Organization profile

Organisation profile

The Department of Mechatronics Engineering provides a platform for synergistic integration of multi-disciplinary engineering sciences. The research activity in the department upholds the moto of creativity, improvised usability, innovation and detail to approach in the fields of control and automation, intelligent controllers and soft computing, structural integrity, multi-utility robots and tribology. The inherent traits of multi-engineering accelerate the department growth in collaborative work and in turn aids in expanding our capacity in terms of research. This resource includes comprehensive information of researcher’s publication and activities in various fields.

Fingerprint The fingerprint is based on mining the text of the scientific documents related to the associated persons. Based on that an index of weighted terms is created, which defines the key subjects of research unit

Composite materials Engineering & Materials Science
Journal bearings Engineering & Materials Science
Bearings (structural) Engineering & Materials Science
Friction Engineering & Materials Science
Axial-flow compressors Engineering & Materials Science
Neural networks Engineering & Materials Science
Machining Engineering & Materials Science
friction Physics & Astronomy

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 1999 2018

  • 138 Citations
  • 7 h-Index
  • 24 Article
  • 6 Conference contribution
  • 5 Conference article

An Efficient Approach to Optimize Wear Behavior of Cryogenic Milling Process of SS316 Using Regression Analysis and Particle Swarm Techniques

Karthik Rao, M. C., Malghan, R. L., ArunKumar, S., Rao, S. S. & Herbert, M. A., 30-01-2018, (Accepted/In press) In : Transactions of the Indian Institute of Metals. 72, 1, p. 191-204 14 p.

Research output: Contribution to journalArticle

Regression analysis
Wear of materials
Particle swarm optimization (PSO)
Milling (machining)
1 Citation (Scopus)

A systematic approach to model and optimize wear behaviour of castings produced by squeeze casting process

Patel G C, M., Shettigar, A. K. & Parappagoudar, M. B., 01-04-2018, In : Journal of Manufacturing Processes. 32, p. 199-212 14 p.

Research output: Contribution to journalArticle

Squeeze casting
Wear of materials
Neural networks
Particle swarm optimization (PSO)
Open Access
Bearings (structural)
Journal bearings