Digitising the European Industry is aimed at drawing the full benefits from digital technologies in manufacturing. In real manufacturing environments, aligning management and control approaches to the current state of the system and managing the occurrence of unforeseen events are key factors.

The interaction between humans and digital technologies is a valuable tool to master these situations.

The objective of the course is to provide knowledge and skills in digital models of manufacturing systems taking into consideration the presence of human workers. These models support the management and control of complex manufacturing systems coping with variability and uncertainty.

The main expected learning outcomes of the course, achievable through a mix of activities aimed at providing the students the possibility to learn and experiment digital tools for manufacturing and use them within realistic industrial problems. In particular, the course will allow students to achieve knowledge and comprehension in order to:

1. Model a manufacturing environment in terms of its Digital Twin, understand the relevant elements and influencing factors, defining proper modelling hypothesis, collecting and structuring information and data;
2. Select and apply digital tools and technologies to analyse and solve realistic industrial cases;
3. Work and cooperate in a group addressing the complexity of manufacturing problems as well the integration of different digital tools;

1. Digital models for manufacturing systems (Digital Twin).

   • Digital models for the representation of factory objects (resources, processes and products) based on standards and ontologies for manufacturing.

   • Digital Twin models for manufacturing systems.

   • VR/AR models for manufacturing systems.

   • UML Statecharts for the modelling of the control of manufacturing systems.

2. Human modelling and monitoring in operating environments

   • AI-based image analysis approaches for human pose estimation and object recognition.

   • Modelling of manual-executed processes (discrete sequences of operations).

   • Activity recognition for error identification and monitoring of human-executed activities (es. assembly processes).

3. Management and control approaches based on Digital Twin models

   • In-situ evaluation approaches for short-term scheduling decisions.

   • Robust planning approaches applied to proactive-reactive scheduling and sequencing policies.

The activities in the course will take advantage of digital tools to support the analysis of manufacutirng system, to this aim a basic knowledge of the python programming language is advised.

A basic knowledge in machine learning applied to image analysis is also advised. In case, elective seminars will be organized.

1. A project work to be carried out in group (group evaluation).
2. A (written) exam. (to be carried out in a virtual environment).