  Carbon nanotube (CNT) thin sheet, or buckypaper, has shown great potential as
a multifunctional platform material due to its desirable properties, including
its lightweight nature, high mechanical properties, and good conductivity.
However, their mass adoption and applications by industry have run into
significant bottlenecks because of large variability and uncertainty in quality
during fabrication. There is an urgent demand to produce high-quality,
high-performance buckypaper at an industrial scale. Raman spectroscopy provides
detailed nanostructure information within seconds, and the obtained spectra can
be decomposed into multiple effects associated with diverse quality
characteristics of buckypaper. However, the decomposed effects are
high-dimensional, and a systematic quantification method for buckypaper quality
assessment has been lacking. In this paper, we propose a real-time data-driven
quality assessment method, which fills in the blank of quantifying the quality
for continuous manufacturing processes of CNT buckypaper. The composite indices
derived from the proposed method are developed by analyzing in-line Raman
spectroscopy sensing data. Weighted cross-correlation and maximum margin
clustering are used to fuse the fixed effects into an inconsistency index to
monitor the long-term mean shift of the process and to fuse the normal effects
into a uniformity index to monitor the within-sample normality. Those
individual quality indices are then combined into a composite index to reflect
the overall quality of buckypaper. A case study indicates that our proposed
approach can determine the quality rank for ten samples, and can provide
quantitative quality indices for single-walled carbon nanotube buckypaper after
acid processing or functionalization. The quality assessment results are
consistent with evaluations from the experienced engineers.
