1
|
Wang J, Sarkar D, Mohan A, Lee M, Ma Z, Chortos A. Deep Learning for Strain Field Customization in Bioreactor with Dielectric Elastomer Actuator Array. CYBORG AND BIONIC SYSTEMS 2024; 5:0155. [PMID: 39144697 PMCID: PMC11322265 DOI: 10.34133/cbsystems.0155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 07/09/2024] [Indexed: 08/16/2024] Open
Abstract
In the field of biomechanics, customizing complex strain fields according to specific requirements poses an important challenge for bioreactor technology, primarily due to the intricate coupling and nonlinear actuation of actuator arrays, which complicates the precise control of strain fields. This paper introduces a bioreactor designed with a 9 × 9 array of independently controllable dielectric elastomer actuators (DEAs), addressing this challenge. We employ image regression-based machine learning for both replicating target strain fields through inverse control and rapidly predicting feasible strain fields generated by the bioreactor in response to control inputs via forward control. To generate training data, a finite element analysis (FEA) simulation model was developed. In the FEA, the device was prestretched, followed by the random assignment of voltages to each pixel, yielding 10,000 distinct output strain field images for the training set. For inverse control, a multilayer perceptron (MLP) is utilized to predict control inputs from images, whereas, for forward control, MLP maps control inputs to low-resolution images, which are then upscaled to high-resolution outputs through a super-resolution generative adversarial network (SRGAN). Demonstrations include inputting biomechanically significant strain fields, where the method successfully replicated the intended fields. Additionally, by using various tumor-stroma interfaces as inputs, the bioreactor demonstrated its ability to customize strain fields accordingly, showcasing its potential as an advanced testbed for tumor biomechanics research.
Collapse
Affiliation(s)
- Jue Wang
- School of Mechanical Engineering, College of Engineering,
Purdue University, West Lafayette, IN, USA
| | - Dhirodaatto Sarkar
- School of Mechanical Engineering, College of Engineering,
Purdue University, West Lafayette, IN, USA
| | - Atulya Mohan
- School of Mechanical Engineering, College of Engineering,
Purdue University, West Lafayette, IN, USA
| | - Mina Lee
- School of Mechanical Engineering, College of Engineering,
Purdue University, West Lafayette, IN, USA
| | - Zeyu Ma
- School of Mechanical Engineering, College of Engineering,
Purdue University, West Lafayette, IN, USA
- School of Mechanical Engineering, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Institute of Design Science and Basic Components, Xi’an Jiaotong University, Xi’an, P. R. China
| | - Alex Chortos
- School of Mechanical Engineering, College of Engineering,
Purdue University, West Lafayette, IN, USA
| |
Collapse
|
2
|
Wang J, Sotzing M, Lee M, Chortos A. Passively addressed robotic morphing surface (PARMS) based on machine learning. SCIENCE ADVANCES 2023; 9:eadg8019. [PMID: 37478174 PMCID: PMC10361599 DOI: 10.1126/sciadv.adg8019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 06/21/2023] [Indexed: 07/23/2023]
Abstract
Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy. Developing compact, efficient control interfaces and algorithms is vital for broader adoption. In this work, we describe a passively addressed robotic morphing surface (PARMS) composed of matrix-arranged ionic actuators. To reduce the complexity of the physical control interface, we introduce passive matrix addressing. Matrix addressing allows the control of N2 independent actuators using only 2N control inputs, which is substantially lower than traditional direct addressing (N2 control inputs). Using machine learning with finite element simulations for training, our control algorithm enables real-time, high-precision forward and inverse control, allowing PARMS to dynamically morph into arbitrary achievable predefined surfaces on demand. These innovations may enable the future implementation of PARMS in wearables, haptics, and augmented reality/virtual reality.
Collapse
Affiliation(s)
- Jue Wang
- Department of Mechanical Engineering, Purdue University, 500 Central Dr, Lafayette, IN 47907, USA
| | - Michael Sotzing
- Department of Mechanical Engineering, Purdue University, 500 Central Dr, Lafayette, IN 47907, USA
| | - Mina Lee
- Department of Mechanical Engineering, Purdue University, 500 Central Dr, Lafayette, IN 47907, USA
| | - Alex Chortos
- Department of Mechanical Engineering, Purdue University, 500 Central Dr, Lafayette, IN 47907, USA
| |
Collapse
|