Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning
  Approaches
Md Manjurul Ahsan Shivakumar Raman Zahed Siddique
Additive manufacturing (AM) is gaining attention across various industries
like healthcare, aerospace, and automotive. However, identifying defects early
in the AM process can reduce production costs and improve productivity - a key
challenge. This study explored the effectiveness of machine learning (ML)
approaches, specifically transfer learning (TL) models, for defect detection in
3D-printed cylinders. Images of cylinders were analyzed using models including
VGG16, VGG19, ResNet50, ResNet101, InceptionResNetV2, and MobileNetV2.
Performance was compared across two datasets using accuracy, precision, recall,
and F1-score metrics. In the first study, VGG16, InceptionResNetV2, and
MobileNetV2 achieved perfect scores. In contrast, ResNet50 had the lowest
performance, with an average F1-score of 0.32. Similarly, in the second study,
MobileNetV2 correctly classified all instances, while ResNet50 struggled with
more false positives and fewer true positives, resulting in an F1-score of
0.75. Overall, the findings suggest certain TL models like MobileNetV2 can
deliver high accuracy for AM defect classification, although performance varies
across algorithms. The results provide insights into model optimization and
integration needs for reliable automated defect analysis during 3D printing. By
identifying the top-performing TL techniques, this study aims to enhance AM
product quality through robust image-based monitoring and inspection.