Materials processing with intelligent systems
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
Learning Outcomes
By the end of the course, the student must be able to:
- Integrate the laser-materials interaction
- Assess / Evaluate various type of sensors depending on the time scale of the process
- Argue on the use of sensors depending on applications
- Integrate Nyquist-Shannon sampling theorem
- Integrate concepts of time, frequency, time-frequency domains
- Recognize the various machine learning methods
- Decide methods appropriate to practical problems
Transversal skills
- Use a work methodology appropriate to the task.
- Set objectives and design an action plan to reach those objectives.
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
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Materials processing with intelligent systems
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Materials processing with intelligent systems
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Type: optional
Reference week
| Mo | Tu | We | Th | Fr | |
| 8-9 | |||||
| 9-10 | |||||
| 10-11 | |||||
| 11-12 | |||||
| 12-13 | |||||
| 13-14 | |||||
| 14-15 | |||||
| 15-16 | |||||
| 16-17 | |||||
| 17-18 | |||||
| 18-19 | |||||
| 19-20 | |||||
| 20-21 | |||||
| 21-22 |
Légendes:
Lecture
Exercise, TP
Project, other