Comparative Evaluation of Wavelet Transform Methods for Surface Roughness in Turning of Monel 400 Superalloy: A Precision Analysis

Wavelet transforms offer multi-resolution analysis capabilities, enabling the extraction of both local and global features from the surface roughness data. This research article presents a comparative evaluation of wavelet transform methods for surface roughness evaluation in the turning of Monel 400 alloy. The purpose of the study was to identify the most precise technique among the various wavelet transform methods. Surface roughness data were collected and analyzed using these methods, and their respective accuracy rates were determined. The stationary wavelet transform (SWT) had the highest accuracy rate of 89.6667%, far higher than the other approaches. TDyWT had an accuracy rate of 83.9733%, 6.4% higher than SWT. EWT accuracy was 88.5389%, 1.3% higher than SWT. The discrete wavelet transform (DWT) had an accuracy rate of 88.444%, 1.2% lower than the SWT. Multi-resolution analysis and noise filtering capabilities of stationary wavelet transform allow for a more thorough examination of surface roughness characteristics.