Arul Sanjivi
Amrita Vishwa Vidyapeetham Amrita School of Engineering, Amrita Vishwa Vidyapeetham
About
Dr. Arul completed his B.E degree in Mechanical Engineering from Coimbatore Institute of Technology, Coimbatore, affiliated to Madras University, India. He completed his M.S in Systems and Information from Birla Institute of Technology and Science, Pilani, Rajsathan, India. Presently he is an Associate Professor in the Department of Mechanical Engineering, Amrita School of Engineering, at Amrita Vishwa Vidyapeetham, Coimbatore- 641112, India. His research interests are Surface Alloying, Modelling and Simulation, Welding and Materials Development.
Employment
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Amrita Vishwa Vidyapeetham Amrita School of Engineering Honorary Professor2020 - Present
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Amrita Vishwa Vidyapeetham Associate Professor1997 - 2020
Education
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Amrita Vishwa Vidyapeetham Ph.D.? - 2013
Projects & Funding
Projects & funding information is unavailable.
Publications (45)
- Influence of Soaking Duration in Deep Cryogenic and Heat Treatment on the Microstructure and Properties of Copper Save
- Enhancing Electrical Conductivity of Commercially Pure Aluminium via Deep Cryogenic Treatment and Subsequent Annealing Save
- Effect of Cryogenic Treatment on Mechanical Properties of Aluminium Alloy AA2014 Save
- Improving Surface Hardness of EN31 Steel by Surface Hardening and Cryogenic Treatment Save
- Influence of Homogenization Temperature on Mechanical Properties from Outer to Inner Zone of Al–Cu–Si Alloy Castings Save
- Study on the Effect of GTA Surface Melting and SiC Reinforcement on the Hardness, Wear and Corrosion Properties of AA 5086 Save
- Study of hardness and wear behavior of surface modified AA 7075 with tungsten carbide using GTA as a heat source Save
- Optimization of Squeeze Casting Process Parameters Using Taguchi in LM13 Matrix B<inf>4</inf>C Reinforced Composites Save
- Investigating the Effect of WC on the Hardness and Wear Behaviour of Surface Modified AA 6063 Save
- Effect of surface modification using gtaw as heat source and cryogenic treatment on the surface hardness and its prediction using artificial neural network Save