Abstract
In this present study, an experimental approach for detecting tool chatter in its early stages is proposed. Local mean decomposition based on cubic spline approach is invoked to analyse acquired machining sound data at varied cutting settings. The correlation coefficient and relative energy rule are used to identify the significant PFs. Furthermore, the characteristics of tool chatter are investigated by assessing six dimensional and six dimensionless indexes. Then, for every combination of characteristics, a threshold is defined to detect the aforementioned severity of chatter. At last, tool chatter online monitoring is carried out based on the obtained threshold.
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