Dr. K.B. Badri Narayanan
Assistant Professor
Department of Computer Science and Engineering
Email: [email protected]
Educational Qualifications
- Completed B.E. in Mechatronics Engineering from Anna University, India.
- Completed M.E. in Manufacturing Engineering from Government College of Technology, Coimbatore, India.
- Completed Ph.D. in Smart Manufacturing from IIITDM Kancheepuram, India, specializing in type-2 fuzzy logic applications for machining process maintenance and diagnosis.
Bio
Work Experience
Area of Research
Publications
Bio
B. Badri Narayanan received the BEng degree in mechatronics engineering from Anna University, India, the master’s degree in manufacturing engineering from Government College of Technology, Coimbatore, India, the PhD degree in smart manufacturing from the Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India. His research interest includes IoT in manufacturing, specialized in precise monitoring, diagnosis, prediction and maintenance of subtractive machines like turning centers in automated manufacturing industries. My doctoral program is specialized in application of type-2 fuzzy logic in the machining process for predicting the maintenance schedule and diagnosing the risk state of the machine.
Work Experience
Work Experience
- Design Engineer, CAM Engineers, Coimbatore(2008-09)
- Lecturer, Kongu Polytechnic College, Erode(2009-11)
- Asst Prof, Sri Krishna College of Engg, Coimbatore (2013-15)
- Asst Prof, Hindusthan College of Engg, Coimbatore (2015-16)
- Research Scholar, IIITDM Kancheepuram (2016-2022)
- Asst Prof, Amrita Vishwa Vidyapeetham University, Chennai campus (2022-2024)
- Asst Prof, Shiv Nadar University Chennai (2024-Present)
Area of Research
Area of Research
- Predictive Maintenance
- Diagnosing of failures in CNC machines using AI
- Prediction of Machinability parameters
- Mathematical modelling for manufacturing systems
- Path planning for mobile robots
Publications
Publications:
- Prasannavenkadesan, V., Badri Narayanan, K. B., & Raja, S. (2024). Modified Fuzzy Logic System Based Predictive Model for Cortical Bone Drilling Temperature. Fuzzy Information and Engineering, 16(3), 207–219. https://doi.org/10.26599/FIE.2024.9270042
- Badri Narayanan, K. B., & Sreekumar, M. (2019). Modelling and Analysis of Multi-agent Approach for an IoT-Enabled Autonomous Manufacturing System. In Advances in Computational Methods in Manufacturing (pp. 643–653). Springer Singapore. https://doi.org/10.1007/978-981-32-9072-3_54
- Narayanan, B., & Muthusamy, S. (2024). Investigation on the influence of a new trapezoidal-triangular membership function in IT2FLS with type-reductions for a manufacturing application. Journal of Intelligent & Fuzzy Systems, 46(1), 1167–1182. https://doi.org/10.3233/JIFS-231412
- Kumaran, S. S., Chelladurai, S. J. S., Narayanan, K. B. B., & Selvan, T. A. (2024). PREDICTION OF RECEIVED SIGNAL STRENGTH USING THE FUZZY LOGIC CONTROLLER FOR LOCALISATION OF SENSORS IN MOBILE ROBOTS. International Journal of Robotics and Automation, 39(4), 302–311. https://doi.org/10.2316/J.2024.206-1044
- Prasannavenkadesan, V., Naveed Ul Meiraj, S., Narayanan, K. B. B., Prasanth, S. R., & Prasad, A. (2024). 4 Chitosan in orthopedics: current advancements and future prospects. In Sustainable Bio-Based Composites (pp. 59–78). De Gruyter. https://doi.org/10.1515/9783111321530-004
- Badri Narayanan, K. B., & Sreekumar, M. (2022). Diagnosing of Risk State in Subsystems of CNC Turning Center using Interval Type-2 Fuzzy Logic System with Semi Elliptic Membership Functions. International Journal of Fuzzy Systems, 24(2), 823–840. https://doi.org/10.1007/s40815-021-01172-0
- Narayanan, K. B. B., & Muthusamy, S. (2022). Prediction of machinability parameters in turning operation using interval type-2 fuzzy logic system based on semi-elliptic and trapezoidal membership functions. Soft Computing, 26(7), 3197–3216. https://doi.org/10.1007/s00500-022-06831-4
- Narayanan, B., & Sreekumar, M. (2022). Design, modelling, optimisation and validation of condition-based maintenance in IoT enabled hybrid flow shop. International Journal of Computer Integrated Manufacturing, 35(9), 927–941. https://doi.org/10.1080/0951192X.2022.2028011
