The Smart Manufacturing Research Group (SMRG), which was established at Rochester Institute of Technology in 2018, mainly focuses on investigating the practical problems in manufacturing processes on the fundamental level and expanding our research scope by exploring the new research topics and introducing the state-of-the art technologies.
The SMRG works on a wide range of research topics, including machining simulation, tool condition monitoring, tribology, soft material cutting, and gear manufacturing.
The SMRG is always looking for both undergraduate and graduate students who are interested in manufacturing processes, data-intensive computing, and material characterization to join us. We have several awesome ongoing projects which have been elaborated in the research page.
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• Zhang, Shuhuan, Changfeng Ge, and Rui Liu. “Mechanical Characterization of Polydimethylsiloxane (PDMS) Substrate for Wearable Strain Sensors.” Sensors and Actuators A: Physical 341 (2022):113580.
• Liu, Rui. "An edge-based algorithm for tool wear monitoring in repetitive milling processes." Journal of Intelligent Manufacturing (2022): 1-11.
• Chen, Xinye, Shuhuan Zhang, Yu Gan, Rui Liu, Ruo-Qian Wang, and Ke Du. “Understanding microbeads stacking in deformable Nano-Sieve for Efficient plasma separation and blood cell retrieval.” Journal of Colloid and Interface Science 606 (2022): 1609-1616.
• Liu, Rui, Chao Peng, Yunbo Zhang, Hannah Husarek, and Qi Yu. "A survey of immersive technologies and applications for industrial product development.” Computers & Graphics (2021)..
• Stuhr, Benjamin, and Rui Liu. "A Flexible Similarity-Based Algorithm for Tool Condition Monitoring.” Journal of Manufacturing Science and Engineering 144, no. 3 (2021): 031010.
• Liu, Rui, Achyuth Kothuru, and Shuhuan Zhang. “Calibration-based tool condition monitoring for repetitive machining operations.” Journal of Manufacturing Systems 54 (2020): 285-293.
• Guo, Hong, Fanghua Chen, Rui Liu, and Patricia Iglesias. “Lubricating Ability of Magnesium Silicate Hydroxide-Based Nanopowder as Lubricant Additive in Steel-Steel and Ceramic-Steel Contacts.” Tribology Transactions 63, no. 4 (2020): 585-596.
• Li, Zhixiong, Rui Liu, and Dazhong Wu. “Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning.” Journal of Manufacturing Processes 48 (2019): 66-76.
• 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.
• Kothuru, Achyuth, Sai Prasad Nooka, and Rui Liu. “Audio-based tool condition monitoring in milling of the workpiece material with the hardness variation using support vector machines and convolutional neural networks.” Journal of Manufacturing Science and Engineering 140, no. 11(2018).