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

Xiaowei Yue, Kan Wang, Hao Yan, Jin Gyu Park, Zhiyong Liang, Chuck Zhang, Ben Wang, Jianjun Shi

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Process monitoring and quality control is essential for continuous manufacturing processes of carbon nano- tube (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.

Original languageEnglish (US)
Pages (from-to)196-207
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Issue number1
StatePublished - Jan 2017
Externally publishedYes


  • Carbon nanotube (CNT)
  • Raman spectroscopy
  • nanomanufacturing
  • signal-dependent noise
  • wavelet

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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