Xiaofang Cheng

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About

Xiaofang Cheng is a leading researcher in advanced manufacturing and intelligent process optimization, with a particular focus on robotic polishing and artificial intelligence-driven surface engineering. Their most-cited work introduces a groundbreaking two-step optimization method for robotic polishing of mold steel, integrating XGBoost-based predictive modeling with multi-objective optimization to achieve quantitative surface quality control and enhanced polishing efficiency. This research, published in 2024, has already garnered 4 citations, signaling its rapid impact on the field. Cheng’s contributions are pivotal for industries requiring precision surface finishing, such as automotive and aerospace manufacturing, where they bridge the gap between traditional empirical methods and data-driven automation. By leveraging machine learning to predict workpiece surface outcomes and optimize process parameters, Cheng has set a new standard for intelligent manufacturing workflows. Their work not only advances the theoretical understanding of robotic polishing but also offers practical, scalable solutions for industrial applications. Cheng’s innovative approach to combining artificial intelligence with mechanical processing positions them as a key figure in the next generation of smart manufacturing research.