PLA'nın Mekanik Özelliklerinin Optimizasyonu ve Tahmini: Taguchi ve Yapay Sinir Ağları Kullanılarak Dolgu Deseni, Baskı Sıcaklığı ve Tavlama Süresinin Etkileri

Öz

Bu çalışmada, ergimiş filament imalatı (FFF) yöntemi kullanılarak üretilen PLA çekme numunelerinin mekanik özelliklerinin dolgu deseni, baskı sıcaklığı ve tavlama süresi değişimine göre etkileri deneysel ve istatistiksel olarak incelenmiştir. Numuneler üç farklı dolgu deseni (ızgara, zikzak ve bal peteği) ve iki farklı baskı sıcaklığında (200 ve 230 ℃) üretilmiştir. Numuneler, DoE (Deney Tasarımı) yaklaşımı kullanılarak oluşturulmuş ve yine deney tasarımına göre 90 ve 180 dakika tavlanmıştır. Numunelerin yüzey sertliği ölçülmüş, çekme testleri yapılmış ve gerilme-gerinim eğrilerinden tokluk değerleri hesaplanmıştır. Sonuçlar Taguchi ve ANOVA yöntemleri kullanılarak analiz edilmiş, böylece sonuçlar üzerinde etkili olan ve optimum sonuçların elde edilmesini sağlayan değerler ve parametreler belirlenmiştir. Ek olarak, Yapay Sinir Ağları (YSA) yöntemi kullanılarak bir tahmin modeli oluşturulmuştur. Farklı üretim parametreleri ile kontrol numuneleri üretilmiş ve test edilmiştir. Modelin doğruluğu, deney sonuçları ile tahmin edilen değerler karşılaştırılarak test edilmiştir. Sonuçlar, en yüksek çekme mukavemetinin 45.33 MPa ile H-230-90 numunesi ile elde edildiğini göstermiştir. Bal peteği desenli numunelerde homojen gerilme dağılımları nedeniyle en yüksek çekme mukavemeti ve sertlik değerlerini elde ederken, zikzak desenli numuneler en yüksek süneklik ve tokluk performansını sergilemiştir. Tavlama işleminin çekme mukavemetini ve yüzey sertliğini artırdığı, ancak uzamayı azalttığı bulunmuştur. ANOVA sonuçlarına göre, çekme mukavemeti üzerinde en etkili parametre %96.06 ile dolgu deseni olmuştur. Model, ızgara ve zikzak dolgu desenli numunelerin tahmininde başarılı sonuçlar vermiş, ancak bal peteği numuneleri için yüksek tahmin hataları elde edilmiştir.

Anahtar Kelimeler

• Eklemeli İmalat
• Mekanik özellikler
• Çekme dayanımı
• Taguchi
• Yapay sinir ağları

Optimization and Prediction of Mechanical Properties of PLA: Effects of Infill Pattern, Printing Temperature and Annealing Duration Using Taguchi and Artificial Neural Networks

Öz

This study experimentally and statistically examined the effects of infill pattern, printing temperature, and annealing duration on the mechanical properties of PLA tensile samples produced using the fused filament fabrication method (FFF). The samples were produced in three different infill patterns (grid, zigzag, and honeycomb) and at two different printing temperatures (200 and 230 ℃). The samples were annealed for 90 and 180 minutes according to the experimental design created using the DoE (Design of Experiment) approach. The surface hardness of the samples was measured and tensile tests were performed. The results were analyzed using Taguchi and ANOVA methods, so that the values and parameters effective on the results and obtaining the optimum results were determined. Additionally, a prediction model was created using the Artificial Neural Network method (ANN). Control samples were produced under different manufacturing parameters and tested. The accuracy of the model was tested by comparing the experimental results to the predicted values. The results indicated that the highest tensile strength was achieved with sample H-230-90 at 45.33 MPa. The honeycomb achieves the highest tensile strength and hardness values due to its homogeneous stress distribution, while zigzag patterned samples exhibit the highest ductility. It was found that the annealing process increased tensile strength and surface hardness, but decreased elongation. According to the ANOVA results, the most effective parameter on tensile strength was the infill pattern with 96.06%. The model yielded successful results in predicting grid and zigzag infill pattern samples but had high error rates in predicting honeycomb samples.

Anahtar Kelimeler

• Additive manufacturing
• Mechanical properties
• Tensile strength
• Taguchi
• Artificial neural network

Kaynakça

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