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Deep Learning-Based Defect Detection and Quality Assurance in Advanced 3D Printing and Microfabrication Processes
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Author(s): Hemant Sahu (Janardan Rai Nagar Rajasthan Vidyapeeth, India), Neeru Rathore (Janardan Rai Nagar Rajasthan Vidyapeeth, India), Vinita Nagda (Geetanjali Institute of Technical Studies, Udaipur, India)and Anita Bhati (Vivekananda Global University, Jaipur, India)
Copyright: 2026
Pages: 16
Source title:
Next-Generation Electronic Textiles and Conductive Materials for Smart Wearables
Source Author(s)/Editor(s): Pranshu Saxena (Bennett University, India), Mandeep Singh (Bennett University, India), Sanjay Kumar Singh (University School of Automation and Robotics, Guru Gobind Singh Indraprastha University, East Delhi, India)and Mamoon Rashid (Bahrain Polytechnic, Bahrain)
DOI: 10.4018/979-8-3373-4287-0.ch012
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Abstract
This paper examines the utilization of deep learning models YOLOv8, ResNet-50, and U-Net together with real-time defect recognition and quality control in FDM (Fused Deposition Modeling) 3D printing. Printed part samples with high-resolution images taken at different stages of printing were annotated and used for training. The models were assessed using performance measurements such as precision, recall, F1-score, accuracy, and AUC-ROC. YOLOv8 performed the best across all measurements and is therefore best suited for real-time implementation. ResNet-50 provided high precision and moderate recall, and U-Net provided excellent recall but poor precision with the occurrence of false positives. The outcomes make YOLOv8 the most suitable for in-process quality control. The work showcases the potential of deep learning in improving the defect recognition process and reducing the occurrence of printing errors and the overall inefficiency of 3D printing.
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