Defect detection in fused deposition modelling using lightweight convolutional neural networks

Basil Kuriachen, Rathinaraja Jeyaraj, Deepak Raphael, P. Ashok, P. Shanmuga Sundari, Anand Paul

Research output: Contribution to journalArticlepeer-review

Abstract

Early detection of defects in additive manufacturing (AM) processes has significant benefits such as reducing material waste and improving production time, which results in an increase in overall productivity. Existing methods for defect detection in AM lacks in generalizability, takes more time for training, and finds difficulty in identifying complex defects in the vertical plane in real-time. In addition, there are no datasets freely available for active research. Considering this, in this article, we propose an algorithm that uses simple convolutional neural networks (CNNs), which is an artificial intelligence (AI) technique, to detect major defects in layers during fused deposition modelling (FDM) in real-time. It analyses AM infill patterns to identify irregularities such as staircase, overfill, and void defects. The proposed model is trained using the dataset that has been collected manually and augmented at different scales for building a robust model. The results show that the proposed model is very effective and provides over 97.77% accuracy on real-time images. Furthermore, our proposed model uses fewer convolution layers than popular models, such as visual geometry group (VGG) 19, mobile neural network (MobileNet) V2, residual network (ResNet) 50, and densely connected convolutional network (DenseNet) 121. In addition, we open source the custom-generated datasets that contain staircase, overfill, and void defects images. For future research, we plan to expand dataset diversity and employ real-time adaptive learning.

Original languageEnglish
Article number109802
JournalEngineering Applications of Artificial Intelligence
Volume141
DOIs
StatePublished - 1 Feb 2025

Keywords

  • Additive manufacturing
  • Application of artificial intelligence
  • Convolutional neural networks
  • Defect detection
  • Fused deposition modelling

Fingerprint

Dive into the research topics of 'Defect detection in fused deposition modelling using lightweight convolutional neural networks'. Together they form a unique fingerprint.

Cite this