Acoustic Emission Sensor-Based In-Process Characterizations for Surface Morphology of Mechanical-Based Machining at the Nanoscale

Abstract Real-time monitoring of nanometric level morphology and structure is essential for the quality inspection and mechanism exploration of the nanofabrication process, which has not been fully studied based on previous research. The acoustic emission (AE) sensor signals are induced with real-time information of the underlying processes, which allows immediate anomaly detection and diagnosis of potential quality issues in manufacturing. However, the micro/nanolevel time domain signal cannot distinguish the various cutting conditions due to the weak signal energy and the high-level noises from the surroundings, which may adversely impact the accuracy and effectiveness of information extraction from monitored sensor signals. This article reports an in situ characterization of the nanoscale surface morphology using AE sensor signals. It links the AE spectral responses to the material removals under different cutting conditions: the AE spectral energy from various cutting conditions shows different patterns highly related to nanofabrication. The selected AE spectral responses can be used to effectively predict the in situ nanomachined surface characteristics while the precision of the process remains under sub-10 nm. Using the significant AE spectral features, the predictive model can obtain an overall accuracy of 82% in R2 value to estimate the achieved surface characteristics. Therefore, the presented AE sensor-based monitoring scheme may open up an avenue to allow real-time characterizations and quality inspection for surface characteristics during machining under the nanoscale.