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Due to the demands of Computer-integrated manufacturing (CIM), tool condition monitoring system, as a major component of CIM, is essential to improve the production quality, optimize the labor and maintenance costs, and minimize the manufacturing losses with the increase in productivity. After joining the Mechanical Engineering department at RIT, my first area of research interest is to develop a flexible, cost-effective, accurate tool condition monitoring system. Our research path aims to open up a new research field to unify the theory of machining mechanics, advanced sensing technology, machine learning techniques, and predictive analytics into machining monitoring applications for the ultimate realization of a practical product which can be applied in actual machining production.
Selected Publications:
• Kothuru, Achyuth, Sai Prasad Nooka, and Rui Liu. "Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling." The International Journal of Advanced Manufacturing Technology 95 (2018): 3797-3808.
• Kothuru, Achyuth, Sai Prasad Nooka, and Rui Liu. “Application of deep visualization in CNN-based tool condition monitoring for end milling.” Procedia Manufacturing 34 (2019): 995-1004.
• Liu, Rui, Achyuth Kothuru, and Shuhuan Zhang. "Calibration-based tool condition monitoring for repetitive machining operations." Journal of Manufacturing Systems 54 (2020): 285-293.
The objective of this research area is to fundamentally understand the roles and relationships of human sensory perception, cognition, and knowledge in machining operations and better integrate machinists into various machining environments. The knowledge acquired from this study is expected to impact the entire machining industry to enable a human-centered and human-machine interactive working mode.
Selected Publications:
• Kothuru, Achyuth, Sai Prasad Nooka, and Rui Liu. "Application of audible sound signals for tool wear monitoring using machine learning techniques in end milling." The International Journal of Advanced Manufacturing Technology 95 (2018): 3797-3808.
• TRAK LPM vertical CNC milling center with a Modbus-RS485 communication module;
• Tool Monitoring Adaptive Control system, TMAC (×2);
• Tobii Pro Glasses third-generation wearable eye tracker with integrated analysis platform, iMotions;
• Motion tracking system with multiple cameras (GoPro×3);
• Virtual reality headset Oculus Quest 2;
• Virtual reality headset HTC VIVE Pro 2;
• Audio recording system including condenser recording microphones, uni-directional dynamic instrument microphones, and Steinberg UR44 USB 2.0 audio interface;
• PC-based data acquisition system;
• One workstation with CPU of Intel-core i7-6700K quad core processor with 4.0 GHz and GPU of Geforce GTX 1080 Ti will be dedicated to process acquired data
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