{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:59:43Z","timestamp":1780084783097,"version":"3.54.0"},"reference-count":38,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>Infrared (IR) spectroscopy offers direct insight into the vibrational modes of polymers, yet its full potential for predicting critical thermal properties remains underexplored. Herein, IR spectra is integrated with machine learning (ML) models, including support vector regression (SVR) and convolutional neural networks, to predict glass transition temperatures () with high accuracy across diverse polymer formulations. Compared to fingerprint\u2010based descriptors, IR\u2010driven approaches capture subtle molecular variations such as impurities, additives, and colorants, that significantly influence the properties of polymers. By leveraging the rich spectral data, the models achieve improved predictive performance, with the SVR model achieving the highest prediction accuracy, evidenced by  value of 0.94 and tighter residual distributions. The real\u2010time process control is further demonstrated by applying these models to 3D printing, dynamically adjusting bed and nozzle temperatures to accommodate feedstock variability. This end\u2010to\u2010end system highlights the promise of IR\u2010enabled ML in advancing polymer characterization, offering a robust path toward more intelligent, more adaptive, and more efficient manufacturing workflows.<\/jats:p>","DOI":"10.1002\/aisy.202500427","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T18:05:03Z","timestamp":1751911503000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Infrared\u2010Driven Polymer Intelligence"],"prefix":"10.1002","volume":"7","author":[{"given":"Gorkem Anil","family":"Al","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering University of Bath  Bath BA2 7AY UK"},{"name":"Centre for Digital Manufacturing and Design (The Foundry) University of Bath  Bath BA2 7AY 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