Image-based monitoring of high-precision laser machining via a convolutional neural network (Conference Presentation)

Ben Mills; Daniel J. Heath; James A. Grant-Jacob; Yunhui Xie; Benita S. MacKay; Rob W. Eason

doi:10.1117/12.2507376

4 March 2019 Image-based monitoring of high-precision laser machining via a convolutional neural network (Conference Presentation)

Ben Mills, Daniel J. Heath, James A. Grant-Jacob, Yunhui Xie, Benita S. MacKay, Rob W. Eason

Author Affiliations +

Proceedings Volume 10906, Laser-based Micro- and Nanoprocessing XIII; 109060Z (2019) https://doi.org/10.1117/12.2507376
Event: SPIE LASE, 2019, San Francisco, California, United States

Abstract

Materials processing using femtosecond laser pulses offers the potential for high-precision manufacturing. However, due to the associated nonlinear processes, even small levels of experimental noise (e.g. instability in laser power, or unexpected debris) can result in substantial deviations from the desired machined structures. There is therefore much interest in the development of closed-loop feedback processes. Recent advances in the algorithms behind neural networks, and in particular convolutional neural networks (CNNs) have led to rapid advancements in the field. Here, we will present the first demonstration of the application of a CNN for observing and identifying the experimental parameters exclusively from a camera that observes the sample during laser machining. We will show that the CNN was able to accurately determine the laser fluence, number of pulses and the material used. Although there are many other computational approaches for image-based feedback, this CNN approach has the significant advantage that it works purely as a pattern recognition device, and hence requires minimal human input with regards to the physical processes that underlie the laser machining process. Therefore, this avoids the need for a comprehensive programmatical description of the nonlinear interaction of laser light and material. Training time was one hour, and the time to process and identify the experimental parameters from a single image was approximately 30 milliseconds, hence showing the potential for a CNN to act as the central component of a real-time feedback system for laser machining, and enabling undesired or incorrect machining to be immediately compensated.

Conference Presentation

Citation Download Citation

Ben Mills, Daniel J. Heath, James A. Grant-Jacob, Yunhui Xie, Benita S. MacKay, and Rob W. Eason "Image-based monitoring of high-precision laser machining via a convolutional neural network (Conference Presentation)", Proc. SPIE 10906, Laser-based Micro- and Nanoprocessing XIII, 109060Z (4 March 2019); https://doi.org/10.1117/12.2507376

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KEYWORDS

Convolutional neural networks

Image processing

Cameras

Evolutionary algorithms

Femtosecond phenomena

Light emitting diodes

Manufacturing

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