Project Description

In Nature, physical processes conduct computation. In contrast, classic digital computation conducts calculations abstracted in mathematical or logic notions, away from physical substrates. This project aims to realize a novel hybrid material that self-conforms around an object of interest as a physical route for computing and reporting the object’s shape. Such an adaptive material computing platform will allow for solving complex contact problems often encountered in designing and controlling soft robot actuation, locomotion, morphing structures, and gripping without the need for expensive finite element methods-based approaches. By having a material that does not just sense an applied force, but rather uses the force to adapt its own geometry, its response can be viewed as a self-shaping feature extraction of the input. This will enable hybrid systems able to self-learn the shape of any input field or object, which is reported through changes in material conductivity and self-shaping. We envision our hybrid materials as an interface that simplifies the complexity of inputs and data generated by interaction of physical stimuli of such systems and the central control that makes important decision-making. Such hybrid materials impact a broad range of applications, including aircraft systems, soft robotics, compliant exoskeletons, and wearable devices. 

Start Date

Spring 2021

Postdoc Qualifications

The candidate should have a Ph.D. in Engineering or closely related area(s), with a strong background in nonlinear finite element analysis and mechanical instability. Previous experience with mechanical metamaterials, machine learning, and 3-D printing processing will be advantageous and applicants with such background are encouraged to apply. Strong oral and written communication skills are essential, as evidenced by refereed journal publications and conference presentations.

Co-Advisors

Andres F. Arrieta
aarrieta@purdue.edu
Mechanical Engineering School
https://engineering.purdue.edu/ProgrammableStructures/

Milind Kulkarni
milind@purdue.edu
Electrical and Computer Engineering
https://engineering.purdue.edu/~milind/

Ilias Bilionis
ibilion@purdue.edu
Mechanical Engineering
https://www.predictivesciencelab.org/ 

References

C. Du, F. Cai, M. A. Zidan, W. Ma, S. H. Lee and W. D. Lu, "Reservoir computing using dynamic memristors for temporal information processing," Nature Communications, vol. 8, no. 1, pp. 1-10, 2017.

Z. Wang, S. Joshi, S. Savel, W. Song, R. Midya, Y. Li, M. Rao, P. Yan, S. Asapu, Y. Zhuo, H. Jiang, P. Lin, C. Li, J. H. Yoon, N. K. Upadhyay, J. Zhang, M. Hu, J. P. Strachan, M. Barnell, Q. Wu, H. Wu, R. S. Williams, Q. Xia and J. J. Yang, "Fully memristive neural networks for pattern classification with unsupervised learning," Nature Electronics, vol. 1, no. February, pp. 137-145, 2018.

H. Le Ferrand, A. R. Studart and A. F. Arrieta, "Filtered Mechanosensing Using Snapping Composites with Embedded Mechano-Electrical Transduction," ACS Nano, vol. 13, no. 4, pp. 4752-4760, 2019.

J. U. Schmied, H. Le Ferrand, P. Ermanni, A. R. Studart and A. F. Arrieta, "Programmable snapping composites with bio-inspired architecture," Bioinspiration & Biomimetics, vol. 12, no. 2, p. 026012, 2017.

J. Faber, J. P. Udani, K. S. Riley, A. F. Arrieta and A. R. Studart, " Dome-Patterned Metamaterial Sheets," under review, 2019.