Mechanical strength of PLA parts manufactured by FDM using RSM and fuzzy logic
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Erman Zurnacı
Dr. Erman Zurnacı has been working as an Assistant Professor in the Department of Mechanical Engineering at the Faculty of Engineering and Architecture, Kastamonu University since 2020. He completed his B.Sc., M.Sc., and pH.D. degrees at Karabük University, Karabük, Turkey. His research interests include additive manufacturing, component design, lightweight design, crashworthiness, shape and topology optimization, optimization techniques, and intelligent manufacturing.
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Faik Cüneyd Alıcıoğlu
Faik Cüneyd Alıcıoğlu was born in 1996. He received his B.Sc. degree in Mechanical Engineering from the Faculty of Engineering, Hitit University, Çorum, Turkey, and completed his M.Sc. degree at Kastamonu University in 2025. His main research interests include 3D printing technologies, additive manufacturing, and optimization methods. He is currently working at Kronospan Company as a Flooring Line Production Manager.
Abstract
In 3D printing, the mechanical properties of the products can be improved by optimizing manufacturing parameters. In this study, the manufacturing parameters that influence the mechanical performance of PLA (polylactic acid) components produced using fused deposition modeling (FDM) were investigated through experimental testing and modeling-based optimization techniques. Three key manufacturing parameters (raster angle, infill type, and infill rate) were selected to evaluate their effects on ultimate tensile strength (UTS) and impact strength (IS), which were used as performance criteria. An L18 orthogonal array was used for the experimental design, and tensile and impact tests were conducted in accordance with ASTM standards. Two different optimization techniques were used to predict mechanical properties: Response surface methodology (RSM) and fuzzy logic (FL). A comparative evaluation based on experimental data revealed that RSM provides superior prediction accuracy, with average error rates of 3.89 % for UTS and 9.57 % for IS, while FL exhibits higher deviations (6.35 % and 13.35 %, respectively). The results highlight the importance of statistical modeling in predicting mechanical behavior in additive manufacturing processes and demonstrate that RSM produces more reliable results than FL for parameter optimization. This study provides practical insights for engineers aiming to improve the mechanical performance of 3D printed components.
About the authors
Dr. Erman Zurnacı has been working as an Assistant Professor in the Department of Mechanical Engineering at the Faculty of Engineering and Architecture, Kastamonu University since 2020. He completed his B.Sc., M.Sc., and pH.D. degrees at Karabük University, Karabük, Turkey. His research interests include additive manufacturing, component design, lightweight design, crashworthiness, shape and topology optimization, optimization techniques, and intelligent manufacturing.
Faik Cüneyd Alıcıoğlu was born in 1996. He received his B.Sc. degree in Mechanical Engineering from the Faculty of Engineering, Hitit University, Çorum, Turkey, and completed his M.Sc. degree at Kastamonu University in 2025. His main research interests include 3D printing technologies, additive manufacturing, and optimization methods. He is currently working at Kronospan Company as a Flooring Line Production Manager.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. E. Zurnacı: Supervision, Conceptualization, Methodological design, Statiscal Analysis, Specimen production, Writing – review & editing, F.C. Alıcıoğlu: Conceptualization, Experimental work, Data curation, Formal analysis, Statiscal Analysis, Writing – original draft.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Supplementary Material
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