Steven Y. Liang

TL;DR: In this paper, the authors discuss the evolution of machining process monitoring and control technologies and conduct an in-depth review of the state-of-the-art of these technologies over the past decade.

TL;DR: In this article, the authors investigated defect detection methods for rolling element bearings through sensor signature analysis, specifically the use of a new signal processing combination of the high-frequency resonance technique and adaptive line enhancer.

TL;DR: In this paper, a remaining life adaptation methodology based on mechanistic modeling and parameter tuning is proposed to estimate defect severity by monitoring the signals generated from rotating bearings and an adaptive algorithm is developed to fine tune the parameters involved in the propagation model by comparing predicted and measured defect sizes.

TL;DR: In this paper, a probabilistic chip thickness model was proposed to predict the arithmetic mean surface roughness of ground surfaces using a geometrical analysis of the grooves left on the surface by ideal conic grains.

TL;DR: In this article, a stochastic defect propagation model is established by instituting a lognormal random variable in a deterministic defect propagation rate model, which is calibrated on-line by a recursive least squares (RLS) approach without the requirement of a priori knowledge on bearing characteristics.