November 28, 2025Virtual and Physical PrototypingOpen Access

Defects, monitoring, and AI-enabled control in soft material additive manufacturing: a review

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Authors

KYKaicheng Yu YYYifeng Yao WZWei Zhang

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Implication

This review demonstrates monitoring techniques for detecting defects in soft materials, suggesting deep learning may enhance predictive monitoring in biomedical applications.

Key Points

Soft materials in additive manufacturing may exhibit defects impacting performance and design, necessitating innovative monitoring techniques.
The review highlights deep learning as a promising avenue for predictive monitoring and defect detection in 3D printing of hydrogels and elastomers.
Assessment of rheological and physicochemical properties in soft materials is critical for understanding defect formation patterns in additive manufacturing.
This review elaborates on AI-enabled strategies and application opportunities in biomedical scaffolds, flexible electronics, and soft robotics.

Cite This Study

Yu et al. (2025) studied this question.

synapsesocial.com/papers/6928f11ba65b730b9ea7a29d https://doi.org/10.1080/17452759.2025.2588456

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Implication

This review demonstrates monitoring techniques for detecting defects in soft materials, suggesting deep learning may enhance predictive monitoring in biomedical applications.

Defects, monitoring, and AI-enabled control in soft material additive manufacturing: a review

Discussion

Member takes

Implication

Key Points

Cite This Study

Defects, monitoring, and AI-enabled control in soft material additive manufacturing: a review

Discussion

Member takes

Implication

Key Points

Cite This Study

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