Abstract
In this paper, comprehensive analysis has been carried out to seek out the effective features which can reveal the tool conditions when turning 50# normalized steel. Tool failure mechanism arising in the cutting processes shows that flank wear is the most common failure mode which is taken as the object in this study. Fourteen time-domain features sensitive to tool wear are picked out by utilizing correlation analysis. There are two kinds of tool wear condition, coded as 0 and 1, which is distinguished by the blunt standard. The predictive v-support vector regression (v-SVR)-based model is constructed to monitor the tool wear conditions. Experimental results show that the prediction accuracy of the v-SVR model reaches up to 96.76%. Besides, the v-SVR model has better prediction effect and stability than the GRNN- and BPNN-based models.
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Li, N., Chen, Y., Kong, D. et al. Force-based tool condition monitoring for turning process using v-support vector regression. Int J Adv Manuf Technol 91, 351–361 (2017). https://doi.org/10.1007/s00170-016-9735-5
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DOI: https://doi.org/10.1007/s00170-016-9735-5