Assist. Prof. Dr. Mohanned M. H. AL-Khafaji | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Mohanned M. H. AL-Khafaji | Artificial Intelligence | Best Researcher Award

Engineering | University of Technology | Iraq

Dr. Mohanned Mohammed Hussein Al-Khafaji is an accomplished researcher and academic leader in production engineering, specializing in intelligent manufacturing systems, laser material processing, neural network modeling, and fuzzy logic control applications. As Dean of the College of Production Engineering and Metallurgy at the University of Technology, Baghdad, his research integrates computational modeling, automation, and artificial intelligence to enhance production efficiency and precision engineering. He has made significant contributions to the development of computer-controlled manufacturing systems, laser-based material processing, and predictive modeling using advanced algorithms. His work on CO₂ laser processing, neural network-based machining analysis, and hybrid intelligent systems has advanced industrial automation and smart manufacturing processes. Dr. Al-Khafaji’s research also explores mechatronics, robotic systems, and additive manufacturing, emphasizing simulation tools like Abaqus, COMSOL Multiphysics, and MATLAB. His scientific output reflects substantial academic influence, with 15 Scopus-indexed documents, 41 citations from 37 documents, and an h-index of 3. On Google Scholar, he has accumulated 125 citations, an h-index of 6, and an i10-index of 4, underscoring his growing impact in engineering research.

Profile

Scopus | ORCID | Google Scholar

Featured Publications

Al-Khafaji, M. M. H., & Hubeatir, K. A. (2021). CO2 laser micro-engraving of PMMA complemented by Taguchi and ANOVA methods. Journal of Physics: Conference Series, 1795(1), 012062.

Al-Khafaji, M. M. H. (2018). Neural network modeling of cutting force and chip thickness ratio for turning aluminum alloy 7075-T6. Al-Khwarizmi Engineering Journal, 14(1), 67–76.

Khayoon, M. A., Hubeatir, K. A., & Al-Khafaji, M. M. (2021). Laser transmission welding is a promising joining technology technique – A recent review. Journal of Physics: Conference Series, 1973(1), 012023.

Momena, T. F. A., Mohammed, M. M. H., & Al-Khafaji, M. M. H. (2023). Smart robot vision for a pick and place robotic system. Engineering and Technology Journal, 40(6), 1–15.

Shaker, F., Al-Khafaji, M., & Hubeatir, K. (2020). Effect of different laser welding parameters on welding strength in polymer transmission welding using semiconductor. Engineering and Technology Journal, 38(5), 761–768.*

Matthias Schmitz | Autonomous Systems | Best Researcher Award

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Mr. Matthias Schmitz | Autonomous Systems | Best Researcher Award

Mr. Matthias Schmitz | Ulm University | Germany

Matthias Schmitz is a graduate student in Computational Science and Engineering at the University of Ulm, Germany. His academic journey spans multiple international exchange programs, and he has contributed to research in areas ranging from autonomous vehicle control and SLAM-based trajectory planning to high-performance computing and algorithmic development. He combines his interdisciplinary background in computer science, mathematics, engineering, and mechanical systems to pursue innovation in autonomous systems and computational modeling, working under esteemed advisors across Europe and the U.S.

Publication Profile

ORCID

Education Background

Matthias completed a Bachelor of Science in Mechanical Engineering (Mechatronics) at the University of Duisburg-Essen. He is currently pursuing a Master in Computational Science and Engineering at the University of Ulm (in cooperation with Ulm University of Applied Sciences). Along the way, he enriched his studies through exchange semesters at the University of Bordeaux (Computer Science), the University of Memphis (Machine Learning, Neural Networks, Control Systems, CAD), and the University of Padua (Computer Vision, Robotics, Control), gaining foundational skills in French and Italian.

Professional Experience

Matthias has served as a student researcher and intern in both academic and industry environments. At Ulm University and Ulm University of Applied Sciences, he contributed to autonomous vehicle control projects involving SLAM, LiDAR, and prototype systems. He has interned at IMS Messsysteme GmbH, acquiring machining and manufacturing experience, and at Sakthi Automotive Group in Detroit, applying FEA and material analysis to optimize cast aluminum specimens. In Duisburg, he led force sensor development and prototyping as a student employee, leveraging CAD and microcontroller systems.

Awards and Honors

Matthias has been awarded numerous academic scholarships and fellowships: the University of Ulm Scholarship, additional mobility grants from the Foundation of the German Economy (sdw) for his stays in Bordeaux, Memphis, and Padua, the Erasmus scholarship from the University of Duisburg, and a Deutschlandstipendium from the University of Duisburg-Essen. He also received a Study Fellowship from the Employers’ Association Ruhr/Westfalen. His recognition underscores both scholarly merit and commitment to international experience.

Research Focus

Matthias’s research centers on development and control of agile autonomous systems, computational algorithms for high-performance and parallel computing, and applications of computer vision and robotics. His projects include real-time SLAM and trajectory planning for the Nimbulus-e platform, Galois group computation, optimization algorithms like the Sparrow Search, FEM-based Poisson equation solvers, robotic control via Lego Mindstorms, and hand-segmentation using deep learning in computer vision.

Publication

Conclusion

Drawing on a strong foundation in engineering, computer science, and applied mathematics, Matthias Schmitz embraces interdisciplinary research and international collaboration. His diverse academic experiences, combined with practical skills in algorithm design, prototype development, and sensor-based autonomy, position him to contribute innovatively to the fields of autonomous systems and computational engineering.