Acoustic and plasma sensing of laser ablation via deep learning

Abstract

Monitoring laser ablation when using high power lasers can be challenging due to plasma obscuring the view of the machined sample. Whilst the appearance of the generated plasma is correlated with the laser ablation conditions, extracting useful information is extremely difficult due to the highly nonlinear processes involved. Here, we show that deep learning can enable the identification of laser pulse energy and a prediction for the appearance of the ablated sample, directly from camera images of the plasma generated during single-pulse femtosecond ablation of silica. We show that this information can also be identified directly from the acoustic signal recorded during this process. This approach has the potential to enhance real-time feedback and monitoring of laser materials processing in situations where the sample is obscured from direct viewing, and hence could be an invaluable diagnostic for laser-based manufacturing.

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Identifiers

ORCID for James Grant-Jacob: orcid.org/0000-0002-4270-4247

ORCID for Benjamin Mills: orcid.org/0000-0002-1784-1012

ORCID for Michael N. Zervas: orcid.org/0000-0002-0651-4059

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