi-automatic manufacturing processes. A compelling management problem is the definition of convenient performance measures on which to assess the new generation of robots, to improve process performances both at the robotic cell design stage and at the production stage. A consequent problem is to gather the required data to measure performances. Data must be obtained automatically and in real time. Different levels of communication protocols have to be harmonized in order to transfer data from robots and other factory machines to the cloud on the internet and eventually to the production control system. A case study allows to demonstrate the operation of data acquisition system for collaborative and mobile robots and the real–time monitoring dashboard. The outcome of the study is the gathering of data at field level, the evaluation of robot performances at machine level in order to execute the real time production control at factory level.

ACS Style

Khurshid Aliev; Dario Antonelli; Ahmed Awouda; Paolo Chiabert. Key Performance Indicators Integrating Collaborative and Mobile Robots in the Factory Networks. Security Education and Critical Infrastructures 2019, 635 -642.

AMA Style

Khurshid Aliev, Dario Antonelli, Ahmed Awouda, Paolo Chiabert. Key Performance Indicators Integrating Collaborative and Mobile Robots in the Factory Networks. Security Education and Critical Infrastructures. 2019; ():635-642.

Chicago/Turabian Style

Khurshid Aliev; Dario Antonelli; Ahmed Awouda; Paolo Chiabert. 2019. "Key Performance Indicators Integrating Collaborative and Mobile Robots in the Factory Networks." Security Education and Critical Infrastructures , no. : 635-642.

Conference paper
Published: 26 April 2019 in Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020)
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The emergence of mobile robots as a flexible upgrade of industrial AGVs and the simultaneous diffusion of collaborative manipulators pose new problems for the organization of work in industrial plants. The new robots address work environments characterized by limited automation and unstructured layouts. Present study is aimed at demonstrating that, using commercially available technologies, it is possible to assure a fruitful collaborative interaction among three main actors of the factory of tomorrow: the human operator, the mobile robot and the manipulator.

ACS Style

Khurshid Aliev; Dario Antonelli. Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2019, 248 -260.

AMA Style

Khurshid Aliev, Dario Antonelli. Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2019; ():248-260.

Chicago/Turabian Style

Khurshid Aliev; Dario Antonelli. 2019. "Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 248-260.

Article
Published: 05 April 2019 in International Journal of Computer Integrated Manufacturing
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Industry 4.0 stresses the importance of considering production automation as an integrated and collaborative teamwork process between human workers and intelligent machines and tools. Collaborative robotics is a recent field of study for industrial automation. Although fenceless robot systems are available, the actual implementation of collaborative schemes for the conduction of assembly jobs should be supported through dedicated procedures and guidelines. These procedures have yet to be found and defined in detail. In this work, the authors claim that it may be possible to approach the problem of collaborative cell design with the methods devised for lean thinking. In the paper, the most common lean strategies are listed and analysed from the viewpoint of setting up a collaborative work cell. The most suitable strategies and tools are then recommended in a methodology that has been proposed to redesign an industrial assembly cell. The methodology has then been adopted in the presented industrial use case which is focused on the steps of HRC design process such as tasks assignment and scheduling.

ACS Style

Dorota Stadnicka; Dario Antonelli. Human-robot collaborative work cell implementation through lean thinking. International Journal of Computer Integrated Manufacturing 2019, 32, 580 -595.

AMA Style

Dorota Stadnicka, Dario Antonelli. Human-robot collaborative work cell implementation through lean thinking. International Journal of Computer Integrated Manufacturing. 2019; 32 (6):580-595.

Chicago/Turabian Style

Dorota Stadnicka; Dario Antonelli. 2019. "Human-robot collaborative work cell implementation through lean thinking." International Journal of Computer Integrated Manufacturing 32, no. 6: 580-595.

Journal article
Published: 13 March 2019 in Procedia CIRP
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Die life estimation in hot forging processes is a compelling challenge, due to the number of factors, mainly wear and plastic deformation induced by thermal effects (tempering). The extent of the heating-cooling cycle and the steady state die temperature are known only after hundredth of work cycles. In the paper a realistic work sequence of repeated forging is simulated by the Finite Elements Method on a symmetrical workpiece geometry, for ease of calculation. Tool wear and tempering-induced deformation are estimated along the complete die life cycle, with the help of Neural Network Regression.

ACS Style

Doriana M. D’Addona; Dario Antonelli. Application of numerical simulation for the estimation of die life after repeated hot forging work cycles. Procedia CIRP 2019, 79, 632 -637.

AMA Style

Doriana M. D’Addona, Dario Antonelli. Application of numerical simulation for the estimation of die life after repeated hot forging work cycles. Procedia CIRP. 2019; 79 ():632-637.

Chicago/Turabian Style

Doriana M. D’Addona; Dario Antonelli. 2019. "Application of numerical simulation for the estimation of die life after repeated hot forging work cycles." Procedia CIRP 79, no. : 632-637.

Journal article
Published: 13 March 2019 in Procedia CIRP
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Objective of Tiphys project is building an Open Networked Platform for the learning of Industry 4.0 themes. The project will create a Virtual Reality (VR) platform, where users will be able to design and create a VR based environment for training and simulating industrial processes but they will be able to study and select among a set of models in order to standardize the learning and physical processes as a virtual representation of the real industrial world and the required interactions so that to acquire learning and training capabilities. The models will be structured in a modular approach to promote the integration in the existing mechanisms as well as for future necessary adaptations. The students will be able to co-create their learning track and the learning contents by collaborative working in a dynamic environment. The paper presents the development and validation of the learning model, built on CONALI learning ontology. The concepts of the ontology will be detailed and the platform functions will be demonstrated on selected use cases.

ACS Style

Dario Antonelli; Doriana M. D’Addona; Antonio Maffei; Vladimir Modrak; Goran Putnik; Dorota Stadnicka; Chrysostomos Stylios. Tiphys: An Open Networked Platform for Higher Education on Industry 4.0. Procedia CIRP 2019, 79, 706 -711.

AMA Style

Dario Antonelli, Doriana M. D’Addona, Antonio Maffei, Vladimir Modrak, Goran Putnik, Dorota Stadnicka, Chrysostomos Stylios. Tiphys: An Open Networked Platform for Higher Education on Industry 4.0. Procedia CIRP. 2019; 79 ():706-711.

Chicago/Turabian Style

Dario Antonelli; Doriana M. D’Addona; Antonio Maffei; Vladimir Modrak; Goran Putnik; Dorota Stadnicka; Chrysostomos Stylios. 2019. "Tiphys: An Open Networked Platform for Higher Education on Industry 4.0." Procedia CIRP 79, no. : 706-711.

Journal article
Published: 13 March 2019 in Procedia CIRP
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People play a central role in intelligent manufacturing systems because of two reasons: their knowledge is indispensable to create and improve intelligent manufacturing systems; and their motivation is very important to identify and solve causes of the problems which may occur in order to prevent them in the future. Therefore, adequate learning methods are required to accomplish these two goals: empower and motivate people. In this paper innovative methods such as learning by doing, simulations and virtual reality will be presented as the ways to transfer the knowledge about intelligent manufacturing systems and to increase motivation concerning their improvements.

ACS Style

Dorota Stadnicka; Pawel Litwin; Dario Antonelli. Human factor in intelligent manufacturing systems - knowledge acquisition and motivation. Procedia CIRP 2019, 79, 718 -723.

AMA Style

Dorota Stadnicka, Pawel Litwin, Dario Antonelli. Human factor in intelligent manufacturing systems - knowledge acquisition and motivation. Procedia CIRP. 2019; 79 ():718-723.

Chicago/Turabian Style

Dorota Stadnicka; Pawel Litwin; Dario Antonelli. 2019. "Human factor in intelligent manufacturing systems - knowledge acquisition and motivation." Procedia CIRP 79, no. : 718-723.

Journal article
Published: 01 January 2019 in FME Transactions
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Industrija budućnosti zasniva se na znanju, kreativnosti i motivaciji ljudi. Iako se broj potrebnih radnika u fabrikama u budućnosti smanjuje, zahtevi vezani za vještine zaposlenih su u porastu. Znanje zaposlenih određuje kvalitet i efikasnost fabričkog sistema. Motivacija ljudi određuje kontinuirano usavršavanje i razvoj koji se ostvaruju identifikacijom i eliminacijom problema. Zbog toga su potrebne adekvatne metode učenja da bi se postigli sledeći ciljevi: dati ljudima odgovornost i motivisati ih. Ovaj rad predstavlja izabrane metode kao što su učenje kroz rad, računarske simulacije i virtualna stvarnost koje podržavaju sticanje znanja onih koji se pripremaju za rad u fabrikama budućnosti. Predstavljene metode takođe povećavaju svest zaposlenih o mogućnostima poboljšanja. industry of the future; human factor; learning by doing; simulations; virtual reality

ACS Style

Dorota Stadnicka; Paweł Litwin; Dario Antonelli. Human factor in industry of the future: Knowledge acquisition and motivation. FME Transactions 2019, 47, 823 -830.

AMA Style

Dorota Stadnicka, Paweł Litwin, Dario Antonelli. Human factor in industry of the future: Knowledge acquisition and motivation. FME Transactions. 2019; 47 (4):823-830.

Chicago/Turabian Style

Dorota Stadnicka; Paweł Litwin; Dario Antonelli. 2019. "Human factor in industry of the future: Knowledge acquisition and motivation." FME Transactions 47, no. 4: 823-830.

Journal article
Published: 01 January 2019 in FME Transactions
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Kolaborativni roboti pripadaju tehnologijama koje čine industriju 4.0. Oni omogućavaju postavljanje poluautomatskih radnih ćelija u kojima roboti i ljudi sarađuju u izvršavanju složenih zadataka, uz nikad pre dostignutu fleksibilnost u poređenju sa standardnim robotskim ćelijama. Ovaj rad se odnosi na neka od brojnih pitanja koja proizilaze iz njihovog uvođenja u fabriku, ne samo kao nove radne ćelije, već i kao nove radne paradigme. Studija razmatra uvođenje kolaborativnih robota u malu proizvodnu radnu ćeliju. Da bi se povećale šanse za uspeh nove ćelije, predlaže se metoda prema kojoj se prvo dodeljuju zadaci ljudima i operaterima robota, na osnovu karakteristika zadatka i sposobnosti operatera, a zatim se vrši dinamička preraspodela zadataka kako bi se prevazišli poremećaji ili kašnjenja na nivou pogona. Razlozi za metodu su da su ispadi česti u malim nestandardizovanim produkcijama, tako da je "of-line" optimizirano dodeljivanje zadataka moglo biti neefikasno. Metoda je testirana u odnosu na industrijsku studiju slučaja i rezultati su prodiskutovani. human-robot collaboration; man-machine system; Industry 4.0; automation; flexible manufacturing system

ACS Style

Dario Antonelli; Giulia Bruno. Dynamic distribution of assembly tasks in a collaborative workcell of humans and robots. FME Transactions 2019, 47, 723 -730.

AMA Style

Dario Antonelli, Giulia Bruno. Dynamic distribution of assembly tasks in a collaborative workcell of humans and robots. FME Transactions. 2019; 47 (4):723-730.

Chicago/Turabian Style

Dario Antonelli; Giulia Bruno. 2019. "Dynamic distribution of assembly tasks in a collaborative workcell of humans and robots." FME Transactions 47, no. 4: 723-730.

Conference paper
Published: 08 December 2018 in Security Education and Critical Infrastructures
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Industry is currently undergoing a digital transformation for strengthening its competitiveness through the convergence between industrial automation and data exchange technologies. This trend exploits a multiplicity of technologies from Cyber Physical Systems and intelligent robotics to PLM and big data management, in order to transform the manufacturing systems in a network of smart and autonomous agents. Even if most of the technologies are already available nowadays, the key obstacle lies in the lack of experience in operating with such technologies. To develop the required skills, different strategies for learning should be adopted. The paper describes the first outcomes of TIPHYS (http://www.tiphys.eu/), an EU funded project for the development of ‘social network based doctoral education on Industry 4.0’. The project organizes the learning material as small didactic elements that are accessed through an ontology-based platform. PLM concepts are applied to allow learners to customize their learning path, to provide them with a dynamic repository, whose content evolves and is enriched by the collaborative contribution of students themselves. The ontology structure is described with the help of selected examples.

ACS Style

Giulia Bruno; Dario Antonelli. Ontology-Based Platform for Sharing Knowledge on Industry 4.0. Security Education and Critical Infrastructures 2018, 377 -385.

AMA Style

Giulia Bruno, Dario Antonelli. Ontology-Based Platform for Sharing Knowledge on Industry 4.0. Security Education and Critical Infrastructures. 2018; ():377-385.

Chicago/Turabian Style

Giulia Bruno; Dario Antonelli. 2018. "Ontology-Based Platform for Sharing Knowledge on Industry 4.0." Security Education and Critical Infrastructures , no. : 377-385.

Journal article
Published: 24 November 2018 in Procedia CIRP
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In the paper two kind of simulation methods, i.e. system dynamics simulation (SDS) and discrete event simulation (DES), are proposed to simulate orders processing in a manufacturing enterprise. SDS model is applied to simulate non stationary (dynamic) behaviour of the system. This dynamic behaviour emerges from lack of automation (process variability) and complex structure of the system (feedback loops). SDS will be used for manual work. In SDS a productivity curve will be incorporated to see the influence of employees productivity on the manufacturing system. Additionally, in the simulation demand influences on number of workers involved in a manual process will be presented. SDS model is combined with DES model which will be used for work realized on the automatic machines. DES models will be used to simulate the operation of the manufacturing system with different scenarios. Proposed approach allows to evaluate overall performance of the manufacturing system for a selected product family.

ACS Style

Dario Antonelli; Paweł Litwin; Dorota Stadnicka. Multiple System Dynamics and Discrete Event Simulation for manufacturing system performance evaluation. Procedia CIRP 2018, 78, 178 -183.

AMA Style

Dario Antonelli, Paweł Litwin, Dorota Stadnicka. Multiple System Dynamics and Discrete Event Simulation for manufacturing system performance evaluation. Procedia CIRP. 2018; 78 ():178-183.

Chicago/Turabian Style

Dario Antonelli; Paweł Litwin; Dorota Stadnicka. 2018. "Multiple System Dynamics and Discrete Event Simulation for manufacturing system performance evaluation." Procedia CIRP 78, no. : 178-183.

Original article
Published: 09 July 2018 in The International Journal of Advanced Manufacturing Technology
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The rise of interest in collaborative robotic cells for assembly or manufacturing has been attested by their inclusion among the enabling technologies of Industry 4.0. In collaborative cells, robots work side by side with human operators allowing to address a larger production scope characterized by medium production volumes and significant product variability. Despite the advances in research and the availability of suitable industrial robot models, several open problems still exist, due to the shift in the way of working: correct assessment of the economic profitability, definition of a suitable process plan, task assignment to humans and robots, intuitive and fast robot programming. This paper addresses the task assignment problem by proposing a method for the classification of tasks starting from the hierarchical decomposition of production activities. Task classification is employed for workload distribution and detailed activity planning. The method relays on the assumption that tasks should be allocated, exploiting the different skills and assets of humans and robots, regardless of workload balancing. The proposed method was firstly tested on a simplified assembly process executed in laboratory, then it has been applied to the redesign of an actual industrial process.

ACS Style

Giulia Bruno; Dario Antonelli. Dynamic task classification and assignment for the management of human-robot collaborative teams in workcells. The International Journal of Advanced Manufacturing Technology 2018, 98, 2415 -2427.

AMA Style

Giulia Bruno, Dario Antonelli. Dynamic task classification and assignment for the management of human-robot collaborative teams in workcells. The International Journal of Advanced Manufacturing Technology. 2018; 98 (9-12):2415-2427.

Chicago/Turabian Style

Giulia Bruno; Dario Antonelli. 2018. "Dynamic task classification and assignment for the management of human-robot collaborative teams in workcells." The International Journal of Advanced Manufacturing Technology 98, no. 9-12: 2415-2427.

Journal article
Published: 01 January 2018 in International Journal of Services and Operations Management
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Rationalising costs while guaranteeing a good quality of service is a challenge that healthcare systems are currently facing. In elective surgery departments, as operations can be scheduled in advance, the goal is usually to maximise the utilisation index of the operating theatre. Nevertheless, the optimisation of a single stage of the process is pointless without an efficient management of the entire routing from income to dismissal. The paper presents discrete events simulation of the actual patient flows in elective surgery exploiting the recovery logs of a hospital department. A UML activity diagram of the surgery process together with the collected hospital data have been used to build a stochastic model of queuing network, identify its parameters and conduct different simulated experiments in order to select the solution that best optimises the performances of the system. The simulation results have shown that there is a large variation in waiting times in correspondence to small variations of the average value of the inter-arrival times. Therefore, solutions that optimise utilisation indexes of both beds and operating theatres should consider a concurrent effort to reduce the variance of admittance processes, otherwise the waiting times will lengthen beyond acceptable limits.

ACS Style

Dario Antonelli; Giulia Bruno; Teresa Taurino. Analysis of patient flows in elective surgery: modelling and optimisation of the hospitalisation process. International Journal of Services and Operations Management 2018, 31, 513 .

AMA Style

Dario Antonelli, Giulia Bruno, Teresa Taurino. Analysis of patient flows in elective surgery: modelling and optimisation of the hospitalisation process. International Journal of Services and Operations Management. 2018; 31 (4):513.

Chicago/Turabian Style

Dario Antonelli; Giulia Bruno; Teresa Taurino. 2018. "Analysis of patient flows in elective surgery: modelling and optimisation of the hospitalisation process." International Journal of Services and Operations Management 31, no. 4: 513.

Journal article
Published: 01 January 2018 in Procedia CIRP
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ACS Style

Dario Antonelli; Dorota Stadnicka. Combining factory simulation with value stream mapping: a critical discussion. Procedia CIRP 2018, 67, 30 -35.

AMA Style

Dario Antonelli, Dorota Stadnicka. Combining factory simulation with value stream mapping: a critical discussion. Procedia CIRP. 2018; 67 ():30-35.

Chicago/Turabian Style

Dario Antonelli; Dorota Stadnicka. 2018. "Combining factory simulation with value stream mapping: a critical discussion." Procedia CIRP 67, no. : 30-35.

Journal article
Published: 01 January 2018 in Procedia CIRP
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ACS Style

Doriana M. D’Addona; Dario Antonelli. Neural Network Multiobjective Optimization of Hot Forging. Procedia CIRP 2018, 67, 498 -503.

AMA Style

Doriana M. D’Addona, Dario Antonelli. Neural Network Multiobjective Optimization of Hot Forging. Procedia CIRP. 2018; 67 ():498-503.

Chicago/Turabian Style

Doriana M. D’Addona; Dario Antonelli. 2018. "Neural Network Multiobjective Optimization of Hot Forging." Procedia CIRP 67, no. : 498-503.

Journal article
Published: 01 January 2018 in International Journal of Services and Operations Management
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International publishers of academic, scientific and professional journals since 1979.

ACS Style

Dario Antonelli; Giulia Bruno; Teresa Taurino. Analysis of patient flows in elective surgery: modelling and optimisation of the hospitalisation process. International Journal of Services and Operations Management 2018, 31, 513 .

AMA Style

Dario Antonelli, Giulia Bruno, Teresa Taurino. Analysis of patient flows in elective surgery: modelling and optimisation of the hospitalisation process. International Journal of Services and Operations Management. 2018; 31 (4):513.

Chicago/Turabian Style

Dario Antonelli; Giulia Bruno; Teresa Taurino. 2018. "Analysis of patient flows in elective surgery: modelling and optimisation of the hospitalisation process." International Journal of Services and Operations Management 31, no. 4: 513.