Estimation of friction and wear properties of additively manufactured recycled-ABS parts using artificial neural network approach : effects of layer thickness, infill rate, and building direction / Cagin Bolat in INTERNATIONAL POLYMER PROCESSING, Vol. 39, N° 3 (2024)  

[article]  inINTERNATIONAL POLYMER PROCESSING > Vol. 39, N° 3 (2024) . - p. 293-307 Titre : Estimation of friction and wear properties of additively manufactured recycled-ABS parts using artificial neural network approach : effects of layer thickness, infill rate, and building direction Type de document : texte imprimé Auteurs : Cagin Bolat, Auteur ; Abdulkadir Çebi, Auteur ; Sarp Çoban, Auteur ; Berkay Ergene, Auteur Année de publication : 2024 Article en page(s) : p. 293-307 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Coefficient de friction Dépôt de fil fondu Impression tridimensionnelle Modélisation par dépôt en fusion Produits et matériaux recyclés Réseaux neuronaux (informatique) Résistance à l'usure Terpolymère acrylonitrile butadiène styrène Index. décimale : 668.4 Plastiques, vinyles Résumé : This investigation aims to elucidate friction and wear features of additively manufactured recycled-ABS components by utilizing neural network algorithms. In that sense, it is the first initiative in the technical literature and brings fused deposition modeling (FDM) technology, recycled filament-based products, and artificial neural network strategies together to estimate the friction coefficient and volume loss outcomes. In the experimental stage, to provide the required data for five different neural algorithms, dry-sliding wear tests, and hardness measurements were conducted. As FDM printing variables, layer thickness (0.1, 0.2, and 0.3 mm), infill rate (40, 70, and 100 %), and building direction (vertical, and horizontal) were selected. The obtained results pointed out that vertically built samples usually had lower wear resistance than the horizontally built samples. This case can be clarified with the initially measured hardness levels of horizontally built samples and optical microscopic analyses. Besides, the Levenberg Marquard (LM) algorithm was the best option to foresee the wear outputs compared to other approaches. Considering all error levels in this paper, the offered results by neural networks are notably acceptable for the real industrial usage of material, mechanical, and manufacturing engineering areas. Note de contenu : - MATERIALS AND METHODOLOGY : Material and 3D printing procedure - Friction test procedure - Artificial neural network strategy - RESULTS AND DISCUSSION : Wear, hardness and friction coefficient analyses - Neural network outcomes DOI : https://doi.org/10.1515/ipp-2023-4481 En ligne : https://drive.google.com/file/d/1rWcVIQYQYA4PZTW2nrGdBcqGX1nc6H1s/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=40871 [article]

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