IJPEM

Real-Time Defect Detection in Fused Deposition 3D Printing Process Using Transformer-based Models

Aarush Aggarwal/Thapar Institute of Engineering and Technology: Thapar Institute of Engineering and Technology (Deemed to be University)

Fused Deposition Modeling, Real-Time defect detection, Transformer-based models, Stringing and Spaghetti defects, CLAHE, SRCNN, SRGAN, DeiT, ViT, Swin Transformer
Fused Deposition Modelling, a key additive manufacturing process, faces persistent challenges in real-time defect detection, particularly for defects such as stringing and spaghetti-like defects. This study presents an approach that integrates transformer-based models, Vision Transformer, Swin Transformer, and Data-efficient Image Transformer, with traditional deep learning architectures such as CNN, InceptionNet, and UNet. A hybrid framework, combining these models with Contrast Limited Adaptive Histogram Equalisation (CLAHE) pre-processing, enhances defect visibility and detection accuracy. Experimental results have shown that DeiT with CLAHE has achieved an average accuracy of 97.6%±0.7, along with high sensitivity and precision. The findings highlight that transformer-based methods can significantly improve defect detection reliability, reduce material waste, and support automation in FDM quality assurance.