A novel contrastive learning framework for multi-parameter optimization in 3D printing

Additive manufacturing (3D printing) revolutionizes prototyping and production through unparalleled material efficiency. However, part quality remains highly sensitive to parameter variations, where subtle deviations induce defects, material waste, and process instability. Traditional quality control methods – reliant on rule-based heuristics or manual inspections – fail to address complex multi-parameter interactions and fine-grained anomalies, limiting industrial scalability. This article focuses on an important issue in 3D printing: How can we develop a robust, automated framework to simultaneously detect and classify subtle multi-parameter anomalies in 3D printing, overcoming the limitations of manual and single-defect-focused approaches? We propose a supervised contrastive learning framework integrating Vision Transformers (ViT) to learn discriminative feature representations for multi-parameter optimization. By maximizing intra-class similarity and inter-class separation, our model captures nuanced variations across printing scenarios. The ViT architecture processes real-time printing images, while contrastive loss ensures compact feature clusters for “Low”, “Optimal”, and “High” parameter classes. Experimental evaluations on open-source datasets demonstrate our framework achieves 8. 45% accuracy, outperforming conventional CNNs by 10. 11%. Real-world validation shows robust performance across critical parameters: flow rate (86. 5% accuracy in nominal ranges), feed rate (87% accuracy), and extrusion temperature (90% accuracy at optimal settings). The ViT’s self-attention mechanism enables precise detection of localized anomalies, such as under-extrusion and layer misalignment.

Authors

• Jieyang Peng
• Simon Kreuzwieser
• Dongkun Wang State Key Laboratory of Internet of Things for Smart City, University of Macau
• Andreas Kimmig
• Zhi Fan
• Jianing Li
• Jivka Ovtcharova

Keywords

• 3D printing
• Contrastive learning
• Multi-parameter optimization
• Vision transformer
• Representation learning