Aug 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 36 references
TL;DR
A lightweight feature-embedding framework for node-level material prediction in CAD assemblies that projects semantic, geometric, and physical component descriptors into an expanded embedding space, concatenates the learned embedding with the original descriptors, and uses a zero-initialized adaptive residual branch to control low-level feature supplementation during training.
This work builds ORDER, a multimodal framework linking microstructures and descriptors with preserved property trends, aiding prediction, retrieval, and microstructure generation and consistently outperforms alignment-oriented and property-aware baselines across property prediction, cross-modal retrieval, and microstru...
Xinyao Li, Hangwei Qian, Jingjing Li et al.· Nature Communications· 0 citations
The proposed PC-MCVAE framework provides an effective methodology for multimodal information fusion, knowledge-guided generative modeling, and intelligent decision support in engineering-oriented design systems.
Overall, this method provides an intelligent solution that balances efficiency, cost control and market adaptability for the visual branding construction of agricultural products in resource-constrained scenarios.
Yanan Jiang, Yong-Xiao Liu· International journal of pat...· 0 citations
This article presents how ML approaches may speed up material discovery, reduce trial-and-error, and enable individualized design solutions for 3D-printed polymers across a variety of contexts.
N. Senthilkumar, S. Gopalakrishnan, S. Gopinath et al.· Interactions· 0 citations
An improved lightweight detection model (MBHS-DETR) is proposed that maintains high accuracy while also ensuring real-time performance, providing an effective and practical solution for bearing surface defect detection.
This review aims to offer a conceptual framework and a solid reference for building intelligent multimodal material selection systems and presents a forward-looking research agenda covering self-supervised learning, knowledge-enhanced models, and interpretable human–AI collaboration.
Yuwei Zhang, C. Tan, Bei-Chen Wang et al.· IEEE Access· 0 citations
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