August 1, 2025Open Access

Fine detection of process state and secondary process emissions in laser powder-bed fusion (LPBF) by dual acoustic sensor acquisition and machine learning

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This work demonstrates machine learning techniques predicting process state and emissions using dual acoustic sensors.

Classification accuracy reached ≈ 99% for predicting process state in powder bed fusion using a dual-sensor system with machine learning.
The structure-borne sensor outperforms the airborne sensor in detecting secondary emissions, enhancing monitoring capabilities.
Acoustic monitoring enables cost-effective in-situ emissions detection, leveraging both sensors for improved process stability.
Method employs a fine-tuned neural network, suggesting potential for versatile applications in additive manufacturing processes.

Richter et al. (2025) studied this question.

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