Carbon fiber-reinforced polymer (CFRP) drilling is a typical process in the aircraft industry. Because the components of CFRP are different and uneven, it is difficult to extract tool wear characteristics from the machining signals, which are composed of the processing characteristics of various materials and the tool state characteristics. The aim of this work is to present a new comprehensive approach based on multicharacteristics and multisignal sources to predict the tool wear state during CFRP drilling through a combination of a backpropagation (BP) artificial neural network (ANN) model and an efficient automatic system depending on the sliding window algorithm. It was verified that the peak factor and Kurtosis coefficient of different signals and the energy value of the d5 layer of the thrust force signal and the d3 layer of the vibration signal after wavelet decomposition were related to tool wear. Among them, the energy value of the d3 layer of the vibration signal was selected as the wear indicator and was able to describe the state of the tool during the CFRP drilling process regardless of the drilling conditions and individual tool differences. A confirmatory drilling experiment using 6-mm-diameter polycrystalline diamond twist drilling under different processing parameters was conducted to verify the ANN model based on multicharacteristics and multisignal sources. A lower feed speed and a higher cutting speed were both highly correlated with the VB value of flank wear. Drill wear accelerated because of the occurrence of adhesive wear when the number of drilled holes reached around 90. The accuracy of the neural network model is 80–87% when using the value of only one characteristic but clearly increases based on multicharacteristics and multisignal sources in real time, indicating that the BP ANN model has higher accuracy in predicting the tool state in CFRP drilling through the sensor signal fusion method.

In this paper, the L-band wind profile radar data, densely-covered meteorological observation data and environmental monitoring data are used to analyze the characteristics and meteorological causes of a rare persistent heavy haze episode in Changsha from Jan. 27th to 29th, 2017. Results show that at 05:00 on the 28th, the air quality index (AQI) of Changsha exceeded 500, creating the historical record since 2012. During the heavy pollution episode, the south branch trough deepened continuously, and the strong southwest warm and humid air flow in front of the trough stably maintained for 4 days. Water vapor was continuously transported to Changsha, increasing the air humidity in this area, which was conducive to the maintenance and development of heavy pollution. In the maintenance stage of heavy pollution, the low-level jet nucleus, the weak upward motion and the weak vertical wind shear in the lower layer could limit the atmospheric vertical diffusion. The boundary layer height judged by the refractive index structure constant 2 is low, which is only about 300 m. Along with the strong inversion stratification in low levels, the vertical atmospheric turbulent exchange and thermal convection are weakened. With mountains on three sides and the Dongting Lake on the north, Changsha has a relatively weak environmental carrying capacity. Under adverse weather conditions, heavy pollution can be easily formed in Changsha, through the imported polluted air mass and local pollution accumulation. 1