Prediction of Flexural Strength with Fuzzy Logic Approach for Fused Deposition Modeling of Polyethylene Terephthalate Glycol Components

Ulkir O., AKGÜN G.

Journal of Materials Engineering and Performance, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s11665-024-09291-z
  • Dergi Adı: Journal of Materials Engineering and Performance
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Chemical Abstracts Core, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Anahtar Kelimeler: additive manufacturing, FDM, flexural strength, fuzzy logic, PETG, Taguchi
  • Marmara Üniversitesi Adresli: Evet

Özet

Additive manufacturing (AM) is a preferred industrial manufacturing method for modeling and rapid prototyping of physical systems. The final product in AM must have appropriate mechanical properties, such as flexural strength and be of good quality. The selection of printing parameters is essential for this reason. In this study, three critical printing parameters, such as layer thickness (100-200-300 µm), raster angle (0-30-60°), and infill density (40-60-80%) were examined. The analysis of variance method was used to look at the relationship between these parameters and the flexure strength of samples fabricated using the fused deposition modeling technique with polyethylene terephthalate glycol material. The experimental design process was performed using Taguchi L9 orthogonal design. Fuzzy logic-based modeling was applied to estimate the flexural strength. The results demonstrated that the infill density is the most important parameter affecting flexural strength compared to the other parameters. The highest strength of 57.76 MPa was achieved when the layer thickness, raster angle, and infill density were set to 100 µm, 60°, and 80%, respectively. The fuzzy logic provided a high-accuracy estimation of the flexural strength with a maximum percentage error of 2.65%. Consequently, it was determined that the model and experimental results were in agreement.