Name : Dr. K. Karthik
Designation : Assistant Professor
Email ID : karthikmeed@
Mobile : +91 8124400431
Contact at : TTS2103, Assistant Professor,
Department of Mechanical Engineering,
Vel Tech Rangarajan Dr Sagunthala R&D Institute of Sci& Tech.
400 feet Outer Ring Road, Avadi, Chennai - 600 062.TN. India.
EDUCATION
Ph.D. Mechanical Engineering
M.E Engineering Design
RESEARCH INTERESTS
Additive Manufacturing, Nano Composite materials, Materials and machining
FUTURE PROJECTS
Hybrid Nanocomposite
Applications Invited
127
Scopus Publications
Scopus Publications
Catalytic Microwave Pyrolysis of Plastic Waste for Hydrogen and Nanocarbons K. Karthik, Viyat Varun Upadhyay, Puppala Satyavathi, Frederick Sidney Correa, Saravanan Arumugam, et al. Chemical Engineering and Technology, 2026 Microwave‐assisted pyrolysis offers a promising route for converting plastic waste into hydrogen‐rich gas and carbon nanomaterials. This review examines hydrogen evolution, carbon‐forming pathways, catalyst functions, absorber‐support interactions, and reactor constraints in microwave systems. It also assesses process parameters, localized heating, field nonuniformity, and scale‐up challenges. Overall, the process supports circular plastic valorization, but practical deployment depends on catalyst stability, realistic handling of mixed feeds, and transparent techno‐economic and environmental evaluation.
Innovations at the Intersection of Generative Design and Additive Manufacturing: Trends, Challenges, and Future Directions K. Karthik, Ramesh Kumar R, S. Balaguru EPJ Web of Conferences, 2026 The contributions of generative design and additive manufacture will be enormous to innovations in the field of modern engineering and industrial applications. A literature survey of recently published papers to understand major advances in deep learning applications, manufacturing optimization, and industry-derived implementations is presented by the current paper. Besides, it depicts significant progress in two domains of stress-driven generative design for lattice structures and real-time process simulation through digital twins to be developed along with manufacturing aware algorithms. This work points to an increasing direction of the generative design method towards sustainability and bio-inspired approaches. Besides, the study discussed coyote issues in standardization, reliability assessment, and mass production implementations. Hence, this review will be a great source of information for the researchers and the practitioners and also serve as an opening of new and promising research avenues in generative design for additive manufacturing.
Deep Learning-Based Early Detection of Autism in Children Mandava Venudhar, K. Karthik, Sankar Ganesh Karuppasamy, Sajeev Ram Arumugam, V Jaganraja, et al. Proceedings of 8th International Conference on Intelligent Sustainable Systems Iciss 2026, 2026