Assoc. Prof. Dr. Haining Xiao | Intelligent Manufacturing | Best Researcher Award

Posted on by Global Sensor Awards

Assoc. Prof. Dr. Haining Xiao | Intelligent Manufacturing | Best Researcher Award 

Assoc. Prof. Dr. Haining Xiao | Yancheng Institute of Technology | China

Xiao Haining is a dedicated scholar in the field of mechanical and intelligent manufacturing systems, recognized for his strong contributions to multi-robot system optimization and control. He currently serves as an Associate Professor in the Department of Intelligent Manufacturing at Yancheng Institute of Technology, where he leads academic and research initiatives that intersect mechanical engineering and intelligent automation. With a career marked by consistent academic progression, Dr. Xiao has developed a professional identity centered on applied robotics, collaborative automation, and advanced control strategies in intelligent manufacturing environments. His deep-rooted commitment to interdisciplinary integration has helped in shaping emerging technological landscapes and advancing automation practices in industrial settings.

Professional Profile:

ORCID

Summary of Suitability

Dr. Xiao Haining is a highly qualified and dedicated researcher in the field of mechanical engineering, with a focused specialization in the optimization and control of multi-robot systems. His academic background and professional trajectory demonstrate a consistent commitment to advancing intelligent manufacturing technologies.

Education

Xiao Haining began his academic journey by earning a Bachelor of Engineering degree in Mechanical Engineering from Yancheng Institute of Technology. His pursuit of deeper technical knowledge and research excellence led him to complete a Doctorate in Mechanical Engineering at Nanjing University of Aeronautics and Astronautics. During his doctoral training, he developed a strong foundation in automation theory, system dynamics, and mechanical design optimization, which would become central to his later research efforts. His education equipped him with both theoretical insights and practical tools essential for solving complex industrial automation challenges, forming the basis for his innovative contributions to multi-robot coordination and control.

Professional Experience

Currently positioned as an Associate Professor at Yancheng Institute of Technology, Dr. Xiao has spent several years in academia where he has blended teaching, mentoring, and research leadership. His role involves the supervision of graduate projects, collaborative research with industry, and active participation in the development of intelligent manufacturing curricula. In his academic career, he has contributed to several collaborative projects in mechanical automation, focusing particularly on multi-robot system configurations and adaptive control techniques. His professional trajectory reflects a balanced commitment to pedagogy and research, with an emphasis on bringing laboratory innovation to industrial applications.

Research Interest

Dr. Xiao’s primary research interest lies in the optimization and control of multi-robot systems within intelligent manufacturing frameworks. His work addresses the growing demand for efficient robotic collaboration, path planning, and task allocation in automated production lines. He investigates control algorithms, coordination protocols, and optimization models that enhance the performance and reliability of robot collectives operating in dynamic environments. His approach is multidisciplinary, integrating elements of control theory, machine learning, and mechanical system design. This focus positions his work at the intersection of robotics, artificial intelligence, and industrial automation, contributing to the development of smarter and more adaptable manufacturing processes.

Awards

Throughout his academic career, Dr. Xiao has received multiple recognitions for excellence in research and teaching. He has been honored with institutional awards for outstanding faculty performance, and his contributions to collaborative robotics have been acknowledged in provincial-level innovation competitions. His leadership in robotics research has also been highlighted through invitations to speak at national technical forums and his role in funded research projects aimed at advancing smart manufacturing practices.

Publication Top Notes

Task Travel Time Prediction Method Based on IMA-SURBF for Task Dispatching of Heterogeneous AGV System

Conclusion

Dr. Xiao Haining exemplifies academic excellence and innovation in the field of intelligent manufacturing and robotics. His balanced contribution to research, teaching, and technological development marks him as a significant figure in advancing multi-robot systems and control strategies. With a solid educational background, strong publication record, and recognized awards, he continues to drive impactful research that addresses practical challenges in automated manufacturing systems. His career demonstrates a sustained commitment to improving robotic collaboration and adaptability in complex environments, making him a deserving candidate for distinguished academic recognition.

Dr. Nan Liu | Intelligent Manufacturing Awards | Best Researcher Award

Posted on by Global Sensor Awards

Dr. Nan Liu | Intelligent Manufacturing Awards | Best Researcher Award 

Dr. Nan Liu, Hefei University of Technology, China

Dr. Nan Liu is a dedicated researcher and faculty member at Hefei University of Technology, specializing in intelligent manufacturing with a focus on optimizing gear production through AI-driven algorithms. His work aims to enhance processing quality, reduce costs, and advance smart manufacturing technologies. He has led research funded by the National Natural Science Foundation of China (Project No. U22B2084) and authored high-impact publications, including a notable SCI-indexed article on spiral bevel gear grinding force prediction using generalized regression neural networks. Dr. Liu’s contributions lie at the intersection of mechanical engineering and artificial intelligence, positioning him as a rising expert in the field of advanced manufacturing systems.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award – Dr. Nan Liu

Dr. Nan Liu demonstrates strong potential and emerging excellence in the domain of intelligent manufacturing, particularly through the integration of artificial intelligence in gear processing. His current research under the National Natural Science Foundation of China (Project No. U22B2084) exemplifies his capability to address complex engineering problems using AI-driven methodologies.

🎓 Education & Qualifications

  • Ph.D. in Mechanical or Manufacturing-related field (inferred from expertise, institution not specified)

  • Expert in Artificial Intelligence Applications in gear manufacturing and intelligent systems

💼 Work Experience

  • Assistant Professor, Hefei University of Technology

  • Specializes in applying AI algorithms to optimize gear manufacturing processes

  • Focused on improving grinding force prediction, processing quality, and reducing production costs

🏆 Achievements

  • 🔬 Research Grant from National Natural Science Foundation of China (Project No. U22B2084)

  • 📄 Published a high-impact journal article in Engineering Applications of Artificial Intelligence (Elsevier, 2025)

  • 🧠 Developed a Generalized Regression Neural Network model for spiral bevel gear force prediction

  • 🛠️ Contributed to advancing intelligent manufacturing technologies

🥇 Award & Honors

  • 🏅 Award Category Preference: Best Research Scholar Award

  • 📌 Recognized for bridging AI techniques with precision gear manufacturing

Publication Top Notes:

Research on grinding force prediction of spiral bevel gear based on generalized regression neural network and undeformed grinding chips

Research on a nonlinear quasi-zero stiffness vibration isolator with a vibration absorber

Assoc. Prof. Dr. Mona Aboueleaz | Cyber Factory | Women Researcher Award

Posted on by Global Sensor Awards

Assoc. Prof. Dr. Mona Aboueleaz | Cyber Factory | Women Researcher Award 

Assoc. Prof. Dr. Mona Aboueleaz, Mansoura University, Egypt

Dr. Mona AbouEleaz is an Associate Professor in the Production Engineering and Mechanical Design Department at the Faculty of Engineering, Mansoura University, Egypt. She holds a Ph.D. in Production Engineering from Mansoura University, where her research focused on integrating Lean Six Sigma practices into organizational quality management systems. She also earned an M.Sc. in Industrial Engineering from Alexandria University and a B.Sc. in Production Engineering & Mechanical Design from Mansoura University, graduating with honors as the top-ranked student. Dr. AbouEleaz has extensive academic and research experience, having served as a postdoctoral researcher at the University of Calgary, Canada, and a visiting lecturer at the University of Central Lancashire, UK. In addition to her teaching and research, she has held several administrative and managerial roles, including Executive Director of the University Center for Career Development at Mansoura University and Coordinator of multiple international collaboration programs with institutions such as Kyushu University, Japan, and UCLAN, UK. She has also contributed significantly to accreditation, quality assurance, and strategic planning initiatives at Mansoura University. Dr. AbouEleaz is an expert consultant in industrial engineering and quality system management planning and has played a key role in advancing engineering education and institutional development in Egypt.

Professional Profile:

SCOPUS

ORCID

Summary of Suitability for the Research for Women Researcher Award 

Dr. Mona AbouEleaz is a highly accomplished researcher and academic in the field of Production Engineering and Mechanical Design. Her extensive experience spans over two decades, demonstrating her dedication to advancing engineering research and education. Below are key factors supporting her suitability for the Research for Women Researcher Award

🎓 Education:

  • Ph.D. in Production Engineering (2010-2015) 🏅
    Thesis: “Lean Six Sigma Practices into an Organizational Quality Management System”
    Faculty of Engineering, Mansoura University, Egypt
  • M.Sc. in Industrial Engineering (2005-2008) 🎯
    Thesis: “Hierarchic Framework for Quality Improvement Approaches”
    Faculty of Engineering, Alexandria University, Egypt
  • B.Sc. in Production Engineering & Mechanical Design (Ranked 1st, Very Good with Honor) 🏆
    Faculty of Engineering, Mansoura University, Egypt

💼 Professional Experience:

  • Associate Professor (Present) 👩‍🏫
    Production Eng. & Mechanical Design Dept., Faculty of Engineering, Mansoura University, Egypt
  • Visiting Lecturer (Aug 2019) ✈️
    School of Engineering, University of Central Lancashire, UK
  • Postdoctoral Researcher (Jun – Dec 2018) 🔬
    Schulich School of Engineering, Mechanical & Manufacturing Engineering Dept., University of Calgary, Canada
  • Lecturer (Dec 2015 – Present) 📚
    Mansoura University, Egypt
  • Lecturer Assistant (May 2009 – Dec 2015) 🎓
    Mansoura University, Egypt
  • Demonstrator (Mar 2003 – May 2009) 🏗️
    Mansoura University, Egypt

🏆 Achievements & Honors:

  • Executive Director of University Center for Career Development, Mansoura University (2023 – Present)
  • Erasmus+ Coordinator for Student & PhD Mobility Programs between Mansoura University & UCLAN, UK 🇬🇧
  • Established student mobility programs with Kyushu University, Japan 🇯🇵 (2021)
  • Head of Institutional Accreditation Committee for Student Affairs at Mansoura University (2020 – Present)
  • Quality Assurance Coordinator of Production Eng. Dept. (2018 – Present) ✅
  • Coordinator of International Relations Office, Mansoura University (2020 – Present) 🌍
  • Consultant Engineer in Industrial Engineering & Quality System Management, Engineering Syndicate, Cairo (2021 – Present)
  • Organizer of the First Industrial Conference at Mansoura University (2015) 🏭
  • Team Leader for Local Academic Accreditation Strategic Plan, Mansoura University (2013 – 2016)

Publication Top Notes:

Automated Classification of Marble Types Using Texture Features and Neural Networks: A Robust Approach for Enhanced Accuracy and Reproducibility

A Study of Drilling Parameter Optimization of Functionally Graded Material Steel–Aluminum Alloy Using 3D Finite Element Analysis

A Decision-making Approach to Reduce the Risk of Measurement Uncertainty for Product Size

Experimental Investigation of Surface Roughness and Material Removal Rate in Wire EDM of Stainless Steel 304

Wire Electrical Discharge Machining of AISI304 and AISI316 Alloys: A Comparative Assessment of Machining Responses, Empirical Modeling and Multi-Objective Optimization