Back close

On-Line Condition Monitoring and Chatter Control in Thin-wall Machining Systems Using Machine Learning Algorithms

Project Incharge: Dr. Rameshkumar K.

Co-Project Incharge: Dr. Saravanamurugan S., Dr. Rajan M. A.

Research Scholar: Mr. Viswajith S Nair

Agency & Scheme: TCS Research Scholar Program (Ph D)

Duration: 4 Years (July 2023 – June 2027)

Monthly Stipend: Year 1 & 2 – Rs.70,000, Year 3 – Rs.75,000, Year 4 – Rs. 97,500

Additional Support: Year 1 to 3 – Up to Rs. 2,00,000, Year 4 – Rs. 2,20,000 (General contingency expense and support)

On-Line Condition Monitoring and Chatter Control in Thin-wall Machining Systems Using Machine Learning Algorithms

This research focuses on addressing machining vibration, or chatter, a critical concern in thin-wall machining systems. Chatter adversely affects productivity, dimensional accuracy, and surface quality of machined components. Particularly detrimental to thin-walled components, chatter can adversely affect productivity, tool life, machine health, as well as the dimensional accuracy and surface quality of machined components. The research aims to address this through on-line condition monitoring to accurately identify and predict processing stability, utilizing signals such as cutting force, voltage, acoustic, and acceleration. The research also employs stability lobe diagrams based on regenerative chatter theory to identify chatter-free optimal process parameters. Furthermore, the research applies machine learning models to detect and control chatter during thin-wall machining of difficult-to-cut materials, using the processed signal data for enhanced on-line performance.

Related Projects

Regulation of Inflammasomes by Natural Product extracts:Role in metabolic disorders
Regulation of Inflammasomes by Natural Product extracts:Role in metabolic disorders
Teacher Training for Screening and Supporting Autistic Students in India
Teacher Training for Screening and Supporting Autistic Students in India
Advanced Integrated Wireless Sensor Networks for Real Time Monitoring and Detection of Disasters
Advanced Integrated Wireless Sensor Networks for Real Time Monitoring and Detection of Disasters
Non-Invasive Real-Time Monitoring of Blood Pressure and Blood Glucose through Photoplethysmography leveraging IoMT and AI 
Non-Invasive Real-Time Monitoring of Blood Pressure and Blood Glucose through Photoplethysmography leveraging IoMT and AI 
Quantifying Glacier Retreat and Elevated Lake Growth Using Remote Sensing for Disaster Management in Western Himalaya
Quantifying Glacier Retreat and Elevated Lake Growth Using Remote Sensing for Disaster Management in Western Himalaya
Admissions Apply Now