P43: Disturbance Analysis in Manufacturing Systems #6

Aim

The course aims to provide advanced knowledge and hands-on experience in disturbances and variation analysis in manufacturing systems. It covers advanced topics in data-driven analysis of disturbances and variations in manufacturing processes. The course focuses on the application of data-driven methods, including data preprocessing, machine learning and deep learning techniques, and how these methods augment physics-based process models

Prerequisites

There are no prerequisite courses. However, prior knowledge in probability and statistics and use of computational tools facilitates the learning.

Content

The course is divided in the following four modules:

• Module 1: Data Driven Models
• Module 2: Predictive Modeling
• Module 3: Anomaly Detection
• Module 4: Building Digital Twins

Each module consists of sessions composed of lectures and student presentations. There will be five full-day sessions (09:00 – 15:00) scheduled for autumn 2025 as shown below.

Fee for late drop-out

This course is included in the Produktion2030 program, and it is therefore free of charge for PhD student members of Graduate School Produktion2030. However, in case of drop-out later than 3 weeks before course start, a fee of 1 000 SEK per course credit will be charged the student’s home institution.