Analysis of friction and wear vibration signals in Micro-Textured coated Cemented Carbide and Titanium Alloys using the STFT-CWT method

Vibration behavior is an important evaluation index in the friction and wear performance test of materials. However, the current method is not comprehensive enough in the data representation of vibration behavior, which easily leads to the loss of features. Therefore, this paper proposes a vibration behavior data representation method based on dual time-frequency analysis and multiple algorithms. Firstly, a test platform for friction and wear of micro-textured AlSiTiNcoated cemented carbide and titanium alloy was built. Subsequently, vibration signals and friction data were acquired. DBO (Dung Beetle Optimizer) was used to optimize VMD (Variational Mode Decomposition) to denoise the original signal. Secondly, the characteristics of time-frequency images of vibration signals STFT (Short-Time Fourier Transform) and CWT (Continuous Wavelet Transform) are explored through image morphological analysis. Based on the results of the analysis, the corresponding features are extracted. MIC (Maximal Information Coefficient) combined with the BP neural network is used to screen the extracted features. Bayesian optimization random forest algorithm is used to verify the feature screening results and provide a friction prediction model. Finally, based on the obtained vibration behavior characteristic values, the effects of preparation process parameters and properties of micro-textured AlSiTiN coatings on vibration were investigated. Optimal parameters for micro-textured AlSiTiN coatings that exhibit superior vibration behavior were identified as follows: h = 2.9 mu m, P = 45 W, v = 1700 mm/s, n = 8 time, d = 50 mu m, l = 130 mu m. It provides a novel method for the digital characterization of vibration behavior and a new idea for improving the surface properties of cemented carbide.