The nucleation and growth of bubbles within a solid matrix is a ubiquitous phenomenon that affects many natural and synthetic processes. However, such a bubbling process is almost “invisible” to common characterization methods because it has an intrinsically multiphased nature and occurs on very short time/length scales. Using in situ transmission electron microscopy to explore the decomposition of a solid precursor that emits gaseous byproducts, the direct observation of a complete nanoscale bubbling process confined in ultrathin 2D flakes is presented here. This result suggests a three‐step pathway for bubble formation in the confined environment: void formation via spinodal decomposition, bubble nucleation from the spherization of voids, and bubble growth by coalescence. Furthermore, the systematic kinetics analysis based on COMSOL simulations shows that bubble growth is actually achieved by developing metastable or unstable necks between neighboring bubbles before coalescing into one. This thorough understanding of the bubbling mechanism in a confined geometry has implications for refining modern nucleation theories and controlling bubble‐related processes in the fabrication of advanced materials (i.e., topological porous materials).
Tool state monitoring is a key technology in intelligent manufacturing. But it is still in a research stage and lacks general adaptability for different machining conditions. To overcome this limitation, this work systematically investigates an intelligent, real-time, and visible tool state monitoring through adopting integrated theories and technologies, i.e., (a) through distinctively designed experimental technique with comprehensive consideration of cutting parameters and tool wear values as variables, (b) through bisensor fusion for simultaneous measurements of low and high frequency signals, (c) through multitheory fusion of wavelet decomposition and the Relief-F algorithm for performing dual feature extraction and feature dimension reduction to achieve more accurate state identification using neural network, and (d) through an innovative programming technique of MATLAB-nested labVIEW. This tool monitoring system has neural network adaptive learning ability with the change of the experimental sample signals which are collected simultaneously by selected vibration and acoustic emission (AE) sensors in all factors turning experiments. Based on LabVIEW and MATLAB hybrid programming, the waveforms of signals are observed and the significant characteristics of signals are extracted through the time-frequency domain analysis and then the calculation of the energy proportion of each frequency band obtained using 4 levels of wavelet packet decompositions of the vibration signal as well as 8 levels of wavelet multiresolution decompositions of the AE signal; the ensuing Relief-F algorithm is used to further reextract the features that are most relevant to the tool state as input of neural network pattern recognition. Through the BP neural network adaptive learning, tool state recognition model is finally established. The results show that the correct recognition rate of BP network model after samples training is 92.59%, which can more accurately and intelligently monitor the tool wear state.
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