Generalized Wavelet Shrinkage of Inline Raman Spectroscopy for Quality Monitoring of Continuous Manufacturing of Carbon Nanotube Buckypaper

Process monitoring and quality control is essential for continuous manufacturing processes of carbon nanotube (CNT) thin sheets or buckypaper. Raman spectroscopy is an attractive inline quality characterization and quantification tool for nanomanufacturing because of its nondestructive nature, fast data acquisition speed, and ability to provide detailed material information. However, there is signal-dependent noise buried in the Raman spectra, which reduces the signal-to-noise (S/N) ratio and affects the accuracy, efficiency, and sensitivity for Raman spectrum-based quality control approaches. In this paper, a signal analysis model with signal-dependent noise for Raman spectroscopy is developed and validated based on experimental data. The wavelet shrinkage method is used for denoising and improving the S/N ratio of raw Raman spectra. Based on the validated signal-noise relationship, a novel generalized wavelet shrinkage approach is introduced to remove noise in all wavelet coefficients by applying individual adaptive wavelet thresholds. The effectiveness of this method is demonstrated using both simulation and experimental case studies of inline Raman monitoring of continuous buckypaper manufacturing. The proposed method allows for a significant reduction of Raman data acquisition time without much loss of S/N ratio, which inherently enables Raman spectroscopy for inline monitoring and control for continuous nanomanufacturing processes. Note to Practitioners-This paper was motivated by the problem of implementing denoising and signal enhancement for Raman spectra to realize inline process monitoring and quality control of continuous nanomanufacturing of carbon nanotube (CNT) buckypaper. Existing approaches like wavelet denoising cannot deal with signal dependent noise buried in the Raman spectra effectively. This paper develops a signal analysis model and validates the signal-noise dependence property. Then a novel generalized wavelet shrinkage approach is used to remove noise in each wavelet coefficient by applying individual adaptive wavelet threshold. With this new approach, signal-noise ratio can be improved efficiently, and process monitoring accuracy and sensitivity can be enhanced effectively. This paper provides a solid foundation for inline process monitoring and quality control for continuous nanomanufacturing of CNT buckypaper. Furthermore, the developed methodology can be applied into denoising signal-dependent noise in other kinds of signals. In future work, we will develop a process monitoring approach of multistage nanomanufacturing process.