Additive manufacturing (AM) offers several advantages for transforming productive chains, such as generation of complex geometries on demand. However, challenges must be overcome toward increasing manufacturing precision and quality of the parts produced, reducing production time, and standardizing the processing parameters. One example of common failure is the incorrect processing parameter selection for the filament-based AM, which can damage 3D printing machines, such as hotend clogging. In that way, this paper introduces a method that identifies AM’s polymeric materials through in situ near-infrared (NIR) spectroscopy and classifies them into poly(lactic acid), acrylonitrile butadiene styrene, and poly(ethylene glycol terephthalate), also enabling a manual parameter input. A low-cost NIR spectrophotometer was used to analyze 16 filaments with color and manufacturer variability. Each filament was probed 3 times in 3 distinct places, raising 144 spectra. Chemometrics were applied to identify relevant peaks for functional groups, and a linear regression was used to filter out data that showed no such peaks. In a second stage, a second-derivative Savitzky–Golay was used to aid in class separation, and a principal component analysis was performed to reduce data dimensionality. The resulting projections were classified by an LDA algorithm, and 3 study cases conducted with data augmentation tested the classifier. The results show the proposed method is robust to bias variation and can handle blends of up to 70%–30% mix and correctly separate signals with and without peaks. Such responses have proved the feasibility of the classification system, especially when fed with a highly varied data set.
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The authors acknowledge Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Brazil—finance code 001 (fellowship 88887.200686/2018-00) for the financial support, 3D Fila®, 3D Lab®, Cliver®, and UP3D® companies for the donation of the polymer filaments, and Prof. J.J.R. Rohwedder for the use of NIR spectroscopy. INCTAA (CNPq 465768/2014-8 and FAPESP 2014/50951-4) is also acknowledged for financial support.
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES): 88887.200686/2018–00 INCTAA (CNPq 465768/2014–8 and FAPESP 2014/50951–4).
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da Cunha, D.A.L.V., Ribessi, R.L., Hernandes, A.C. et al. Online near-infrared spectroscopy for automatic polymeric material identification. J Braz. Soc. Mech. Sci. Eng. 44, 338 (2022). https://doi.org/10.1007/s40430-022-03645-1