Postgraduate at Zhejiang University, China
Taoyong Lin is an emerging researcher in the field of mechanical engineering, specializing in intelligent manufacturing, digital twin systems, and data-driven process optimization. With a strong foundation in both theoretical principles and hands-on applications, he has contributed to cutting-edge research in spatial metal tube bending, integrating deep learning models with multi-objective evolutionary algorithms. His work demonstrates a rare balance of computational intelligence and mechanical innovation, as evidenced by first-author publications in high-impact Q1 journals and successful implementation of industry-oriented systems. Beyond academic excellence, Taoyong has shown leadership through project management roles, software development, and mentoring. His ability to convert abstract models into practical tools—such as springback compensation systems and parametric design platforms—sets him apart as a future leader in digital manufacturing. He is also committed to community engagement, having participated in volunteer teaching and student leadership roles. Taoyong continues to pursue research that blends engineering insight with societal and industrial impact.
Professional Profile
Education🎓
Taoyong Lin began his academic journey at Central South University, a prestigious Double First-Class institution under Project 985 and 211, where he earned his Bachelor’s degree in Mechanical Design, Manufacturing and Automation (Mechatronics Engineering). He graduated in June 2023 with a weighted GPA. His coursework included over 30 subjects scoring above 90, such as Electrical Engineering, Robotics, Machine Vision, and Mechanical Control Engineering. He passed both CET-4 and CET-6 English proficiency tests and developed proficiency in reading technical literature and academic writing in English. Currently, he is pursuing a Master’s degree in Mechanical Engineering at Zhejiang University. His graduate studies focus on intelligent optimization and digital manufacturing, allowing him to further integrate computational models with advanced mechanical systems, reinforcing his commitment to interdisciplinary research and academic excellence.
Professional Experience📝
Taoyong Lin has developed a well-rounded portfolio of research and professional experience focused on intelligent mechanical systems, digital twin platforms, and smart manufacturing. As the first author, he led the design and implementation of a sparse expert decomposition framework and X-MOEA algorithm for optimizing variable curvature tube bending, with a paper submitted to a top-tier journal (IF = 9.4). He also co-authored an LSTM-based axial prediction model for spatial bending, applying advanced tuning techniques like AWPSO. As a core developer, he contributed to a provincial-level project building a digital twin system for tube component bending, including UI design and subsystem development in MATLAB and SolidWorks. His innovation has led to one granted utility patent and one invention patent under review. Additionally, he has held leadership roles in national innovation programs, developed temperature-controlled dispersion devices, and led multiple university-level design and modeling projects. His experience bridges academic research with practical, real-world engineering solutions.
Research Interest🔎
Taoyong Lin’s research interests lie at the intersection of intelligent manufacturing, mechanical system optimization, and computational intelligence. He focuses on combining deep learning models (such as LSTM and CNN) with multi-objective evolutionary algorithms to enhance precision and efficiency in metal tube bending processes. His work seeks to solve real-world manufacturing challenges—such as springback compensation, curvature prediction, and process parameter tuning—through data-driven, interpretable models. In addition, he has a strong interest in digital twin systems, aiming to create real-time, virtual replicas of complex forming systems that enable simulation, optimization, and process control. His recent projects also explore frequency-domain analysis, neuroevolution, and process simulation, allowing him to develop intelligent frameworks for highly nonlinear mechanical operations. Taoyong is committed to advancing the digital transformation of mechanical engineering by integrating mechanical design with artificial intelligence, thereby contributing to smart factory initiatives, human-machine collaboration, and sustainable manufacturing technologies.
Award and Honor🏆
Taoyong Lin has received multiple prestigious awards and honors recognizing both his academic excellence and innovative capabilities. At Central South University, he was twice named an Excellent Student and received the Huang Qianheng Foundation Scholarship for outstanding academic performance. He earned Second-Class Scholarships for two consecutive years and was honored as an Excellent Graduate upon completing his bachelor’s degree. His innovation was also recognized through top placements in national and university-level competitions. He won the 2nd Prize in the Mechanical Design Competition during CSU’s “Xuanji Tech Festival” and received the S Award in the Mathematical Contest in Modeling (MCM). Additional accolades include the 3rd Prize in CSU’s Mathematical Modeling Contest and an Excellence Award in the “Huashu Cup” National College Mathematical Modeling Competition. These awards reflect his strong academic foundation, creative problem-solving ability, and leadership in research and innovation at both institutional and national levels.
Research Skill🔬
Taoyong Lin possesses a comprehensive set of research skills that span multiple domains of engineering, computation, and experimental design. His technical proficiency includes advanced deep learning techniques (LSTM, CNN using PyTorch), intelligent optimization algorithms (PSO, MOEA/D, NSGA-III), and finite element analysis using Abaqus. He is highly skilled in 3D modeling and CAD software such as SolidWorks and AutoCAD, and in simulation tools like Simulink. Taoyong is proficient in multiple programming languages, including Python, C++, MATLAB, and C#, enabling him to design custom neural networks, develop GUIs, and run computational simulations. His practical engineering abilities are complemented by experience with hardware development platforms like Arduino, STM32, and PLC systems. Additionally, he is fluent in academic writing using LaTeX, and skilled in data analysis tools such as Origin, Pandas, and SciPy. This diverse skillset empowers him to tackle interdisciplinary challenges and deliver innovative, high-impact research outcomes.
Conclusion💡
✅ Taoyong Lin is a highly suitable and promising candidate for the Best Researcher Award, especially in the context of early-career researchers or master’s-level recognition.
He showcases:
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Technical excellence,
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Original contributions,
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Innovation-driven mindset, and
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A well-rounded portfolio of leadership, social responsibility, and academic rigor.
Publication Top Noted✍️
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Title: Spatial Spiral Tube Multi-Roller Bending: Accurate Axial Prediction Utilizing AWPSO-FECAM-LSTM Framework
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Authors:
Zili Wang, Yonglin Tao, Shuyou Zhang, Xiaojian Liu, Yaochen Lin, Liangyou Li, Jianrong Tan, Zheyi Li
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Journal: Expert Systems with Applications
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Publisher: Elsevier
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Publication Type: Journal Article
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Publication Date: January 16, 2026
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Volume/Issue: [Not specified]
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DOI: 10.1016/j.eswa.2025.128960
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ISSN: 0957-4174
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Indexed In: SCI (Q1 Journal), Elsevier
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Primary Focus: Axial prediction in multi-roller bending using a hybrid model combining Adaptive Weight PSO (AWPSO), Frequency-Enhanced Channel Attention Mechanism (FECAM), and Long Short-Term Memory (LSTM) neural networks.
