Estimation of friction stir welding parameters of 3D printed sheets using ANN

Year 2024, Volume: 13 Issue: 1, 176 - 187, 15.01.2024

Nergizhan Anaç Oğuz Koçar Erhan Baysal

https://doi.org/10.28948/ngumuh.1295673

Abstract

In this study, sheets were printed from PLA Wood and PLA-CF filaments using a 3D printer. The prepared sheets were welded using the FSW method. The process parameters were selected as different mixed tip geometries, tool feed rate, and rotational tool speed. The data set obtained from the experiments was used to train and test artificial neural networks. To optimize the ANN model, 30 models were created by changing the training algorithm, transfer function, and the number of neurons in the hidden layer. The training and test results of the models were statistically evaluated. As a result, in the best prediction model (trainlm, number of neurons 14 and tansig), the training result was 96% accurate and the test result was 99% accurate. The training and testing results of the ANN model show that ANN can be used to estimate the value of process parameters in the FSW method.

Keywords

Additive manufacturing, 3D printing, Thermoplastic material, Filler reinforced filament, Friction stir welding, Artificial neural networks (ANN)

3B yazıcıda üretilen plakaların sürtünme karıştırma kaynak parametrelerinin YSA ile tahmini

Abstract

Bu çalışmada 3B yazıcı kullanılarak PLA Wood (Ahşap katkılı polilaktik Asit) ve PLA-CF (karbon fiber katkılı polilaktik Asit) filamentlerden plakalar basılmıştır. Hazırlanan plakalar SKK metodu kullanılarak birleştirilmiştir. SKK metodunda işlem parametreleri farklı karıştırıcı uç geometrisi (kare, üçgen ve vida), takım ilerleme hızı (20, 40 ve 60 mm/dk) ve takım dönme hızı (1250, 1750 ve 2250 dev/dk) olarak seçilmiştir. Yapılan deneyler sonucunda elde edilen veri seti (54 deney) yapay sinir ağlarının eğitim ve testi için kullanılmıştır. YSA modelinin optimizasyonu için eğitim algoritması, transfer fonksiyonu ve gizli katmandaki nöron sayısı değiştirilerek 30 model oluşturulmuştur. Modellerin eğitim ve test sonuçları istatistiksel olarak değerlendirilmiştir. Sonuç olarak en iyi tahmin modelinde (trainlm, nöron sayısı 14 ve tansig) eğitim sonucu %99, test sonucu %96 doğrulukla yapılmıştır. YSA modelinin eğitim ve test sonuçları, YSA'nın SKK metodunda işlem parametrelerinin değerini tahmin etmek için kullanılabileceğini göstermektedir.

Keywords

Eklemeli imalat, 3B yazdırma, Termoplastik malzeme, Dolgu takviyeli filament, Sürtünme karıştırma kaynağı, Yapay sinir ağları (YSA)

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