@article {3182028,
	title = {3D-Printed Mechano-Optic Force Sensor for Soft Robotic Gripper Enabled by Programmable Structural Metamaterials},
	journal = {Advanced Intelligent Systems},
	year = {2024},
	pages = {2400057},
	abstract = {Rapid deployment of automation in today{\textquoteright}s world has opened up exciting possibilities in the realm of design and fabrication of soft robotic grippers endowed with sensing capabilities. Herein, a novel design and rapid fabrication by 3D printing of a mechano-optic force sensor with a large dynamic range, sensitivity, and linear response, enabled by metamaterials-based structures, is presented. A simple approach for programming the metamaterial{\textquoteright}s behavior based on mathematical modeling of the sensor under dynamic loading is proposed. Machine learning models are utilized to predict the complete force{\textendash}deformation profile, encompassing the linear range, the onset of nonlinear behavior, and the slope of profiles in both bending and compression-dominated regions. The design supports seamless integration of the sensor into soft grippers, enabling 3D printing of the soft gripper with an embedded sensor in a single step, thus overcoming the tedious and complex and multiple fabrication steps commonly applied in conventional processes. The sensor boasts a fine resolution of 0.015 N, a measurement range up to 16 N, linearity (adj.\&nbsp;<i>R</i><sup>2</sup>{\textendash}0.991), and delivers consistent performance beyond 100 000 cycles. The sensitivity and range of the embedded mechano-optic force sensor can be easily programmed by both the metamaterial structure and the material{\textquoteright}s properties.},
	url = {https://doi.org/10.1002/aisy.202400057},
	author = {Chidanand Hegde and Ravi Chaithanya Mysa and Aaron Chooi and Saikrishna Dontu and Joel Ming Rui Tan and Lydia Helena Wong and Pablo Valdivia y Alvarado and Shlomo Magdassi}
}
