Coursebooks

Materials processing with intelligent systems

MICRO-457

Lecturer(s) :

Hoffmann Patrik
Wasmer Kilian Thomas

Language:

English

Summary

Repeatability in laser material processing is challenging due to high-speed dynamics. To address this issue, the course provides an overview of laser theory, laser-material interaction, various types of sensors (acoustic & optic), data acquisition, online monitoring, and control via machine learning

Content

The goal of this lecture is to acquaint students with approaches for the in situ and real-time process monitoring and control of highly dynamical processes. Given the generality of the topic, the content is very broad and can be divided into 5 sub-sections.

1) Laser processing. We will provide the basis of laser processing (laser theory, interaction laser-materials, type of laser, laser safety, various process (ablation, polishing, welding, ');

2) Sensors. State-of-the-art sensors (acoustic sensors, including piezo and optical fiber, and optical sensors, including spectroscopic detectors, photodiodes, '). For each type of sensors, we will provide industrial applications, theoretical background, advantages, disadvantages, and limitations.

3) Data acquisition. Information about various ways of acquiring data depending on the use and sensors selected.

Mathematical foundations of signal discretization (sampling): Shannon Theorem and frequency analysis. Data storage and reconstruction without information loss.

4) Signal processing techniques. A short introduction/overview of the latest machine learning methods will be given

(supervised, unsupervised, and reinforcement learning). Classification, clustering, and intelligent controllers.

5) Practical examples of combining (1) to (4) to have an in situ and real-time laser process monitoring and control unit.

Keywords

Laser processing, material processing, sensors, data acquistion, signal processing, machine learning, reinforcement learning.

Learning Prerequisites

Required courses

None, the lecture is self-contained.

Recommended courses

- MICRO-310a: Signals and systems I

- MICRO-311a: Signals and systems II

- MICRO-330: Sensors


Learning Outcomes

By the end of the course, the student must be able to:

Transversal skills

Teaching methods

Oral presentation + discussions, guided exercises and rehearsal

Expected student activities

1) Participate actively in the lecture

2) Carry out all exercises

Assessment methods

Final written exam (85% grade), in-class assessment (15% grade).

Supervision

Office hours No
Assistants No
Forum No

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11   CM013 
11-12    
12-13   CM013 
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German