Digitising the European Industry aims to draw 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 vital factors.

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

The course aims to provide knowledge and skills in digital models of manufacturing systems, considering the presence of human workers. These models support managing and controlling complex manufacturing systems that cope with variability and uncertainty.

The course fits into the program curriculum, pursuing some of the expected learning goals. In particular, the course contributes to the development of the following capabilities:

1. Understand challenges, functions, processes in a business and industrial environment and their mutual effects on business, economy, environment and society.
2. Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) to face problems and opportunities in a business and industrial environment 

The primary expected learning outcomes of the course are achievable through a mix of activities aimed at providing the students with the possibility to learn and experiment with digital tools for manufacturing and use them within realistic industrial problems.

In particular, the course will allow students to achieve knowledge and comprehension to:

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

Students will work on an individual project covering one of the subjects in the syllabus.
Students will have the possibility to share pieces of information and tools if possible and synergic.

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

   • Digital models for representing 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

   • Digital Twin-based approaches for short-term control decisions.

• SDG8 - DECENT WORK AND ECONOMIC GROWTH
• SDG9 - INDUSTRY, INNOVATION AND INFRASTRUCTURE

SDG8 - 20 hours (Support to human workers in manufacturing systems)
SDG9 - 20 hours (Digital Twin and AI-based approaches for industry)

The activities in the course will take advantage of digital tools to support the analysis of manufacturing systems.
To this aim, a basic knowledge of the Python programming language is advised.

A basic knowledge of machine learning applied to computer vision is also advised.
Elective seminars on this topic will be organized.

The evaluation will be based on an individual project work carried out during the whole semester, a discussion of the work done, and a written (quiz) exam covering the theoretical part of the course.