Dr Rohit Kshirsagar
Advanced Manufacturing Research Centre
Technical Fellow for Battery Assembly
Full contact details
Advanced Manufacturing Research Centre
Samlesbury Enterprise Zone
Roy Chadwick Way, Mellor Brook
Blackburn
BB2 7HP
- Profile
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Rohit completed his PhD in 2019 from Coventry University, UK focusing on the application of artificial intelligence to optimize welding processes. During his PhD, he implemented various algorithms for the prediction and reverse optimisation of properties of the welds made in austenitic stainless steels, specifically for aerospace applications. Apart for the development of the AI tools for prediction and optimisation, he also has a string background in material science and metallurgy involving various alloys. More recently, Rohit is working on developing sustainable batteries that utilise a reversible joining technique to non-destructively recover components at the end-of-life. Previously, Rohit has completed MSc in mechanical engineering from Coventry University, UK and BEng in mechanical engineering from Pune University, UK.
Rohit joined AMRC in 2020 in the Batteries and Automation team based at the Northwest facility. He leads activities related to development of novel techniques to assemble battery packs for various energy storage applications. He has a strong research and industrial background having previously worked for reputed organisations such as GKN Hybrid Power (as a RA from Coventry University), Larsen and Toubro Ltd., and Bosch India Ltd.
- Research interests
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- Similar and dissimilar metal joining
- Optimisation of laser and arc based welding processes
- Laser applications for surface engineering
- Application of AI for process optimisation
- Development of in-process monitoring techniques
- Publications
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Journal articles
- A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data. Scientific Reports, 13. View this article in WRRO
- Machine learning-based prediction and optimisation system for laser shock peening. Applied Sciences, 11(7).
- Optimization of TIG welding parameters using a hybrid nelder mead-evolutionary algorithms method. Journal of Manufacturing and Materials Processing, 4(1). View this article in WRRO
- Measurement of ferrite content of stainless steel sheet welds using a new Ferrite Density Number scale. Journal of Materials Processing Technology, 274, 116278-116278.
- Prediction of bead geometry using a two-stage SVM–ANN algorithm for automated tungsten inert gas (TIG) welds. Journal of Manufacturing and Materials Processing, 3(2). View this article in WRRO
- Effect of the Addition of Nitrogen through Shielding Gas on TIG Welds Made Homogenously and Heterogeneously on 300 Series Austenitic Stainless Steels. Journal of Manufacturing and Materials Processing, 5(3), 72-72.
Preprints
- A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data, Research Square Platform LLC.
- A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data. Scientific Reports, 13. View this article in WRRO
- Grants
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SUstainable Reversal oF metallic Adhesive Connections Tailored for Augmenting Net Zero Transportation (SURFACTANT) £2.2 mil, ~1mil to AMRC - Co-Investigator funded by EPSRC. This project is expected to start in March 2025 and will focus on the development of a reversible joining technique that can be used to non-destructively disassemble battery pack at the end of their life. The project is in collaboration with Heriot-Watt University and University of West of Scotland
- Professional activities and memberships
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- Lead from AMRC to the Associated of Industrial Laser User (AILU)
- Member of the HVMC T-SPG group