On-line crystallinity measurement using laser Raman spectrometer and neural network

Batur, C.; Vhora, Mohamad Hanif; Çakmak, Mükerrem; Serhatkulu, T.

doi:10.1016/s0019-0578(99)00012-9

Cited by 12 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They often rely on small sample size and require significant handling time. The most common off‐line method is differential scanning calorimetry (DSC), which relies on the heat associated with melting of small samples, typically less than 20 mg. DSC has been widely used in polymer analysis (Batur et al, 1999; Saeki et al, 2003; Sato et al, 2003; Albano et al, 2003; Watanabe et al, 2006; Pelsoci, 2007). Numerous microscopy methods also exist.…”

Section: Introductionmentioning

confidence: 99%

“…Infrared reflection spectroscopy has also been used to study polymer crystallinity; Sato et al (2003) collected the diffuse reflectance spectra of plastic pellets placed on a rotating cell and used the average spectra for predicting pellet crystallinity. Work has also been reported on real‐time crystallinity estimation by single‐point laser Raman spectroscopy (Batur et al, 1999; Cherukupalli and Ogale, 2004).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On‐line prediction of crystallinity spatial distribution across polymer films using NIR spectral imaging and chemometrics methods

2008

View full text Add to dashboard Cite

A rapid and non-intrusive on-line NIR imaging sensor is developed for monitoring spatio-temporal crystallinity variations across the surface of polymer films. A multivariate image analysis and regression (MIA/MIR) approach is proposed and compared with standard NIR calibration techniques using averaged spectra or second order derivatives combined with PLS regression. Predictions of both the local and global crystallinity variations of HDPE, LDPE, and PP polymer samples were obtained with each approach. Our results show that small variations in crystallinity introduced by changes in cooling rates can be predicted within experimental errors. Crystallinity spatial distributions were also validated and found consistent with processing conditions. Un capteur d'imagerie en proche infrarouge (NIR) rapide et non intrusif est mis au point pour faire le suivi des variations de cristallinité spatiotemporelleà la surface des films de polymères. Une approche par régression et analyse d'images multivariées (MIA/MIR) est proposée et comparée aux techniques de calibration NIR utilisant des spectres moyens ou des dérivées du second ordre combinésà la régression PLS. Des prédictions des variations de cristallinité locales et globales d'échantillons de polymères HDPE, LDPE et PP sont obtenues avec chacune des approches. Nos résultats montrent que de faibles variations dans la cristallinité introduites par des changements dans les vitesses de refroidissement peuventêtre préditesà l'intérieur des erreurs expérimentales. Les distributions spatiales de cristallinité sontégalement validées et s'avèrent cohérentes avec les conditions de traitement.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On‐line prediction of crystallinity spatial distribution across polymer films using NIR spectral imaging and chemometrics methods

2008

View full text Add to dashboard Cite

show abstract

“…This article is an extension of the previous study18 on the application of RANN to measure the crystallinity of a polymeric material online and nonintrusively while the sample is cooled from the melt at a fixed cooling rate. The advantage of the Neural Network is that it makes use of the whole Raman spectrum in calculating the crystallinity rather than concentrating on the unique Raman peaks as is common in the literature.…”

Section: Resultsmentioning

confidence: 99%

“…We term the whole experimental and estimation procedure as Raman spectroscopy with neural network (RANN). Initial studies have also been successfully conducted on other systems 16, 18…”

Section: Introductionmentioning

confidence: 99%

On line measurement of crystallinity of nylon 6 nanocomposites by laser Raman spectroscopy and neural networks

Ergungor

Batur

Çakmak

2004

J of Applied Polymer Sci

View full text Add to dashboard Cite

ABSTRACT:A neural network is trained to estimate the unknown crystallinity and temperature of Nylon 6 and its nanocomposites while the material is undergoing cooling at a fixed rate. The innovation of the work is that the full spectrum captured by the laser Raman spectroscope is used to train a neural network for estimation of crystallinity and temperature. The small-angle light scattering (SALS) and differential scanning calorimetry (DSC) data were used to provide the training examples for the neural network. Results indicate that the neural network can provide reliable estimates of the crystallinity and temperature provided that there is a sufficient number of training data available. Neural network methodology is also efficient in establishing the crystallization-temperature relationship as a function of cooling rate and demonstrates the heterogeneous nucleation effect of nanoclay in the nylon 6 matrix. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 474 -483, 2004

show abstract

“…Batur et al also used a number of machine learning tools, including PCA, and a set of different experimental techniques in a bid to quantify the crystallinity of low-density polyethylene (LDPE) films [117]. In a multistep experiment, a first set of training data was produced by heating a thin film of LDPE to 120 • C in order to produce a completely amorphous sample.…”

Section: Classification and Chemometrics Problemsmentioning

confidence: 99%

Polymer Informatics

Adams

2010

Polymer Libraries

View full text Add to dashboard Cite

Polymers are arguably the most important set of materials in common use. The increasing adoption of both combinatorial as well as high-throughput approaches, coupled with an increasing amount of interdisciplinarity, has wrought tremendous change in the field of polymer science. Yet the informatics tools required to support and further enhance these changes are almost completely absent. In the first part of the chapter, a critical analysis of the challenges facing modern polymer informatics is provided. It is argued, that most of the problems facing the field today are rooted in the current scholarly communication process and the way in which chemists and polymer scientists handle and publish data. Furthermore, the chapter reviews existing modes of representing and communicating polymer information and discusses the impact, which the emergence of semantic technologies will have on the way in which scientific and polymer data is published and transmitted. In the second part, a review of the use of informatics tools for the prediction of polymer properties and in silico design of polymers is offered.

show abstract

On-line crystallinity measurement using laser Raman spectrometer and neural network

Cited by 12 publications

References 6 publications

On‐line prediction of crystallinity spatial distribution across polymer films using NIR spectral imaging and chemometrics methods

On‐line prediction of crystallinity spatial distribution across polymer films using NIR spectral imaging and chemometrics methods

On line measurement of crystallinity of nylon 6 nanocomposites by laser Raman spectroscopy and neural networks

Polymer Informatics

Contact Info

Product

Resources

About