Prof. Shao Hsien Chen | Smart machine | Best Researcher Award
Prof. Shao Hsien Chen,Ā National Chin-Yi University of Technology, Taiwan
Prof. Shao Hsien Chen is academic and researcher in the field of renewable energy, holds a PhD in Bio systems Engineering from Kangwon National University, South Korea. His academic journey has been marked by a profound dedication to advancing solar energy technologies, specifically in solar thermal harvesting and its integration into agricultural and architectural applications.
š Academic Background:
B.S. degree from National Chin-Yi University of Technology, Taiwan, in 1992.M.S. and Ph.D. degrees from National Chung Cheng University, Taiwan, in 2001 and 2006, respectively.
šØāš« Professional Experience:
R&D manager at Ching Hung Machinery & Electric Industrial Co. LTD and AWEA Machinery & Electric Industrial Co. LTD, Taiwan, from 2005 to 2009.Professor at National Chin-Yi University of Technology since 2009.
š Research Focus:
Smart machines, machine tool design, and superalloy machining.
š Selected Research Highlights:
š Utilized Neural Networks for cutting temperature prediction.š Developed a Hybrid Optimization Algorithm for thermal displacement compensation.š ļø Explored the application of Response Surface Methodology in rigid tapping.š Investigated the use of CNN-BP for Inconel-718 chip feature analysis.š Applied Backpropagation Neural Model in automatic lubrication installation.š Explored the integration of CNC Machine Tool with Plasma for online surface heat treatment.This comprehensive research portfolio showcases Dr. Shao-Hsien Chen’s expertise in advanced manufacturing technologies, optimization methodologies, and innovative applications in machine tool design. šš§
šĀ Citation Metrics (Google Scholar):
Citations by: All ā 215, Since 2018 ā 158
h-index: All ā 7, Since 2018 ā 7
i10 index: All ā 6, Since 2018 ā6
š PublicationsĀ Top Note :
Study of using cutting chip color to the tool wear prediction
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Tool life and surface integrity when milling inconel 718 with coated cemented carbide tools
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The study of plasma-assisted machining to Inconel-718
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The machine-tool temperature variation measurement and compensation using regression analysis method
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Lifespan of super-alloy Waspaloy cutting tools
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Tool wear prediction using a hybrid of tool chip image and evolutionary fuzzy neural network
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The use of response surface methodology to optimize parameter adjustments in CNC machine tools
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