Dr Malarvizhi Kaniappan Chinnathai

Dr Malarvizhi Kaniappan Chinnathai

Malarvizhi K. Chinnathai is a Lecturer in the School of Computer Science and Engineering and joined the University of Westminster during March 2022. She previously worked as a Research Fellow in the Brunel Innovation Centre, Brunel University before which she worked as a Project Engineer (Digital Manufacturing) in the Automation Systems Group (ASG) at the Warwick Manufacturing Group (WMG), University of Warwick. She graduated from the University of Warwick with a PhD in Engineering (2021) and an MSc in Manufacturing Systems Engineering (2015)

Malarvizhi has submitted numerous peer-reviewed conference proceedings and journal articles, and is performing research in the areas of manufacturing system design, electric vehicles (EV), EV battery assembly, applied machine learning and artificial intelligence, machine vision, image analysis, mathematical optimization, and operations research. She has worked as a researcher on various internationally teamed EPSRC and Innovate-UK funded projects such as: AI6S, ATTIC, AMPLiFII, H1PERBAT, DIGIMAN, etc. She previously worked as a guest editor for the special issue on AI-integrated Advanced Robotics towards Industry 5.0 in the open access journal by MDPI named 'Machines'. 

List of past publications are as follows:


Hasan, N., Webb, L., Chinanthai, M.K., Hossain, M.A.A., Ozkat, E.C., Tokhi, M.O. and Alkan, B., 2023, October. Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network. In Climbing and Walking Robots Conference (pp. 323-335). Cham: Springer Nature Switzerland.


Chinnathai, M.K. and Alkan, B., 2023. "A digital life-cycle management framework for sustainable smart manufacturing in energy intensive industries", Journal of Cleaner Production, p.138259.


 1) C. Ferri, M. K. Chinnathai, R. Titmarsh, H. Abdelaziz , "Image segmentation of micro-TIG battery welds",  IEEE International Conference on Automation and Computing (ICAC) :1-6


1) M. K. Chinnathai, B. Alkan, R. Harrison, “A Novel Data‑driven Approach to Support Decision‑Making during Production Scale‑up of Assembly Systems.”, Elsevier ‑ Journal of Manufacturing Systems, 59: 577 ‑ 595.

2) B. Alkan, M. K. Chinnathai, "Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components", Machines 9 (12): 341.


1) Fadi Assad, Bugra Alkan, M. K. Chinnathai, Mus’ab H Ahmad, Emma J Rushforth, Robert Harrison, “A framework to predict energy related key performance indicators of manufacturing systems at early design phase.”, Procedia CIRP‑CMS, 81: 145‑150. 

2)  M. K. Chinnathai, Zeinab Al‑Mowafy, Bugra Alkan, Daniel Vera, Robert Harrison, “A framework for pilot line scale‑up using digital manufacturing.”, Procedia CIRP‑CMS, 81: 962‑967.


1) M. K. Chinnathai, Bugra Alkan, Daniel Vera, Robert Harrison, “Pilot to full‑scale production: A battery module assembly case study ”, Procedia CIRP‑CMS, 72: 796‑801. 

2) Dominik Kohr, Mussawar Ahmad, Bugra Alkan, M. K. Chinnathai, Lukas Budde, Daniel Alexandre Vera, Thomas Friedli, Robert Harrison, “Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data‑centric and Human‑centric Approaches.”, COMPLEXIS, 86‑93. 


1) M. K. Chinnathai, Till Günther, Mussawar Ahmad, Cosima Stocker, Lukas Richter, David Schreiner, Daniel Vera, Gunther Reinhart, Robert Harrison, “An application of physical flexibility and software reconfigurability for the automation of battery module assembly.”, Procedia CIRP‑CMS, 63: 604‑609.

2) M. K. Chinnathai, Bugra Alkan, Robert Harrison, “Convertibility evaluation of automated assembly system designs for high variety production.”, Procedia CIRP‑Design, 60: 74‑79. 

3) Bugra Alkan, Daniel Vera, M. K. Chinnathai, Robert Harrison, “Assessing complexity of component‑based control architectures used in modular automation systems.”, International Journal of Computer and Electrical Engineering, 9: 393‑408. 

Sustainable Development Goals
In brief

Research areas

Applied Artificial Intelligence, Convolutional Neural Networks, Sustainable Smart Manufacturing and Robotics and Automation