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.

中文翻译：

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碳纳米管巴克纸连续制造的实时数据驱动质量评估

碳纳米管 (CNT) 薄片或巴克纸由于其理想的特性，包括其轻质、高机械性能和良好的导电性，已显示出作为多功能平台材料的巨大潜力。然而，由于制造过程中质量的巨大可变性和不确定性，它们的大规模采用和行业应用遇到了重大瓶颈。迫切需要以工业规模生产高质量、高性能的巴克纸。拉曼光谱可在几秒钟内提供详细的纳米结构信息，所获得的光谱可以分解为与巴基纸不同质量特征相关的多种效应。然而，分解效果是高维的，一直缺乏对巴克纸质量评估的系统量化方法。在本文中，我们提出了一种实时数据驱动的质量评估方法，填补了量化碳纳米管巴克纸连续制造过程质量的空白。通过分析在线拉曼光谱传感数据，开发了从所提出的方法得出的复合指数。加权互相关和最大边际聚类用于将固定效应融合为不一致性指数以监测过程的长期均值漂移，并将正态效应融合为均匀性指数以监测样本内正态性。然后将这些单独的质量指数组合成一个综合指数，以反映巴克纸的整体质量。一个案例研究表明，我们提出的方法可以确定十个样本的质量等级，并且可以为酸处理或功能化后的单壁碳纳米管巴克纸提供定量的质量指标。质量评估结果与经验丰富的工程师的评估一致。