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Abstract

This book presents results relevant in the manufacturing research field, that are mainly aimed at closing the gap between the academic investigation and the industrial application, in collaboration with manufacturing companies. Several hardware and software prototypes represent the key outcome of the scientific contributions that can be grouped into five main areas, representing different perspectives of the factory domain:1) Evolutionary and reconfigurable factories to cope with dynamic production contexts characterized by evolving demand and technologies, products and processes. 2) Factories for sustainable production, asking for energy efficiency, low environmental impact products and processes, new de-production logics, sustainable logistics. 3) Factories for the People who need new kinds of interactions between production processes, machines, and human beings to offer a more comfortable and stimulating working environment. 4) Factories for customized products that will be more and more tailored to the final user’s needs and sold at cost-effective prices. 5) High performance factories to yield the due production while minimizing the inefficiencies caused by failures, management problems, maintenance. This books is primarily targeted to academic researchers and industrial practitioners in the manufacturing domain.
Tullio Tolio Giacomo Copani
Walter Terkaj
Editors
Factories of the Future
The Italian Flagship Initiative
Editors
Tullio Tolio
Director of the Italian Flagship Project
Factories of the Future, Direttore del
Progetto Bandiera La Fabbrica del Futuro
CNR - National Research Council of Italy
Rome, Italy
and
Dipartimento di Meccanica
Politecnico di Milano
Milan, Italy
Giacomo Copani
CNR-STIIMA, Istituto di Sistemi e
Tecnologie Industriali Intelligenti per il
Manifatturiero Avanzato
Milan, Italy
Walter Terkaj
CNR-STIIMA, Istituto di Sistemi e
Tecnologie Industriali Intelligenti per il
Manifatturiero Avanzato
Milan, Italy
ISBN 978-3-319-94357-2 ISBN 978-3-319-94358-9 (eBook)
https://doi.org/10.1007/978-3-319-94358-9
Library of Congress Control Number: 2018960237
©The Editor(s) (if applicable) and The Author(s) 2019. This book is an open access publication.
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Preface
Manufacturing plays a key role both in advanced economies and developing
countries because of the large contribution to the overall employment, value added,
gross domestic product (GDP) and social welfare. Manufacturing is also a pillar for
the tertiary sector, since manufacturing activities generate the need for services and
in turn manufacturing provides products and technologies for the operation of the
service sector. Furthermore, manufacturing is fundamental to guarantee national
independence and security and to design the future of our societies. Continuously
evolving grand challenges compel the manufacturing sector to innovate its pro-
cesses, technologies and business models to continue sustaining the national
economies and progress.
This book presents the philosophy and the ndings of the Italian Flagship
Project Factories of the Future (La fabbrica del futuro 20122018). This agship
project was a major national research program promoted by the Italian Ministry of
University, Innovation and Research (MIUR) and coordinated by the National
Research Council of Italy (CNR) to innovate the manufacturing sector and address
global challenges. Starting from an analysis of research policies, Chap. 1outlines
the main ongoing research programs and initiatives both at international and Italian
level. Among these initiatives, the Italian Flagship Project Factories of the Future is
presented in details. The roadmap for research and innovation implemented by the
agship project is based on ve research priorities that can be seen as different
views of the same factory of the future: Evolutionary and Recongurable Factory,
Sustainable Factory,Factory for the People,Factory for Customised and
Personalised Products,Advanced-Performance Factory.
On the basis of the ve research priorities, the agship project funded 18
research projects and 14 demonstrators. The ndings of the specic scientic and
technological research projects and demonstrators are reported in Chaps. 219.
Chapter 20 proposes seven future missions resulting from the agship project
that can be set ahead for the manufacturing industry. Missions are of vital impor-
tance to guarantee the evolution of our societies by means of new systemic solu-
tions. Missions will also foster the important role of manufacturing as a backbone
for the employment and wealth of our national and European economies. Indeed
v
missions such as Circular Economy,Rapid and Sustainable Industrialisation,
Robotic Assistant,Factories for Personalised Medicine,Internet of Actions,
Factories close to the People, and Turning Ideas into Products will have a relevant
societal impact and at the same time will require to address signicant scientic and
technological challenges which will be particularly important in view of the next
strategic initiatives at national and European level, including Horizon Europe.
Moreover, the demonstration and exploitation of results related to missions require
proper research infrastructures. Therefore, Chap. 21 analyzes and gives examples of
different types of pilot plant together with a discussion about funding mechanisms
needed to support industrial research and make pilot plants sustainable.
Milan, Italy Tullio Tolio
Giacomo Copani
Walter Terkaj
vi Preface
Acknowledgements
The Director of the Italian Flagship Project Factories of the Future (La fabbrica del
futuro) gratefully thanks Prof. Francesco Jovane for his visionary approach to
manufacturing research that triggered the launch of the agship project Factories
of the Future in the context of the National Research Plan (PNR 20112013). Many
thanks also to Prof. Quirico Semeraro and Prof. Vincenzo Nicolòfor the scientic
supervision and guidance of the activities of the two main streams of the agship
project. A special appreciation goes to Dott.ssa Federica Rossi, Vice-director of
the agship project. Finally, warm thanks to the present and past members of the
Implementation Support Group (ISG) of the Flagship Project Factories of the
Future: Walter Terkaj, Giacomo Copani, Eleonora Schiariti, Emanuela Aleri,
Daniele Dalmiglio, Davide Ceresa, and Anna Valente.
vii
Contents
Part I Introduction
1 The Italian Flagship Project: Factories of the Future ........... 3
Walter Terkaj and Tullio Tolio
Part II Evolutionary and Recongurable Factory
2 Model Predictive Control Tools for Evolutionary Plants ......... 39
Andrea Cataldo, Ivan Cibrario Bertolotti and Riccardo Scattolini
3 Exploiting Modular Pallet Flexibility for Product and Process
Co-evolution Through Zero-Point Clamping Systems ........... 57
Marcello Urgo, Walter Terkaj, Franca Giannini, Stefania Pellegrinelli
and Stefano Borgo
4 Knowledge Based Modules for Adaptive Distributed
Control Systems ....................................... 83
Andrea Ballarino, Alessandro Brusaferri, Amedeo Cesta,
Guido Chizzoli, Ivan Cibrario Bertolotti, Luca Durante,
Andrea Orlandini, Riccardo Rasconi, Stefano Spinelli
and Adriano Valenzano
5 Highly Evolvable E-waste Recycling Technologies
and Systems .......................................... 109
Giacomo Copani, Nicoletta Picone, Marcello Colledani,
Monica Pepe and Alessandro Tasora
Part III Sustainable Factory
6 Innovative and Sustainable Production of Biopolymers ......... 131
Simona Ortelli, Anna Luisa Costa, Cristian Torri, Chiara Samorì,
Paola Galletti, Claudia Vineis, Alessio Varesano, Luca Bonura
and Giacomo Bianchi
ix
7 Integrated Technological Solutions for Zero Waste Recycling
of Printed Circuit Boards (PCBs) .......................... 149
Giacomo Copani, Marcello Colledani, Alessandro Brusaferri,
Antonio Pievatolo, Eugenio Amendola, Maurizio Avella
and Monica Fabrizio
Part IV Factory for the People
8 Systemic Approach for the Denition of a Safer Human-Robot
Interaction ........................................... 173
Alessandro Pecora, Luca Maiolo, Antonio Minotti,
Massimiliano Ruggeri, Luca Dariz, Matteo Giussani,
NiccolòIannacci, Loris Roveda, Nicola Pedrocchi
and Federico Vicentini
9 Haptic Teleoperation of UAV Equipped with Gamma-Ray
Spectrometer for Detection and Identication of Radio-Active
Materials in Industrial Plants ............................. 197
Jacopo Aleotti, Giorgio Micconi, Stefano Caselli, Giacomo Benassi,
Nicola Zambelli, Manuele Bettelli, Davide Calestani
and Andrea Zappettini
Part V Factory for Customised and Personalised Products
10 Proposing a Tool for Supply Chain Conguration:
An Application to Customised Production ................... 217
Laura Macchion, Irene Marchiori, Andrea Vinelli
and Rosanna Fornasiero
11 Hospital Factory for Manufacturing Customised, Patient-Specic
3D Anatomo-Functional Models and Prostheses ............... 233
Ettore Lanzarone, Stefania Marconi, Michele Conti,
Ferdinando Auricchio, Irene Fassi, Francesco Modica,
Claudia Pagano and Golboo Pourabdollahian
12 Polymer Nanostructuring by Two-Photon Absorption .......... 255
Tommaso Zandrini, Raffaella Suriano, Carmela De Marco,
Roberto Osellame, Stefano Turri and Francesca Bragheri
13 Use of Nanostructured Coating to Improve Heat Exchanger
Efciency ............................................. 275
Antonino Bonanno, Mariarosa Raimondo and Michele Pinelli
x Contents
Part VI Advanced-Performance Factory
14 Surface Nano-structured Coating for Improved Performance
of Axial Piston Pumps ................................... 295
Antonino Bonanno, Mariarosa Raimondo and Stefano Zapperi
15 Monitoring Systems of an Electrospinning Plant
for the Production of Composite Nanobers .................. 315
Luca Bonura, Giacomo Bianchi, Diego Omar Sanchez Ramirez,
Riccardo Andrea Carletto, Alessio Varesano, Claudia Vineis,
Cinzia Tonetti, Giorgio Mazzuchetti, Ettore Lanzarone,
Simona Ortelli, Anna Luisa Costa and Magda Blosi
16 Plastic Lab-on-Chip for the Optical Manipulation
of Single Cells ......................................... 339
Rebeca Martínez Vázquez, Gianluca Trotta, Annalisa Volpe,
Melania Paturzo, Francesco Modica, Vittorio Bianco, Sara Coppola,
Antonio Ancona, Pietro Ferraro, Irene Fassi and Roberto Osellame
17 CIGS-Based Flexible Solar Cells ........................... 365
Edmondo Gilioli, Cristiano Albonetti, Francesco Bissoli,
Matteo Bronzoni, Pasquale Ciccarelli, Stefano Rampino
and Roberto Verucchi
18 Mechano-Chemistry of Rock Materials for the Industrial
Production of New Geopolymeric Cements ................... 383
Piero Ciccioli, Donatella Capitani, Sabrina Gualtieri, Elena Soragni,
Girolamo Belardi, Paolo Plescia and Giorgio Contini
19 Silk Fibroin Based Technology for Industrial
Biomanufacturing ...................................... 409
Valentina Benfenati, Stefano Toffanin, Camilla Chieco,
Anna Sagnella, Nicola Di Virgilio, Tamara Posati, Greta Varchi,
Marco Natali, Giampiero Ruani, Michele Muccini, Federica Rossi
and Roberto Zamboni
Part VII Conclusions
20 Key Research Priorities for Factories of the FuturePart I:
Missions ............................................. 433
Tullio Tolio, Giacomo Copani and Walter Terkaj
21 Key Research Priorities for Factories of the FuturePart II:
Pilot Plants and Funding Mechanisms ...................... 475
Tullio Tolio, Giacomo Copani and Walter Terkaj
Contents xi
... Model predictive controller (MPC) is a type of controller extensively used in the industry that can be used on linear and non-linear systems [21]. The use of MPC is expected to decrease the value of error [22]. The MPC controller has some drawbacks, especially when dealing with adaptive, constrained and/or multivariable processes [23]. ...
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Formaldehyde is chemical substance that is used in adhesive industry. PT X is formaldehyde producer in East Java which is using proportional-integral control system. This conventional controller has several weaknesses. Multivariable model predictive control (MMPC) is used to increase the performance of control system at PT X. Empirical model is made with process reaction curve followed by first order plus dead time calculation. Four manipulated variables and four controlled variables will construct 16 empirical models. Calculation of MMPC parameter, which include sample time (T), prediction horizon (P), and control horizon (M), is done with Shridhar and Cooper method (1998) and optimized by fine-tuning method. Performance of MMPC is tested by set point (SP) tracking and disturbance rejection. Four controllers tested are evaporator pressure control (PIC-101), liquid percent level control (LIC-101), steam flow control (FIC-102), and air temperature control (TIC-101). The optimized parameter of MMPC which include T, P, and M are 3, 62, and 2 respectively. Multivariable model predictive control can increase control performance in SP tracking with average number of 33.24% for IAE and 42.93% of ISE. Meanwhile, in disturbance rejection, there is an increase in average of 33.485 for IAE and 58.08% for ISE.
... During the 1990s, VF was described in various ways, including emulation facility, virtual organisation and integrated simulations [13,14]. Since then, the VF concept was considered for various purposes such as simulation and optimisation [54], system design and modelling [55], production line control [56], sustainability and reconfigurability of factories [57]. Therefore, the concept conveyed its prominence until the present day. ...
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Manufacturing organisations must compete with each other while adapting to the ever-changing conditions by building and strengthening their chains of competencies to survive. Therefore, companies are challenged to reform and reconstruct their product, process and system models as well as to define new goals conforming to evolving complex and dynamic environments. Recent advancements in technologies such as modelling and simulation (M&S), digital twin (DT), and virtual reality (VR) promise new ways for remodelling organisations’ resources, processes and architectures. Moreover, comprehensive concepts like DT based virtual factory (VF) exploit the potential for utilising such technological concepts in the application domain by enabling the integration of various tools, methods and processes. There are a variety of empirical studies focusing either on the distinct use of technologies, methods and processes or very generic concepts and approaches. However, studies focusing on both conceptual and practical aspects for such comprehensive and integrated solutions to handle co-evolution in the complex manufacturing domain are limited for defining, designing, and utilising novel technologies. In this paper, therefore, we attempt to close this gap by (1) framing and discussing the conceptual and theoretical foundations of DT based VF, (2) introducing and discussing the extension of the DT based VF to virtual enterprise and, (3) generalising and interpreting the prescriptive knowledge discovered during the previous VF demonstrations performed at Vestas Wind Systems A/S. Systems and complexity theories, concepts of business cycles, and competence-based strategic management are discussed to frame descriptive knowledge as a language for depicting the internal and external nature of complex manufacturing enterprise operations. Furthermore, design principles of the DT based VF concept are examined based on framed concepts and theories as well as its potential implications and deviations into different application contexts to provide managerial guidelines for utilising such a concept.
... Together with industrial partners, researchers mainly from the economic sciences and engineering sciences are developing prototypes of such future smart factories (Schuh, G. et al. 2017;Tolio, T. et al. 2019). Smart factories allow individual customer requirements to be met and mean that even individual items can be manufactured profitably. ...
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This article demonstrates the improvement of control performance in formaldehyde production process using model predictive control (MPC) in comparison between conventional proportional-integral control. MPC is an advance process control which can improve the performance of a control process in terms of time delay, open loop instability, constraints, and thereof combinations. MPC will reduce the variance in the control variable that affects the process to operate closer to physical constraints. The empirical model of the MPC controller is based on the process reaction curve (PRC) by using the first order plus dead time (FOPDT) approach. Four controllers which were flow control (FIC-102), temperature control (TIC-101), pressurce control (PIC-101), and liquid level control (LIC-101) were tested by changing the set points (SP) and giving disturbances. The performance indicator for the controllers are shown by their value of integral of absolute error (IAE) and integral of square error (ISE). The results show that the MPC improved the controllers’ performance either tested by changing SP or giving disturbance and are better in terms of IAE or ISE.
Chapter
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Chapter
Full-text available
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