Classifying formulations of crosslinked polyethylene pipe by applying machine‐learning concepts to infrared spectra
| Autor: | Melanie Hiles, Michael Grossutti, John Dutcher |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Materials science
Polymers and Plastics 0211 other engineering and technologies Infrared spectroscopy 02 engineering and technology 010502 geochemistry & geophysics Machine learning computer.software_genre 01 natural sciences chemistry.chemical_compound Materials Chemistry 021108 energy Physical and Theoretical Chemistry Cluster analysis 0105 earth and related environmental sciences Manufacturing process business.industry Polyethylene Condensed Matter Physics 6. Clean water Support vector machine chemistry Principal component analysis Artificial intelligence business computer |
| Zdroj: | Journal of Polymer Science Part B: Polymer Physics. 57:1255-1262 |
| ISSN: | 1099-0488 0887-6266 |
| Popis: | Crosslinked polyethylene (PEX‐a) pipes are emerging as promising replacements for traditional metal or concrete pipes used for water, gas, and sewage transport. Understanding the relationship between pipe formulation and performance is critical to their proper design and implementation. We have developed a methodology using principal component analysis (PCA) and the machine learning techniques of k‐means clustering and support vector machines (SVM) to compare and classify different PEX‐a pipe formulations based on characteristic infrared (IR) spectroscopy absorbance peaks. The application of PCA revealed that a large percentage (89%) of the total variance could be explained by the first three principal components (PC1‐PC3), with distinct clustering of the data for each formulation. By examining the contribution of the individual IR bands to the PCs, we determined that PC1 could be attributed to different peroxide crosslinkers, whereas PC2 and PC3 could be attributed to differences in the additives. Using the PCA results as input to k‐means clustering and SVM resulted in very high accuracy of classifying the different pipe formulations. Our approach highlights the advantages of using PCA and machine learning techniques to characterize different formulations of PEX‐a pipes, which is important to achieve a detailed understanding of the pipe formulation and manufacturing process. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 1255–1262 |
| Databáze: | OpenAIRE |
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