Simulation and optimization of industrial production lines
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Claudio Santo Longo
Claudio Santo Longo is a Ph.D. candidate in Industrial Innovation Engineering at the university of Modena and Reggio Emilia. Received the M.Sc. degree in Management Engineering in 2016, Claudio is very passionate about Big Data and Data analytics topics and he is widely using the techinique of the Hardware in the Loop to accomplish his projects with the best results.
Cesare Fantuzzi received the M.S. degree in Electrical Engineering and Ph.D. degree in System Engineering from the University of Bologna (Italy) in 1990 and 1995, respectively. From 1996 to 2000 Dr Fantuzzi was an Assistant Professor of Automatic Control at University of Ferrara (Italy), from 2000 to 2006 he was an Associate Professor of Automatic Control at University of Modena and Reggio Emilia, Engineering Faculty in Reggio Emilia, and from 2006 to now he is a Full Professor in the same Faculty. He is he director of the graduate program on Mechatronic Engineering at University of Modena and Reggio Emilia.
Abstract
Simulation and optimization techniques are the pillars of for the Virtual Commissioning of modern digital factories. In particular, industrial production lines are complex systems formed by a set of machines that have several cross-dependencies (i. e., the production efficiency of a machine impact deeply performance figures of the others and affects the whole line productivity). The simulation and optimization of production lines is an integrated approach that allows finding efficiently optimized production parameters. This paper presents an example of application of the simulation to the virtual design and optimization of an industrial production line.
Zusammenfassung
Simulations- und Optimierungstechniken sind Säulen für die virtuelle Inbetriebnahme von modernen digitalen Fabriken. Insbesondere sind industrielle Produktionslinien komplexe Systeme aus einer Reihe von Maschinen, die mehrere Querabhängigkeiten haben (d. h., die Produktionseffizienz von einer Maschine wirkt tief auf die Leistungsdaten der anderen und beeinflusst die gesamte Linienproduktivität). Die Simulation und Optimierung von Produktionslinien ist ein integrierter Ansatz, der es erlaubt, effizient Parameter für eine optimierte Produktion zu finden. Dieses Papier zeigt ein Anwendungsbeispiel, das von der Simulation zum virtuellen Design und zur Optimierung einer industriellen Produktionslinie reicht.
Funding source: H2020 European Research Council
Award Identifier / Grant number: 678867
Funding statement: IMPROVE has received funding from the H2020 European Research Council research and innovation programme under grant agreement No. 678867.
About the authors
Claudio Santo Longo is a Ph.D. candidate in Industrial Innovation Engineering at the university of Modena and Reggio Emilia. Received the M.Sc. degree in Management Engineering in 2016, Claudio is very passionate about Big Data and Data analytics topics and he is widely using the techinique of the Hardware in the Loop to accomplish his projects with the best results.
Cesare Fantuzzi received the M.S. degree in Electrical Engineering and Ph.D. degree in System Engineering from the University of Bologna (Italy) in 1990 and 1995, respectively. From 1996 to 2000 Dr Fantuzzi was an Assistant Professor of Automatic Control at University of Ferrara (Italy), from 2000 to 2006 he was an Associate Professor of Automatic Control at University of Modena and Reggio Emilia, Engineering Faculty in Reggio Emilia, and from 2006 to now he is a Full Professor in the same Faculty. He is he director of the graduate program on Mechatronic Engineering at University of Modena and Reggio Emilia.
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© 2018 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Machine Learning in Automation
- Methods
- Interpretable machine learning with reject option
- Combining expert knowledge and unsupervised learning techniques for anomaly detection in aircraft flight data
- Computation of energy efficient driving speeds in conveying systems
- Simulation and optimization of industrial production lines
- Applications
- Online parameter estimation for cyber-physical production systems based on mixed integer nonlinear programming, process mining and black-box optimization techniques
- Assessment of variance & distribution in data for effective use of statistical methods for product quality prediction
Articles in the same Issue
- Frontmatter
- Editorial
- Machine Learning in Automation
- Methods
- Interpretable machine learning with reject option
- Combining expert knowledge and unsupervised learning techniques for anomaly detection in aircraft flight data
- Computation of energy efficient driving speeds in conveying systems
- Simulation and optimization of industrial production lines
- Applications
- Online parameter estimation for cyber-physical production systems based on mixed integer nonlinear programming, process mining and black-box optimization techniques
- Assessment of variance & distribution in data for effective use of statistical methods for product quality prediction
