Toz yatak füzyon birleştirme eklemeli imalatta kusur tespiti için öğrenme aktarımı kullanan derin öğrenme tabanlı bir yaklaşım

Yıl 2022, Cilt: 37 Sayı: 1, 361 - 376, 10.11.2021

Burhan Duman , Koray Özsoy

https://doi.org/10.17341/gazimmfd.870436

Cited By: 7

Öz

Günümüzde toz yatak füzyon birleştirme (TYB) metal eklemeli imalat, karmaşık geometrili parça imalatında sıklıkla tercih edilmesine rağmen, parça imalat süreçlerinin gerçek zamanlı izlenmesi yeterli düzeyde değildir. Bu nedenle makine kontrol sistemi büyük ölçüde açık döngü olarak kalmaktadır. Bazı metal eklemeli imalat makineleri toz yatağının izlenmesini görüntülerle sunarken, toz yatağı katmanında oluşabilecek kusurların otomatik tespiti ve kontrol sistemini uyarıcı yeteneğinin olduğuna rastlanmamıştır. Çalışmada, herhangi bir TYB metal eklemeli imalat makinesinde gerçek zamanlı kontrol sisteminin bir bileşeni olma potansiyeline sahip toz yatağı görüntülerinin yerinde izlenmesi ve kusurların tespiti için makine öğrenmesi temelli örnek bir yaklaşım sunulmuştur. Makine öğrenmesinin alt alanlarından olan derin öğrenme yöntemi kullanılarak, işlemin bir katmanının oluşturulmasında meydana gelebilecek kusurları tespitine yönelik sınıflandırma yapılmıştır. Kusurları algılama ve sınıflandırma işlemi evrişimli sinir ağları modeli kullanılarak yerine getirilmiştir. Modelin eğitimi ve performansı için veri seti, EOS M290 makinesinde imal edilmiş örnek bir üç boyutlu yapının fotoğrafları ile oluşturulmuştur. VGG-16, InceptionV3 ve DenseNet ön öğrenmeli modellerinden transfer öğrenimi yapılarak en iyi performans %86 doğruluk değeri ile VGG-16 modelinde elde edilmiştir.

Anahtar Kelimeler

Eklemeli İmalat, Toz Yatak Füzyon, Derin Öğrenme, Kusur Tespiti, Öğrenme Aktarımı

Teşekkür

Yazarlar, çalışma için imalat ve görüntüleme işlemlerindeki yardımlarından dolayı 3DDT firmasına teşekkür eder.

A deep learning-based approach for defect detection in powder bed fusion additive manufacturing using transfer learning

Öz

Although powder bed fusion joining (TYB) metal additive manufacturing is frequently preferred in the production of complex geometry parts today, real-time monitoring of part manufacturing processes is insufficient. Therefore, the machine control system remains largely open loop. While some metal additive manufacturing machines present the powder bed monitoring with images, it has not been found that they can automatically detect the defects that may occur in the powder bed layer and stimulate the control system. In the study, an exemplary machine learning-based approach is presented for on-site monitoring and defect detection of powder bed images, which can be a component of a real-time control system in any TYB metal additive manufacturing machine. Using the deep learning method, which is one of the subfields of machine learning, a classification was made to detect the defects that may occur in creating a layer of the process. Detection and classification of defects were carried out using the convolutional neural networks model. The data set for training and performance of the model was created with photographs of a three-dimensional sample structure manufactured on the EOS M290 machine. The best performance was obtained in the VGG-16 model with 86% accuracy by performing transfer learning from VGG-16, Inception V3, and DenseNet pre-learning models.

Anahtar Kelimeler

Additive Manufacturing, , Powder Bed Fusion, Deep Learning, Detection Defect, Transfer Learning

Kaynakça

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