Abstract

Tool wear prediction is a major contributor to the dimensional errors of a work piece in precision machining, which plays an important role in industry for higher productivity and product quality. Tool wear monitoring is an effective way to predict the tool wear loss in milling process. In this paper, a new bionic prediction model is presented based on the generation mechanism of tool wear loss. Different milling conditions are estimated as the input variables, tool wear loss is estimated as the output variable, neural network method is proposed to establish the mapping relation and ant algorithm is used to train the weights of BP neural networks during tool wear modeling. Finally, a real-time tool wear loss estimator is developed based on ant colony alogrithm and experiments have been conducted for measuring tool wear based on the estimator in a milling machine. The experimental and estimated results are found to be in satisfactory agreement with average error lower than 6%.

Keywords:

Ant colony algorithm, CNC milling machine, tool wears monitoring, online estimation,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.