Xueming Hua
Papers
2
Total Citations
11
H-Index
2
About
Xueming Hua is a researcher advancing the fields of intelligent manufacturing and machinery safety through deep learning and multiscale analysis. His work focuses on extracting meaningful features from complex industrial processes, notably in his 2022 paper on multiscale feature extraction for weld seam quality prediction in plasma arc welding, which has garnered 7 citations for its practical contributions to real-time quality assurance. More recently, Hua has tackled a critical challenge in risk estimation for machinery safety: the subjectivity of human assessors. His 2024 deep learning method, cited 4 times, demonstrates how AI can standardize risk perception among stakeholders, reducing inconsistencies that hinder safety protocol implementation. By leveraging similarities in risk scenarios, his approach offers a data-driven path to more reliable machinery safety assessments. Hua’s research bridges the gap between advanced computational techniques and real-world industrial applications, making him a notable voice in the push toward safer, smarter manufacturing environments. His work is particularly relevant for students and researchers interested in the intersection of AI, safety engineering, and process optimization.
Research Focus
Key Achievements
Top Papers
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- 2