Improved Crack Detection in Laser Cladding using Custom Threshold Wavelet Denoising

This paper investigates the use of acoustic signals for online crack detection during laser cladding. Normal and crack acoustic signals were captured using a microphone and analyzed using time-frequency analysis and wavelet thresholding denoising. The optimal wavelet thresholding method was determined to utilize the Db12 wavelet basis function, 3-level wavelet decomposition, and hard thresholding function based on the criteria of maximizing the signal-to-noise ratio (SNR) and minimizing the root mean square error (RMSE).

A novel approach utilizing custom thresholds for each level of wavelet coefficients was employed to denoise the crack signals. This method demonstrated superior performance compared to conventional signal denoising techniques, effectively reducing noise while preserving a higher degree of crack information. The results highlight the feasibility and effectiveness of the custom threshold wavelet denoising method for online detection and analysis of crack acoustic signals during laser cladding. This approach offers valuable insights for real-time monitoring and quality control in additive manufacturing processes.