Lasers that learn: The interface of laser machining and machine learning. Issue 5 (9th April 2021)

• Record Type: Journal Article
• Authors: Mills, Benjamin; Grant‐Jacob, James A.
• Is Part Of: IET optoelectronics. Volume 15:Issue 5(2021)
• Page Start: 207
• Page End: 224
• Publication Date: 2021-04-09
• Subjects: Optoelectronics -- Periodicals; 621.36
• DOI: 10.1049/ote2.12039
• Languages: English
• ISSNs: 1751-8768

Abstract

Abstract: Laser machining is a highly flexible non‐contact fabrication method used extensively across academia and industry. Whilst simulations based on fundamental understanding offer some insight into the processes, both the highly non‐linear interactions between laser light and matter and the variety of materials involved mean that theoretical modelling is not particularly applicable to practical experimentation. However, recent breakthroughs in machine learning have resulted in neural networks that are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well beyond those of existing theoretical approaches with applications including 3‐D surface visualisation and real‐time error correction. A perspective at the intersection of laser machining and machine learning is presented, followed by a discussion of future milestones and challenges for this field.