Results 111 to 120 of about 2,745 (141)

Machine learning-assisted wearable sensing systems for speech recognition and interaction. [PDF]

open access: yesNat Commun
Liu T   +8 more
europepmc   +1 more source
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A model to predict the critical undeformed chip thickness in vibration-assisted machining of brittle materials

International Journal of Machine Tools and Manufacture, 2013
Abstract Vibration-assisted machining (VAM) of brittle materials has been proved to be useful in improving the machining performance by significantly increasing the critical undeformed chip thickness for ductile–brittle transition. However, until now, there does not exist any viable method or model to predict the critical undeformed chip thickness in
Zhang, X.   +4 more
openaire   +3 more sources

Critical undeformed chip thickness of brittle materials in single point diamond turning

The International Journal of Advanced Manufacturing Technology, 2015
In this work, the ultimate compression force that enables the plastic removal of brittle materials in diamond turning is initially modeled according to the theory of rigid-plastic mechanics. And subsequently, an independent oscillator model is reconstructed to calculate the microfriction force that appears at contact interface.
W. J. Zong, Z. M. Cao, C. L. He, T. Sun
openaire   +3 more sources

Researching on the factors influencing the critical undeformed chip thickness of single crystal germanium

10th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Micro- and Nano-Optics, Catenary Optics, and Subwavelength Electromagnetics, 2021
Shanyi Ma   +3 more
openaire   +3 more sources

Multiscale Predictive Modelling of Critical Undeformed Chip Thickness

2021
Predictive modelling of critical undeformed chip thickness (tc) is important for ultra-precision machining of brittle materials in a ductile mode. This paper presents a hierarchical multiscale modelling approach that is capable of quantitatively predicting the variation of tc with crystal orientation. The proposed multiscale modelling approach consists
Xuezhen Wang   +3 more
openaire   +1 more source

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