An Efficient Third-Order Full-Discretization Method for Prediction of Regenerative Chatter Stability in Milling

The prediction of regenerative chatter stability has long been recognized as an important issue of concern in the field of machining community because it limits metal removal rate below the machine’s capacity and hence reduces the productivity of the machine. Various full-discretization methods have been designed for predicting regenerative chatter stability. The main problem of such methods is that they can predict the regenerative chatter stability but do not efficiently determine stability lobe diagrams (SLDs). Using third-order Newton interpolation and third-order Hermite interpolation techniques, this study proposes a straightforward and effective third-order full-discretization method (called NI-HI-3rdFDM) to predict the regenerative chatter stability in milling operations. Experimental results using simulation show that the proposed NI-HI-3rdFDM can not only efficiently predict the regenerative chatter stability but also accurately identify the SLD. The comparison results also indicate that the proposed NI-HI-3rdFDM is very much more accurate than that of other existing methods for predicting the regenerative chatter stability in milling operations. A demonstrative experimental verification is provided to illustrate the usage of the proposed NI-HI-3rdFDM to regenerative chatter stability prediction. The feature of accurate computing makes the proposed NI-HI-3rdFDM more adaptable to a dynamic milling scenario, in which a computationally efficient and accurate chatter stability method is required

Optimization of R245fa Flow Boiling Heat Transfer Prediction inside Horizontal Smooth Tubes Based on the GRNN Neural Network

An optimal prediction model for flow boiling heat transfer of refrigerant mixture R245fa inside horizontal smooth tubes is proposed based on the GRNN neural network. The main factors strongly affecting flow boiling such as mass flux rate (G), heat flux (q), quality of vapor-liquid mixture (x), evaporation temperature (Tev), and tube inner diameter (D) are used as the inputs of the model and the flow boiling heat transfer coefficient (h) as the output. Neural network model is used to optimize the prediction of flow boiling heat transfer coefficient of R245fa in horizontal light pipe through training and learning. The prediction results are in good agreement with the experimental results. For the network model of heat transfer, the average deviation is 7.59%, the absolute average deviation is 4.89%, and the root mean square deviation is 10.51%. The optimized prediction accuracy of flow boiling heat transfer coefficient is significantly improved compared with four frequently used conventional correlations. The simulation results reveal that the modeling method based on R245fa neural network is feasible to calculate the flow boiling heat transfer coefficient, and it may provide some guidelines for the optimization design of tube evaporators for R245fa