Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/57264
Title: 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
Authors: Bolat, Cagin
Cebi, Abdulkadir
Çoban, Sarp
Ergene, Berkay
Keywords: recycled filament
fused deposition modeling
friction coefficient
wear resistance
artificial neural network
Mechanical-Properties
Process Parameters
Filaments
Behavior
Virgin
Pla
Publisher: Walter De Gruyter Gmbh
Abstract: 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.
URI: https://doi.org/10.1515/ipp-2023-4481
https://hdl.handle.net/11499/57264
ISSN: 0930-777X
2195-8602
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Teknoloji Fakültesi Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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