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Harmonizing sound and light: X-ray imaging unveils acoustic signatures of stochastic inter-regime instabilities during laser melting

Hamidi Nasab Milad; Masinelli Giulio; de Formanoir Charlotte; Schlenger Lucas; Van Petegem Steven; Esmaeilzadeh Reza; Wasmer Kilian; Ganvir Ashish; Salminen Antti; Aymanns Florian; Marone Federica; Pandiyan Vigneashwara; Goel Sneha; Logé Roland

Harmonizing sound and light: X-ray imaging unveils acoustic signatures of stochastic inter-regime instabilities during laser melting

Hamidi Nasab Milad
Masinelli Giulio
de Formanoir Charlotte
Schlenger Lucas
Van Petegem Steven
Esmaeilzadeh Reza
Wasmer Kilian
Ganvir Ashish
Salminen Antti
Aymanns Florian
Marone Federica
Pandiyan Vigneashwara
Goel Sneha
Logé Roland
Katso/Avaa
s41467-023-43371-3.pdf (3.555Mb)
Lataukset: 

Nature Research
doi:10.1038/s41467-023-43371-3
URI
https://www.nature.com/articles/s41467-023-43371-3
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025082784856
Tiivistelmä

Laser powder bed fusion (LPBF) is a metal additive manufacturing technique involving complex interplays between vapor, liquid, and solid phases. Despite LPBF’s advantageous capabilities compared to conventional manufacturing methods, the underlying physical phenomena can result in inter-regime instabilities followed by transitions between conduction and keyhole melting regimes — leading to defects. We investigate these issues through operando synchrotron X-ray imaging synchronized with acoustic emission recording, during the remelting processes of LPBF-produced thin walls, monitoring regime changes occurring under constant laser processing parameters. The collected data show an increment in acoustic signal amplitude when switching from conduction to keyhole regime, which we correlate to changes in laser absorptivity. Moreover, a full correlation between X-ray imaging and the acoustic signals permits the design of a simple filtering algorithm to predict the melting regimes. As a result, conduction, stable keyhole, and unstable keyhole regimes are identified with a time resolution of 100 µs, even under rapid transitions, providing a straightforward method to accurately detect undesired processing regimes without the use of artificial intelligence.

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