Fangzhou Xia

@inproceedings{2023ICRA,

title = {Robotic Method and Instrument to Efficiently Synthesize Faulty Conditions and Mass-Produce Faulty-Conditioned Data for Rotary Machines},

author = {Yip Fun Yeung* and Fangzhou Xia and Juliana Covarrubias and Mikio Furokawa and Takayuki Hirano and Kamal Youcef-Toumi},

url = {https://ieeexplore.ieee.org/document/10161055},

year  = {2023},

date = {2023-05-29},

urldate = {2023-05-29},

booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},

abstract = {Condition synthesis is vital for generating data for fault detection and diagnosis studies. Traditional methods rely heavily on human labor. This study proposes a robotic method and its instrument to efficiently synthesize faulty conditions and mass-produce data to develop fault detection and diagnosis algorithms. The first contribution is the formalization of a new approach called Robotic Condition Synthesis, which shifts the traditionally labor-intensive task of condition synthesis to a robot-based force control task. The second contribution is developing a new robotic manipulator, which is more effective than current lab-grade robots for the tasks involved in the Robotic Condition Synthesis. The third contribution is empirical evidence of the superiority of this new robot in performing the Robotic Condition Synthesis tasks. This study also explores the potential of the new robot by conducting a three-dimensional system identification of a rotordynamic plant, which lays the foundation for more advanced Robotic Condition Synthesis policies in the future.},

keywords = {Actuator, Automation, Design, Experimentation, Instrumentation, Intelligence, Mechatronics, Motion Control, Theory},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{2022IEEEAIM,

title = {Physical Intelligence in the Metaverse: Mixed Reality Scale Models for Twistronics and Atomic Force Microscopy},

author = {Fangzhou Xia* and Morgan P Mayborne and Qiong Ma and Kamal Youcef-Toumi},

url = {https://ieeexplore.ieee.org/document/9863383},

doi = {10.1109/AIM52237.2022.9863383},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)},

pages = {1722-1729},

abstract = {Physical intelligence (PI) is an emerging research field using new multi-functional smart materials in mechatronic designs. On the microscopic scale, PI principles give rise to unconventional transducers, which are especially useful for micro/nano-robot design with size and resource constrains. Since it is not easy to directly observe nanoscale multi-physics phenomenon, understanding their principles can be challenging. In this work, we bring PI principles into the metaverse to bridge this gap by developing two mixed reality scale models. The first example is a virtual reality (VR) 2D material twistronics visualizer to demonstrate the novel intelligent 2D materials with tunable properties as a rising field in condensed matter physics. Users can interactively control the cross-coupling multi-physics phenomena and observe the visualized material responses. The second example is centered around an Atomic Force Microscope (AFM) to illustrate its imaging and probe principles. For interaction, users can control the twist angle using atomic lattice models and feel the AFM cantilever force using custom haptic devices. We believe these tools can help precision mechatronic engineers understand and make better use of physical intelligence building blocks to design micro-electromechanical systems.},

keywords = {Actuator, Atomic Force Microscopy, Design, Education, Experimentation, Instrumentation, Intelligence, Material Science, Mechatronics, MEMS, Modeling & Simulation, Nanorobotics, Sensor},

pubstate = {published},

tppubtype = {inproceedings}

}