Multi-feature Fusion Based Tool Condition Monitoring in Rough Turning of Inconel 625

Paper presents a tool wear monitoring strategy based on a large number of signal features in rough turning of Inconel 625. Signal features (SFs) were extracted from time domain signals, from frequency domain transforms and from their wavelet coefficients (time-frequency domain). All of them were automatically evaluated regarding their relevancy for tool wear monitoring based on determination coefficient between the feature and its low-pass filtered course, and their repeatability. The selected SF were used for tool wear was estimation. Accuracy of this estimation was then used for evaluation of sensors and signals usability.