Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

This paper investigates an improved noise reduction method and its application on gearbox vibration signal de-noising. A hybrid de-noising algorithm based on local mean decomposition (LMD), sample entropy (SE), and time-frequency peak filtering (TFPF) is proposed. TFPF is a classical filter method in the time-frequency domain. However, there is a contradiction in TFPF, i.e., a good preservation for signal amplitude, but poor random noise reduction results might be obtained by selecting a short window length, whereas a serious attenuation for signal amplitude, but effective random noise reduction might be obtained by selecting a long window length. In order to make a good tradeoff between valid signal amplitude preservation and random noise reduction, LMD and SE are adopted to improve TFPF. Firstly, the original signal is decomposed into PFs by LMD, and the SE value of each product function (PF) is calculated in order to classify the numerous PFs into the useful component, mixed component, and the noise component; then short-window TFPF is employed for the useful component, long-window TFPF is employed for the mixed component, and the noise component is removed; finally, the final signal is obtained after reconstruction. The gearbox vibration signals are employed to verify the proposed algorithm, and the comparison results show that the proposed SE-LMD-TFPF has the best de-noising results compared to traditional wavelet and TFPF method.

Carbon nanotube (CNT) film can be used as thin film electrodes and wearable electronic devices due to their excellent mechanical and electrical properties. The femtosecond laser has the characteristics of an ultra-short pulse duration and an ultra-high peak power, and it is one of the most suitable methods for film material processing. The ablation and patterning of CNT film are performed by a femtosecond laser with different parameters. An ablation threshold of 25 mJ/cm2 was obtained by investigating the effects of laser pulse energy and pulse number on ablation holes. Raman spectroscopy and scanning electron microscope (SEM) were used to characterize the performance of the pattern groove. The results show that the oligomer in the CNT film was removed by the laser ablation, resulting in an increase in Raman G band intensity. As the laser increased, the ablation of the CNTs was caused by the energy of photons interacting with laser-induced thermal elasticity when the pulse energy was increased enough to destroy the carbon–carbon bonds between different carbon atoms. Impurities and amorphous carbon were found at and near the cut edge while laser cutting at high energy, and considerable distortion and tensile was produced on the edge of the CNTs’ groove. Furthermore, appropriate cutting parameters were obtained without introducing defects and damage to the substrate, which provides a practical method applied to large-area patterning machining of CNT film.

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