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Journals / Yekarum / 2024 / Cilt: 9 Sayı: 2

PREDICTION OF TENSILE STRENGTH BASED ON MELTING POINT AND FLEXIBILITY PROPERTIES OF 3D PRINTER PLASTIC FILAMENTS USING MACHINE LEARNING

MAKİNE ÖĞRENMESİ İLE 3 BOYUTLU YAZICI PLASTİK FİLAMENTLERİNİN ERGİME NOKTASI VE ESNEKLİK ÖZELLİKLERİNE DAYALI ÇEKME DAYANIMININ TAHMİNİ

Muzaffer Eylence(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ TEKNOLOJİ FAKÜLTESİ)0000-0001-7299-8525
Bekir Aksoy(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ TEKNOLOJİ FAKÜLTESİ)0000-0001-8052-9411
Koray Özsoy(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ ISPARTA OSB MESLEK YÜKSEKOKULU MAKİNE VE METAL TEKNOLOJİLERİ BÖLÜMÜ)0000-0001-8663-4466
Journal
Yekarum
Pages
91–107
Publication Date
2024-12-27
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results underscore the effectiveness of machine learning in automatically determining the strength values of filaments in EBM processes, one of the additive manufacturing methods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Additive manufacturing is an innovative technology that produces objects by depositing material in layers, commonly known as 3D printing.
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Key Findings & Data:In this study, the aim was to develop machine learning techniques for determining the strength values of thermoplastic filaments from different brands in FDM-based 3D printing applications.
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Conclusion & Impact:These results underscore the effectiveness of machine learning in automatically determining the strength values of filaments in EBM processes, one of the additive manufacturing methods.
🔬Design:Modeling & Simulation
📍Scope:800 samples
⚙️Variables:Eklemeli imalat · EBM · Filament

Abstract

Additive manufacturing is an innovative technology that produces objects by depositing material in layers, commonly known as 3D printing. This method allows for production by adding layers instead of removing material, in contrast to traditional manufacturing, and has led to the widespread use of popular forms such as Fused Deposition Modeling (FDM). In this study, the aim was to develop machine learning techniques for determining the strength values of thermoplastic filaments from different brands in FDM-based 3D printing applications. To achieve this, machine learning algorithms such as Pruned Decision Trees, Support Vector Machines, and Naive Bayes were employed to estimate the strength values of common thermoplastic filaments. The dataset used in this study consists of 800 samples containing information such as plastic type, melting point, flexibility, durability, areas of use, and brand. The performance of the machine learning algorithms was evaluated using standard metrics such as accuracy and F1-score, which provide insights into the model's ability to predict strength values. Notably, the model demonstrated strong performance across all metrics, achieving a 96% success rate with the Pruned Decision Trees algorithm in predicting the strength values of thermoplastic filaments used for 3D printing. These results underscore the effectiveness of machine learning in automatically determining the strength values of filaments in EBM processes, one of the additive manufacturing methods.

Özet

Eklemeli imalat, malzemeyi katmanlar halinde biriktirerek nesneler üreten ve genellikle 3D baskı olarak bilinen yenilikçi bir teknolojidir; bu yöntem, geleneksel imalatın aksine malzemeyi çıkarmak yerine katman ekleyerek üretim sağlarken, Ergiyik Biriktirme Modelleme (EBM) gibi popüler formlarının yaygın kullanımına yol açmıştır. Bu çalışmada, EBM tabanlı 3D baskı uygulamalarında farklı markalara ait termoplastik filamentlerin dayanım değerlerinin belirlenmesine yönelik makine öğrenimi tekniklerinin geliştirilmesi hedeflenmiştir. Bu amaç doğrultusunda, yaygın termoplastik filamentlerin dayanım değerlerinin tahmin edilmesi için Pruned Decision Trees, Destek Vektör Makineleri ve Naive Bayes gibi makine öğrenimi algoritmalarından yararlanılmıştır. Bu çalışmada kullanılan veri seti; plastik türü, erime noktası, esneklik, dayanıklılık, kullanım alanları ve marka gibi bilgileri içeren 800 veri örneğinden oluşmaktadır. Makine öğrenme alagoritmaların performansı, modelin dayanım değerini tahmin yeteneği hakkında bilgi sağlayan doğruluk ve F1-skoru gibi standart değerlendirme metrikleri kullanılarak değerlendirilmiştir. Dikkate değer bir şekilde, model tüm metriklerde yüksek bir performans sergileyerek, 3D baskı için kullanılan termoplastik filamentlerin dayanım değerlerinin tahmininde Pruned Decision Trees algoritması ile %96'lık bir başarı oranı elde etmiştir. Bu sonuçlar, makine öğrenmenin eklemeli imalat yöntemlerinden EBM süreçlerindeki filamentlerin dayanım değerlerinin otomatik tespiti konusunda etkinliğini ortaya koymaktadır.

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Cite This Article

Eylence, M., Aksoy, B., Özsoy, K. (2024). PREDICTION OF TENSILE STRENGTH BASED ON MELTING POINT AND FLEXIBILITY PROPERTIES OF 3D PRINTER PLASTIC FILAMENTS USING MACHINE LEARNING. Yekarum, Cilt: 9 Sayı: 2, 91–107. https://ajindex.com/articles/1577665

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