In-situ acoustic monitoring of direct energy deposition process with deep learning-assisted signal denoising

Abstract

In-situ monitoring is crucial for detecting process anomalies and ensuring part quality in additive manufacturing. Acoustic-based monitoring techniques offer extra benefits such as adjustable sensor setup and lower hardware costs. In the direct energy deposition (DED) process, acoustic signals generated by laser-material interactions carry information about underlying complex physical mechanisms such as melting, solidification, crack propagation, and pore formation. This paper presents a novel acoustic-based in-situ monitoring method for the DED process. The raw acoustic signal is made up of laser-material interaction sound as well as noise from machine movement, inert gas flow, and powder flow. A deep learning model is developed to build an end-to-end signal denoising framework to minimize environmental noise and extract the laser-material interaction sound. Audio equalization, bandpass filtering, and Harmonic-Percussive Source Separation algorithm are used to produce a cleaned laser-material interaction sound as the model's ground truth target. Acoustic data is collected from experiments using different DED machines, materials, and varied process parameters to train the deep learning model. The proposed deep learning-assisted signal denoising strategy lays the groundwork for acoustic-based in-situ defect detection of the DED process.