Microdynamic modeling of acoustic emission in machining

On-line and real-time manufacturing process monitoring and controlling systems are imperative for meeting these drastically growing precision requirements. Recent research has shown that Acoustic Emission (AE) monitoring is effective for early detection of tool wear, and fatigue and other modes of fracture in materials.; The objective of this research is to study AE generation in machining, building on the recent research results pertaining to AE generation from atomistic sources. This research specifically focuses on the annihilation of dislocations in the shear zone of a machining process because annihilations from Frank-Read sources are conjectured to be the major AE sources.; A statistical mechanics modeling is implemented to simulate the generation of AE due to annihilation of random moving dislocations of opposite sign and from Frank-Read sources. A two-dimensional Gaussian density function is chosen to model the likelihood that a dislocation at a particular point annihilates at the specified source location. Each dislocation annihilation event leads to the release of a certain (constant) amount of energy, which is modeled according to the power spectral density function proposed by B. Polyzos et al.; AE signals thus generated are assumed to propagate from the shear zone through a homogeneous workpiece material. A propagation model is developed based on the theory of Ray Acoustics, which can efficiently treat issues pertaining to the propagation medium with finite boundaries and irregular shapes.; The simulation result shows close match of AE characteristics by qualitative as well as quantitative analyses in comparison to the data collected from experiments. The time series plots capturing the random burst nature, power spectral density plots and autocorrelation structures exhibit strong similarity between one another in general. The values of dimensionality for the test of False Nearest Neighbors (FNN) and fractal dimensions as well as those for burst count, count rate and RMS have been examined to be very close to each other. This research opens up the possibility of estimating real-time high frequency AE signals and creates avenues for early detection and control of tool degradation and instability.