| Project Id
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BITSRMIT24101180
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| Project Detail
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| Project Title
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Prediction of friction and wear properties during different material interactions using vibration and acoustic measurement
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| Senior Supervision Team (BITS)
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| Supervisor name and Title
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Arun Kumar Jalan
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School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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|
|
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Email ID
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arunjalan@pilani.bits-pilani.ac.in
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| URL for more info
|
https://www.bits-pilani.ac.in/pilani/arun-kumar-jalan/
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| a) Are you currently supervising a BITS or RMIT HDR student?
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NO
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| Please comment how many you are supervising
|
|
| b) Have you supervised an offshore candidate before?
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NO
|
| If no, what support structures do you have in place?
|
|
| If yes, please elaborate
|
|
| Senior Supervision Team (RMIT)
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| Supervisor name and Title
|
Reza Nakhaie Jazar
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School or Department (or company, if applicable)
|
STEM
|
|
|
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Email ID
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reza.nakahiejazar@rmit.edu.au
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| URL for more info
|
https://www.rmit.edu.au/contact/staff-contacts/academic-staff/n/nakhaie-jazar-professor-reza
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| a) Are you currently supervising a BITS or RMIT HDR student?
|
NO
|
| Please comment how many you are supervising
|
|
| b) Have you supervised an offshore candidate before?
|
NO
|
| If no, what support structures do you have in place?
|
|
| If yes, please elaborate
|
|
| Other Supervisors (BITS)
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| Supervisor name and Title
|
SHARAD SHRIVASTAVA ASSOCIATE PROFESSOR
|
School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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| Phone Number (Optional)
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1596255836
|
Email ID
|
sharad_shrivastava@pilani.bits-pilani.ac.in
|
| URL for more info
|
https://www.bits-pilani.ac.in/pilani/sharadshrivastava/profile
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| Other Supervisors (BITS)
|
| Supervisor name and Title
|
|
School or Department (or company, if applicable)
|
|
| Phone Number (Optional)
|
|
Email ID
|
|
| URL for more info
|
|
| Field of Research (For Codes)
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| 91301 | Acoustics and Noise Control | 30.00 |
| 91304 | Dynamics, Vibration and Vibration Control | 30.00 |
| 91307 | Numerical Modelling and Mechanical Characterisatio | 40.00 |
|
| Project Description
|
|
Phenomena of friction and wear in mechanical contacts are particularly important in the field of dynamic systems. There are different efforts have been made to know their properties in real time but sometime it is very difficult to achieve specially during operations. Again we know friction and wear both are interdependent on noise and vibration but most of the studies treated friction, wear mechanics, acoustic noise and Vibration separately. In view of this there is a requirement to make studies to develop interdependencies between friction, wear, noise and vibration. It may be possible by studying some developed mathematical models of friction and wear and establish the relation with vibration response and acoustic emission.
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| Project Deliverable/Outcomes
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|
• Develop a comprehensive analytical modelling approach to relate the interdependencies of friction, noise, vibration, and wear with mathematical expressions. • Experimental analysis with different materials are in mechanical contacts carried out by simultaneously measures friction, wear, vibration and acoustic emission to validate the analytical model • Use Machine learning techniques and deep learning techniques to train the system and predict the friction and wear properties in real time by simple vibration measurement and or, by acoustic measurement.
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| Research Impact Themes
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| ADVANCED DIGITAL TECHNOLOGIES AND BUSINESS TRANSFORMATION | DEEP LEARNING AND PREDICTIVE MODELLING |
| DESIGN, CREATIVE PRACTICE AND GLOBAL BUSINESS | SOCIAL CONNECTION AND INCLUSION; IMPROVED GLOBAL BUSINESS EFFICIENCIES AND INNOVATION |
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| Which RMIT Sustainable Development Goal (SDG) does your project align to
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INDUSTRY, INNOVATION, AND INFRASTRUCTURE
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| Which RMIT Enabling Impact Platform (EIP) does your project align to
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DESIGN AND CREATIVE PRACTICE
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| Which RMIT Program code will this project sit under?
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DR216 (MECH AND MECHATRONICS)
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| Student Capabilities and Qualifications
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basic experimentation and numerical modelling, MATLAB
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Knowledge of Signal processing, DAQ system
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M.E or, M. Tech
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| Preferred discipline of Student
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| Artificial Intelligence, Deep Learning, Information Extraction & Knowledge Extraction, Machine Learning, Natural Language Processing |
| Mechanical Enineering, Mechanics, Mechatronics, Aerospace Eng, Hypersonics |
|