Development of Tool Wear Detection System Using Smartphone and CNN With Kalman‐Based Noise Reduction

The development of a reliable and cost‐effective detection system of tool wear is essential for monitoring the machining process. Researchers in recent years have proposed many methods to implement tool condition monitoring (TCM) with the use of timeseries signals acquired by high resolution sensors. However, the high installation and operation costs of these sensors incurs high difficulties for small and medium sized enterprises, whereas using a low‐cost alternative like the smartphone is a viable option. This work introduces a smartphone‐enabled approach that utilises the smartphone vibration sensor with deep learning approach to detect the tool wear in two categories: Fresh and Worn. An extended Kalman filter noise reduction technique is applied to reduce undesired noise effects. Then, the Euclidean norm (l2‐norm) is applied to determine the overall magnitude of vibration and the resulted data is z ‐normalisation and transformed by using the sliding window method into a suitable input shape for the 1D CNN model. The implemented method is validated with real‐world datasets from the milling machining of Al 6061.