Hybrid fuzzy-transformer-CNN network combined with fs-LA-SIBS for accurate classification of magnesium alloy components

Xiaoyong He; Chunhui Huang; Zhenman Gao

doi:10.1039/d6ja00006a

January 1, 2026Journal of Analytical Atomic Spectrometry

Hybrid fuzzy-transformer-CNN network combined with fs-LA-SIBS for accurate classification of magnesium alloy components

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Key Points

This research aims to enhance the classification accuracy of magnesium alloy components using a novel technique.
Developed femtosecond laser-ablation spark-induced breakdown spectroscopy technique.
Combined with Hybrid Fuzzy-Transformer-CNN for improved classification.
Focused on achieving high-fidelity results.
Achieved accurate classification of magnesium alloy components through fs-LA-SIBS and HybridTFNet combination.

Implication

Evaluates magnesium alloy classification using a novel hybrid method, suggesting improved accuracy.

Abstract

A femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) technique was developed and coupled with a novel Hybrid Fuzzy-Transformer-CNN (HybridTFNet) to achieve high-fidelity magnesium alloy component classification.

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Cite This Study

He et al. (2026) studied this question.

synapsesocial.com/papers/6a0020cec8f74e3340f9bad9 https://doi.org/10.1039/d6ja00006a

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Hybrid fuzzy-transformer-CNN network combined with fs-LA-SIBS for accurate classification of magnesium alloy components

Key Points

Implication

Abstract

Discussion

Cite This Study

Hybrid fuzzy-transformer-CNN network combined with fs-LA-SIBS for accurate classification of magnesium alloy components

Key Points

Implication

Abstract

Discussion

Cite This Study

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