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In recent decades, lots of work has been done to mitigate self excited vibration effects in milling operations. Still, a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rate (MRR). In the present work, experimentally acquired acoustic signals in milling operation have been computed using a modified Local Mean Decomposition (SBLMD) technique in order to cite tool chatter features. Further, three artificial neural network (ANN) training algorithms viz. Resilient Propagation (RP), Conjugate Gradient-Based (CGP) and Levenberg-Marquardt Algorithm (LM) and two activation functions viz. Hyperbolic Tangent Sigmoid (TANSIG) and Log Sigmoid (LOGSIG) has been used to train the acquired chatter vibration and metal removal rate data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms and activation functions, a suitable one has been selected and further invoked to develop prediction models of chatter severity and metal removal rate. Finally, Multi-Objective Particle Swarm Optimization (MOPSO) has been invoked to optimize developed prediction models for obtaining the most favourable range of input parameters pertaining to stable milling with higher productivity.

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Cavitation in fuel injectors occurs in the nozzle region where local pressure drops below the fuel saturation pressure. The pressure drop might simultaneously induce the formation of gas bubbles such as nitrogen dissolved in the fuel, also known as pseudo-cavitation. A new cavitation model has been developed that accounts for the nitrogen bubbles separation from the fuel stream by accounting for the solubility changes of nitrogen with the pressure drop. A multi-fluid model integrated with the volume-of-fluid interface tracking approach has been developed to capture the interface between the liquid fuel, fuel vapor and the de-gassed nitrogen. Differentiating between cavitation and pseudo-cavitation is a very challenging task experimentally. The new model allows distinguishing between the volume fraction occupied by fuel vapor and de-gassed nitrogen. Comparing the predicted void fraction along the nozzle with available experimental data demonstrates that this model significantly improves the predictions of size/location of the cavitation compared with single-fluid mixture models and the existing multi-fluid simulation results. For the standard fuel case, the bulk of bubble formation is correlated with the de-gassing and these bubbles are observed along the nozzle with higher concentration downstream of the flow. However, for the de-gassed fuel case, vapor cavitation bubbles are focused near the walls with higher concentrations near the entrance. The transient behavior of void formation shows that the local pressure at the nozzle entrance near the walls drops to below the saturation pressure initially and forms the vapor bubbles. As the pressure stabilizes at a pressure higher than the saturation pressure, the de-gassing phenomenon takes over vapor cavitation, leading to void formation throughout the nozzle attributed to the non-condensable gases. The sensitivity of the void fraction predictions to model parameters indicates that controlling the ratio of evaporation to condensation rate is essential for accurate prediction of steady state void fraction.