Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracy.Highlights • An expert technique for tool wear monitoring based on an experimental dataset is explored. • The feature values with respect to the wear status of cutting tools are extracted and analyzed. • The effects of a proposed deep learning network architecture for identifying the different tool wear statuses are considered. • A patterns prediction method is compared and developed.

In the trend of Industry 4.0 development, the big data of system operation is significant for analyzing, predicting, or identifying any possible problem. This study proposes a new diagnosis technique for identifying the vibration signal, which combines the feature dimensional reduction method and optimized classifier. Firstly, an auto-encoder feature dimensional reduction (AE-FDR) method is constructed with the bottleneck hidden layer to extract the low-dimensional feature. Secondly, a supervised classifier is formed to carry out fine-turning and classification. The least square-support vector machine (LSSVM) classifier is used as basic with an optimized parameter exploited by the backtracking search optimisation algorithm (BSOA). This LSSVM-BSOA is used to identify the gear fault based on the original vibration data. The proposed AE-FDR-LSSVM-BSOA diagnosis technique shows good ability for identifying the gear fault. A helical gear is experimented with three fault status for evaluate this method. The diagnosis result achieves a high accuracy of 93.3%.