Lecturer and researcher at HAUQIAO UNIVERSITY, China
Dr. Aliakbar Emamverdian is a dedicated mechanical engineering scholar with a strong academic and research background in manufacturing and automation. Born in February 1984, he currently serves as a lecturer and researcher at Huaqiao University, China. His career spans international institutions including Nanjing University of Science and Technology and Eastern Mediterranean University. Dr. Emamverdian’s expertise includes metal forming, material characterization, failure analysis, and life prediction, with a particular focus on integrating advanced tools like optical scanning and neural network modeling into traditional manufacturing processes. He has co-authored several peer-reviewed journal articles and a technical book on competency design in manufacturing systems. Dr. Emamverdian is multilingual, proficient in Farsi, English, and Turkish, and actively collaborates with leading researchers from institutions such as Imperial College London and Politecnico di Bari. His commitment to scientific innovation, academic teaching, and international collaboration defines his contributions to mechanical engineering.
Professional Profile
Education🎓
Dr. Emamverdian began his academic journey with a Bachelor of Science degree in Mechanical Engineering from Islamic Azad University in 2007. Motivated by a passion for innovation and precision engineering, he pursued a Master of Science in Mechanical Engineering with a specialization in Manufacturing at Eastern Mediterranean University, completing it in February 2013. His academic trajectory culminated with a Ph.D. in Mechanical Engineering, Manufacturing, and Automation from the prestigious Nanjing University of Science and Technology in China, awarded in February 2023. His doctoral research emphasized simulation-based analysis, microstructural evolution, and neural network modeling for predicting material degradation in metal forming processes. Through this diverse educational background, Dr. Emamverdian developed a robust foundation in advanced manufacturing techniques, computational modeling, and experimental validation. His academic achievements reflect a blend of theoretical knowledge and practical problem-solving skills that empower his teaching and research in cutting-edge engineering disciplines.
Professional Experience📝
Dr. Aliakbar Emamverdian has amassed a wealth of international academic experience over the past decade. Currently, he is a lecturer and researcher at Huaqiao University (HQU), China, where he has been contributing to the Mechanical Engineering Department since September 2023. Prior to this, he served as a research assistant at Nanjing University of Science and Technology (NJUST), China, from September 2016 to June 2019, where he was actively engaged in simulation-based material research and experimental validation. From January 2013 to June 2016, Dr. Emamverdian worked as an assistant in the laboratory at Eastern Mediterranean University (EMU), Cyprus, where he supported academic courses and participated in experimental mechanics. His professional background spans teaching, laboratory assistance, and advanced research roles, reflecting a consistent commitment to academic excellence and international collaboration. His roles have allowed him to work on multi-disciplinary projects involving simulation, manufacturing technologies, and intelligent systems.
Research Interest🔎
Dr. Emamverdian’s research interests lie at the intersection of mechanical engineering and advanced manufacturing technologies. He specializes in metal forming, failure analysis, non-destructive testing, and life prediction of mechanical components. A key aspect of his research involves studying material behavior under thermal and mechanical stress during hot forging, particularly focusing on H21 steel dies. His recent work explores the use of optical scanning, finite element simulation, and microstructural analysis to predict degradation mechanisms in forging dies. Furthermore, he integrates neural network modeling and intelligent algorithms to enhance the predictive capabilities of mechanical systems. Dr. Emamverdian is also interested in the material characteristics of alloys and their responses to complex loading conditions. His interdisciplinary research contributes significantly to improving the durability and performance of manufacturing tools and supports the advancement of smart manufacturing systems. His approach combines theoretical analysis, experimental work, and computational intelligence.
Award and Honor🏆
While Dr. Emamverdian’s profile does not list specific personal awards or honors to date, his growing recognition is evident through his collaborations with high-ranking institutions and publication in reputable international journals. His research has appeared in Journal of Materials Research and Technology, Engineering Failure Analysis, and Journal of Visualization, highlighting the academic community’s trust in his work. He has worked alongside distinguished researchers from Imperial College London, University of Strathclyde, and Politecnico di Bari—an indication of his emerging prominence in the global mechanical engineering research community. His book publication on manufacturing system modeling, authored early in his career, showcases his long-standing commitment to research excellence. Continued international academic appointments further signify the respect and demand for his expertise. With ongoing high-quality research and impactful collaborations, Dr. Emamverdian is poised to receive formal accolades and awards recognizing his innovative contributions to mechanical engineering and manufacturing science.
Research Skill🔬
Dr. Emamverdian possesses an extensive portfolio of research and technical skills essential for modern mechanical engineering. He is proficient in advanced simulation tools like ABAQUS, DEFORM, and SIMUFACT FORMING, which he uses for stress analysis and die wear prediction. His modeling expertise includes CATIA V5 and SOLIDWORKS for mechanical design. For data analysis and intelligent systems, he employs MATLAB, particularly neural networks and fuzzy logic algorithms. Additionally, his hands-on experience with EBSD (Channel 5) and SEM techniques enhances his material characterization work. Dr. Emamverdian is also skilled in optical scanning and surface mapping using POLYWORKS, which supports his work in non-destructive evaluation and life prediction of industrial tools. His ability to combine computational, experimental, and analytical methods allows him to solve complex problems in metal forming and manufacturing. These research capabilities underpin his innovative approaches to failure analysis and smart manufacturing technologies.
Conclusion💡
Dr. Aliakbar Emamverdian demonstrates strong qualifications and innovative contributions in mechanical engineering and advanced manufacturing. His research spans experimental and simulation-based approaches, enriched by AI-driven analysis, and he collaborates with prestigious institutions globally. His work on failure analysis, die degradation, and metal forming simulation is both industrially relevant and academically rigorous.
While his profile could benefit from additional publication metrics, research funding leadership, and broader recognition, his technical depth, publication quality, and international collaborations make him a compelling candidate for the Best Researcher Award, particularly in the engineering and manufacturing domain.
Publications Top Noted✍
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Title: Current failure mechanisms and treatment methods of hot forging tools (dies) – A review
Authors: AA Emamverdian, Y Sun, C Cao, C Pruncu, Y Wang
Year: 2021
Citations: 72
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Title: Deformation and wear in a H21 (3Cr2W8V) steel die during hot forging: Simulation, mechanical properties, and microstructural evolution
Authors: AA Emamverdian, Y Sun, C Chunping
Year: 2021
Citations: 22
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Title: The interaction of vortices induced by a pair of microjets in the turbulent boundary layer
Authors: MJ Pour Razzaghi, C Xu, A Emamverdian
Year: 2021
Citations: 7
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Title: Prediction of the main degradation mechanisms in a hot forging steel die: Optical scanning, simulation, microstructural evolution, and neural network modeling
Authors: A Emamverdian, C Pruncu, H Liu, A Rahimzadeh, L Lamberti
Year: 2025
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Title: Corrigendum to “Deformation and wear in a H21 (3Cr2W8V) steel die during hot forging: Simulation, mechanical properties, and microstructural evolution”
Authors: AA Emamverdian, Y Sun, C Chunping
Year: 2022
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Title: Design of a competency-based information and knowledge model for a manufacturing system: Case study EMU CIM Lab
Author: AA Emamverdian
Year: 2013