Andreas Michalowski, University of Stuttgart, Germany
Beat Neuenschwander, Berner Fachhochschule, Switzerland
Machine learning is increasingly proving to be a very powerful approach for supporting a wide variety of technologies. In many cases, the appropriate adaptation of established machine learning methods to new fields of application already represents a major technological advance. In the field of laser material processing, there are numerous interesting approaches to using machine learning in a supportive manner. Applications are, for example, in control and feedback control systems, process monitoring and optimization.
This A&T Topical Review offers the opportunity to discuss current scientific results, demonstrate successful applications and identify further research needs.
Ivan Batalov, Robert Bosch LLC, USA
Hybrid Modeling Design PAtterns - A Modular Approach for Solving Modeling Challenges Combining First Principles and Data
Satoshi Hasegawa, Utsunomiya University, Japan
Laser Processing with Adaptive Optics Based on Convolutional Neural Network
Markus Kogel-Hollacher, Precitec GmbH & Co KG, Germany
Artificial Intelligence in Industrial Laser Material Processing
Benjamin Mills, University of Southampton, UK
Modelling and Optimization of Femtosecond Laser Machining via Deep Learning
Aiko Narazaki, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Data-Driven Ultrashort Pulse Laser Processing Based on Novel In-Process Monitoring and AI High-Speed Optimization
Shuntaro Tani, University of Tokyo, Japan
Neural-Network-Based Ultrashort Laser Ablation Simulator for Micro-Machining