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Factories of the Future - The Italian Flagship Initiative



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
Factories of the Future
The Italian Flagship Initiative
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
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)
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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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
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
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.
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
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
... Zukünftige Fabriken können die Flexibilität, Agilität und Wettbewerbsfähigkeit erhöhen, indem sie die Rolle der Arbeitnehmer:innen stärken, die ihre Fähigkeiten und Kompetenzen kontinuierlich weiterentwickeln. Neue Technologien tragen dazu bei, Fertigkeiten auf neue Generationen von Arbeitnehmer:innen zu übertragen, während leistungsgewandelte Arbeitnehmer:innen mit besseren Informationen unterstützt werden (Tolio et al., 2019). Assistenzsysteme in der Produktion helfen Menschen bei der Ausführung ihrer Tätigkeiten, ohne sie zu substituieren. ...
Individualized products and services enable more satisfied customers, higher profit margins, and competitive advantages that are difficult to copy. This development brings challenges for production and especially for human labor. The individualization of products reduces the product volume per variant up to production in batch size 1. Demographic change and different educational backgrounds lead to a heterogeneous workforce and higher fluctuation. Industrial assistance systems help to meet these challenges while maintaining a human-centered and ergonomic production workplace. Some assistance systems have already found their way into production and are, in some cases, indispensable. However, selecting the right assistance system is complex, given the high number of assistance systems on the market. Previous evaluations have been concerned with assessing a particular system without comparing it to other assistance systems. For example, the difficulty lies in comparing a cognitive assistance system with a physical assistance system, such as comparing an augmented reality application and a cobot. This dissertation aims to develop a multi-criteria evaluation model for assessing industrial assistance systems and comparability of industrial assistance systems for industrial application. The evaluation model is developed, tested, and improved in several cycles based on the Design Science Research Methodology. The result of the work is a multi-criteria evaluation model consisting of five dimensions (1) cost, (2) process, (3) learning and developing, (4) user, and (5) technology, which are further subdivided into ten criteria. The Analytical Hierarchy Process (AHP) is used to determine a process- and company-specific weighting of the criteria. A case study in the TU Vienna pilot factory and in a company with 300 run-throughs demonstrates the applicability of the evaluation model to a process that is supported with cognitive and physical assistance systems. The results are compared in a spider web diagram, from which a recommendation for the best variant corresponding to the weighted objectives is derived.
... In recent years, there has been a growing interest in transforming undergraduate engineering education towards a more active approach and according to the needs of the industry [6]. Complex market demands combined with the technological evolution of products and processes lead to the need for engineers who are capable of reacting and evolving, taking advantage of the flexibility, capacity to change and scalability to remain competitive in dynamic production contexts [7]. ...
... 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]. ...
Conference Paper
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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.
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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.
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Uranium mining and processing had been widespread in Central Asia since the mid-1940s. However, with the establishment of the newly independent states in the 1990s, many of the former uranium mining and processing facilities and their associated wastes (dumps and tailings) were abandoned and have since posed a threat to the environment. The fact that the sites were left behind without proper remediation for a long time has led to the uncontrolled spread of radioactive and toxic contaminants in the environment due to landslides or flooding. Knowledge of the exact location of some waste facilities was lost as a result of social disruptions during the 1990s. In order to assess radiological risks and plan and implement adequate, sustainable, and environmental remediation measures, the radiological situation at the uranium legacy sites must be repeatedly mapped with the best possible accuracy in terms of both sensitivity and spatial resolution. In this paper, we present the experimental use of an unmanned aerial vehicle (UAV) equipped with gamma spectrometry systems as a novel tool for mapping, assessing, and monitoring radioactivity at sites affected by uranium mining and processing and other activities related to enhanced natural radioactivity. Special emphasis is put on the practical conditions of using UAV-based gamma spectrometry in an international context focusing on low- and medium-income countries. Challenges and opportunities of this technology are discussed, and its reliability and robustness under field conditions are critically reviewed. The most promising future application of the technology appears to be the radiological monitoring, institutional control, and quality assurance of legacy sites during and after environmental remediation. One-off administrative and logistical challenges of the technology are outweighed by the significant amount of time and cost saved once a UAV-based gamma spectrometry survey system is set up.
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Silk fibroin (SF) is a natural protein (biopolymer) extracted from the cocoons of Bombyx mori L. (silkworm). It has many properties of interest in the field of biotechnology, the most important being biodegradability, biocompatibility and robust mechanical strength with high tensile strength. SF is usually dissolved in water-based solvents and can be easily reconstructed into a variety of material formats, including films, mats, hydrogels, and sponges, by various fabrication techniques (spin coating, electrospinning, freeze-drying, and physical or chemical crosslinking). Furthermore, SF is a feasible material used in many biomedical applications, including tissue engineering (3D scaffolds, wounds dressing), cancer therapy (mimicking the tumor microenvironment), controlled drug delivery (SF-based complexes), and bone, eye and skin regeneration. In this review, we describe the structure, composition, general properties, and structure–properties relationship of SF. In addition, the main methods used for ecological extraction and processing of SF that make it a green material are discussed. Lastly, technological advances in the use of SF-based materials are addressed, especially in healthcare applications such as tissue engineering and cancer therapeutics.
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In theory, the design of modern production systems in the form of a Cyber Physical Production System (CPPS) allows more flexibility, simple expandability, quick adaptability and intelligent production control by the product. Multi-Agent Systems (MASs) are thereby recommended as control solution because of their autonomy and dynamic, decentralized architecture. Although their potential use and technical excellence have been proven, the costs of implementation and maintenance still outweigh their supposed advantages. This results in low acceptance and usage of operational MASs in the industry. This article describes topics that need to be considered when designing and implementing an MAS as production control for a CPPS in the context of customized massproduction. Generally developed approaches are presented, which were implemented in a commercially developed agent framework and validated on the basis of a industrial use case for a product-led filling process in lot size one. All implemented concepts aim to be reusable in comparable applications across industries. In combination with the MAS-internal testing approach also presented, this should contribute to faster, more cost-effective implementation of reliable MAS solutions and ultimately increase their technical maturity.
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In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types.
Conference Paper
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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.
Durch den Einsatz künstlicher Intelligenz können umfassende Wettbewerbsvorteile erlangt werden. Dieser Beitrag beleuchtet daher Potenziale und Ausgestaltungsmöglichkeiten KI-basierter Geschäftsmodelle im Verarbeitenden den Gewerbe. Basierend auf einer quantitativen Betriebsbefragung wird zunächst der Umsetzungsstand produktbegleitender Dienstleistungen und hybrider Geschäftsmodelle sowie der Digitalisierung von Geschäftsmodellen im Verarbeitenden Gewerbe dargestellt. Es werden mögliche KI-Anwendungen für Geschäftsmodelle im Verarbeitenden Gewerbe beschrieben, bevor Ausgestaltungsmöglichkeiten KI-basierter Geschäftsmodelle mittels eines morphologischen Kastens aufgezeigt werden. Abschließend werden beispielhaft zwei Anwendungsfälle KI-basierter Geschäftsmodelle skizziert.
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