Mechanical performance optimization in FFF 3D printing using Taguchi design and machine learning approach with PLA/walnut Shell composites filaments
Yazarlar (1)
Prof. Dr. Fuat KARTAL Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Journal of Vinyl and Additive Technology (Q1) | ||
| Dergi ISSN | 1083-5601 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2025 |
| Cilt / Sayı / Sayfa | 31 / 3 / 622–638 | DOI | 10.1002/vnl.22195 |
| Makale Linki | https://doi.org/10.1002/vnl.22195 | ||
| UAK Araştırma Alanları |
Üretim Teknolojileri
Optimizasyon ve Teknikleri
Eklemeli İmalat
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| Özet |
| This study explores the optimization of mechanical properties in 3D‐printed components made from a Polylactic Acid (PLA) and Walnut Shell Composite using Fused Filament Fabrication (FFF). Employing a machine learning‐based approach, the research identifies the optimal regression model for predicting relationships between printing parameters and material properties. A Taguchi L18 design is used to minimize experiment count while accurately determining parameter levels. Testing was conducted on a composite containing 30% walnut shell fibers, with the Ultimate Tensile Strength (UTS) and Elastic Modulus (E) measured as per ASTM D638 standards. Experimental factors included Layer Thickness (LT), Nozzle Temperature (NT), Deposition Angle (DA), and Printing Speed (PS). Using Analysis of Variance (ANOVA) and machine learning techniques, the effects of these parameters on UTS and E were … |
| Anahtar Kelimeler |
| 3D printing | fused filament fabrication | machine learning | materials | mechanical optimization | PLA | walnut shell composite |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 21 |
| Google Scholar | 27 |