Assoc. Prof. Dr. Ekrem Ozkaya | Manufacturing processes | Best Industrial Research Award

Associate Research | Faculty of Engineering and Architecture – Department of Mechanical Engineering, Recep Tayyip Erdoğan University | Turkey

Assoc. Prof. Dr. Ing. Ekrem Özkaya is a distinguished mechanical engineering scholar known for advancing high-performance materials, manufacturing processes, and simulation-driven engineering. With extensive industrial and academic experience, he contributes to innovation in material processing, machining optimization, and computational modeling. His academic background spans multiple doctorate degrees in mechanical engineering, informatics, applied mathematics, and fluidics, supported by additional qualifications in energy engineering, product development, and management. Professionally, he has served as a researcher, associate professor, project leader, and head of R&D initiatives, collaborating closely with industry to integrate advanced simulation technologies into manufacturing. His research focuses on FEM, CFD, CAE, FSI, and mathematical optimization for the development and processing of modern aerospace-grade and difficult-to-machine materials, as well as modeling erosion, cavitation, and structural behaviors. His scholarly output includes 37 publications with 524 citations across 371 citing documents and an h-index of 14, reflecting his impact in the field. Recognized for multidisciplinary expertise and leadership in engineering innovation, he has supervised numerous research projects and significantly contributed to advancing industrial R&D through simulation-based methods.

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Assoc. Prof. Dr. Chia-Hung Lai | Machine Learning for Smart Manufacturing | Best Researcher Award

Associate Professor | National Chin-Yi University of Technology | Taiwan

Chia-Hung Lai, Ph.D., is an interdisciplinary researcher whose work bridges intelligent automation, smart manufacturing, and advanced sensing technologies to enhance industrial reliability and technical education. His research integrates deep learning, machine vision, nondestructive testing, and engineering information security, with notable contributions to welding automation, gear defect detection, tool-breakage prediction, and secure engineering data transmission. He has developed innovative AI-driven diagnostic systems using convolutional neural networks, symmetrized dot patterns, discrete wavelet transforms, and current-sensing analytics, enabling high-precision detection of defects in manufacturing processes. His studies also explore VR/AR-based learning systems, reflecting his commitment to advancing industry-aligned technical education through immersive and intelligent technologies. In addition, he has contributed to environmentally sustainable engineering through deep learning approaches for monitoring emissions in industrial operations. His work in information security demonstrates a unique blend of engineering design and cybersecurity through novel applications of steganography in CAD environments. With multiple publications in SCIE-indexed journals and recognition through awards and competitive achievements, he has established a strong research footprint across automation, sensing, and applied AI. He actively contributes to the scholarly community through reviewing roles and by leading numerous industry–academic collaborative projects focused on intelligent systems, advanced diagnostics, and smart manufacturing innovation.

Profiles: Scopus | Google Scholar

Featured Publications

Chien, Y.-C., Wu, T. T., Lai, C.-H., & Huang, Y.-M. (2022). Investigation of the influence of artificial intelligence markup language-based LINE ChatBot in contextual English learning. Frontiers in Psychology, 13, 785752.

Lai, C.-H., Liu, M.-C., Liu, C.-J., & Huang, Y.-M. (2016). Using positive visual stimuli to lighten the online learning experience through in-class questioning. International Review of Research in Open and Distributed Learning, 17(1), 23–41.

Huang, Y.-M., Liu, M.-C., Lai, C.-H., & Liu, C.-J. (2017). Using humorous images to lighten the learning experience through questioning in class. British Journal of Educational Technology, 48(3), 878–896.

Liu, C.-J., Huang, C.-F., Liu, M.-C., Chien, Y.-C., Lai, C.-H., & Huang, Y.-M. (2015). Does gender influence emotions resulting from positive applause feedback in self-assessment testing? Evidence from neuroscience. Journal of Educational Technology & Society, 18(1), 337–350.

Lai, C.-H., Wu, T. E., Huang, S.-H., & Huang, Y.-M. (2020). Developing a virtual learning tool for industrial high schools’ welding course. Procedia Computer Science, 172, 696–700.

Liu, M.-C., Lai, C.-H., Su, Y.-N., Huang, S.-H., Chien, Y.-C., & Huang, Y.-M., & Hwang, J. P. (2015). Learning with great care: The adoption of the multi-sensor technology in education. In Sensing technology: Current status and future trends III (pp. 223–242).

Lai, C.-H., & Yang, H.-C. (2016). Theoretical investigation of a planar rack cutter with variable diametral pitch. Arabian Journal for Science and Engineering, 41(5), 1585–1594.