Coordinator
Prof. Enzo Pasquale Scilingo
email: enzo.scilingo@unipi.it
Administrative seat:
Joint PhD programme with: University of Siena, University of Florence, and University of Pisa.
Administrative headquarters: Department of Computer Science, University of Pisa.
Project description
The Universities of Florence, Pisa and Siena offer the SMART INDUSTRY PhD programme, focused on the Industry 4.0 (I4.0) paradigm. The programme includes training and research activities across the nine interdisciplinary enabling technologies of I4.0 (Advanced Manufacturing, Additive Manufacturing, Augmented Reality, Simulation, Horizontal/Vertical Integration, Industrial Internet, Cloud Computing, Cybersecurity, Big Data & Analytics), with the aim of training young researchers capable of investigating and testing innovative industrial processes and systems that gain competitiveness through the integration of advanced information processing components and methods.
Building on the cultural foundations of industrial and information engineering, the programme will offer opportunities for cross-fertilisation and research that will bring value both in combination and on their own.
The PhD is jointly offered by the three universities, in synergy with ongoing industrial research initiatives across Tuscany. Strong research collaboration with the industrial sector will be promoted, where the development of the I4.0 paradigm can drive innovation in parts of the production process or entire value chains, as well as enable new products, services, and business models.
The methodology will consist of: a training offer that provides a solid scientific and methodological foundation; individual specialisation in areas with appropriate scientific and research depth; and the integration of these areas around the objectives of I4.0.
Course objectives
The programme aims to train high-level technical-scientific professionals, with expertise in specific technological areas as well as in the methodologies required to integrate technological innovation into industrial processes. In particular, it focuses on the following interdisciplinary enabling technologies:
Advanced Manufacturing Solutions: advanced automation systems and collaborative/cooperative autonomous robots that enhance production line automation and productivity while maintaining flexibility, with reduced investments also suitable for SMEs.
Additive Manufacturing: 3D printing, enabling the redesign of production processes and a reduced time-to-market, through rapid prototyping, new production models (digital/cloud manufacturing), and new product lifecycle support approaches (e.g. digital spare parts management).
Augmented Reality: technologies, devices, and algorithms for AR, VR, and computer vision that enable new forms of human–machine interaction (HMI) for training, assistance, control, and automation.
Simulation: simulation environments for creating digital models of machines and processes (digital twins), allowing performance analysis and optimisation.
Horizontal/Vertical Integration: integration of information flows both vertically—through architectures and systems for automation and control of manufacturing processes (e.g. MES)—and horizontally, across the value chain (e.g. Supply Chain Management).
Industrial Internet: the Internet of Things in industrial contexts, using sensors and connectivity to generate large volumes of data for developing new knowledge and insights.
Cloud Computing: infrastructure virtualisation for managing data and software applications, enabling the integration of data lakes and collaborative platforms among companies within the value chain (business ecosystems).
Cybersecurity: systems, technologies, and algorithms that ensure the secure and protected exchange of sensitive and confidential data.
Big Data & Analytics: advanced solutions and algorithms for analytics and predictive modelling, including cognitive systems, machine learning, artificial intelligence, and deep learning, specifically aimed at industrial production contexts. These tools significantly enhance knowledge acquisition from large datasets through guided or autonomous training processes.
Scientific-disciplinary areas
Agronomy and Field Crops
Automatic Control
Electronic and Computer Bioengineering
Electromagnetic Fields
Electrical Converters, Machines and Drives
Engineering Design and Methods
Electronics
Electrical Engineering
Industrial Mechanical Plants
Computer Science
Management and Industrial Engineering
Fluid Machinery
Agricultural Mechanics
Applied Mechanics
Electrical and Electronic Measurements
Mechanical Design and Machine Construction
Operations Research
Information Processing Systems
Electric Power Systems
Energy and Environmental Systems
Manufacturing Technologies and Systems
Telecommunications
Theory of Chemical Process Development
Internal regulations of the PhD Programme in Smart Industry
Website
smartindustry.unipi.it
