Abstract:In this paper, support vector regression with ant colony optimization is presented for the prediction of tool-chip interface temperature depends on cutting parameters in machining. Ant colony (ACO) optimization was developed to optimize three parameters of SVR, including penalty parameter C, insensitive loss function ε and kernel function σ. SVR constructs hyperplane in high dimension space and fits the data in non-linear form. Normalized mean square error (NMSE) of fitting result is used as target of ant colony optimization. ACO finds the best parameters which correspond to the NMSE. The results showed that the proposed approach, by comparing with back-propagation neural network model, was an efficient way to model tool-chip interface temperature with good predictive accuracy.

Based on the principle of spinning process of micro grooves inside the circular micro heat pipe, the effects of relative position of balls and tool on the formation of micro grooves were investigated in this work. Experimental results showed that the relative position of balls and tool and ball equivalent diameter generally determined the pattern of grooves, including groove width and depth. Micro grooves could be produced when suitable value of D was chosen and the rake face of tool was near to the center of balls. In the experiment, the largest aspect ratio of micro grooves were achieved with the conditions of D= 5.56mm, d=-3.293mm and v=5.08mm/s. And the structural parameters were w=135μm, h=154μm and k=1.141.

Abstract. With the development of high power LED technology, junction temperature as a key factor constrains the performance and the service life of LED, and the main parameter of junction temperature is thermal resistance. Therefore, how to measure the thermal resistance of high power LED quickly and accurately plays an important part in improving the performance and the service life of LED. In this paper the accurate and fast measurement equipment was applied to study the thermal characteristics of high power LED. The forward-voltage based method was conducted to measure the junction temperature of high power. Then, support vector regression (SVR) combined with genetic algorithm (GA) for its parameter optimization, was proposed to establish a model to predict the thermal resistance of high power LED. The prediction performance of GA-SVR was compared with those of BPNN model. The result demonstrated that the estimated errors GA-SVR models, such as Mean Absolute Relative Error (MARE) and Root Mean Squared Errors (RMSE), all are smaller than those achieved by the BPNN applying identical samples.