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.
Highlights
Temperature rise in machining, especially along the tool-chip interface, is one of the major concerns and the main limitation in the selection of process parameters
The chip–tool interface temperature occurred in metal cutting as a result of heat directly influencing the tool wear behaviour of the cutting tool, temperature is of fundamental importance in metal cutting operations
Ant Colony Optimization (ACO) algorithm is a kind of algorithm inspired by real ants
Summary
Temperature rise in machining, especially along the tool-chip interface, is one of the major concerns and the main limitation in the selection of process parameters. Several experimental and analytical techniques have been developed for the measurement of temperatures generated in cutting processes. In the field of cutting, the model of the chip break [3], surface roughness [4] and tool wear[5] based on the neural network have been successfully established. ANNs-based models seem to obtain improved and acceptable performance in cutting operation forecasting issue, the conventional ANNs still suffer from several weaknesses such as the need for a large number of controlling parameters, the difficulty in obtaining stable solutions, the danger of over-fitting and the lack of generalization capability.
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