3D Printing And Machine Learning For Control Of Soft Ionic Polymer-Metal Composite Actuators.pdf

CarricoJD_2019_SR.pdf
Preview of 3D Printing and Machine Learning for Control of Soft Ionic Polymer-Metal Composite Actuators
🔗 Source: kam.k.leang.com
📊 Size: 2.84 MB
👤 Author: James D. Carrico
⬇️ Downloads: 274

Summary

Key contributions include:

1. Additive Manufacturing with 3D Printing: A method using fused-filament 3D printing allows for the creation of custom-shaped, monolithic IPMC devices, integrating sensors and actuators directly. This overcomes limitations of traditional manufacturing methods.

2. Machine Learning Control: Bayesian optimization is employed as a learning-based control approach to mitigate complex dynamic effects in 3D-printed actuators. This avoids the need for intricate modeling or continuous sensor feedback.

3. Feasibility Demonstration: The paradigm is applied to create and control an example IPMC soft crawling robot, showcasing its potential.

4. Challenges and Opportunities: While challenges like material degradation over time exist, the paper highlights the promise of this approach for developing more complex IPMC devices, emphasizing the potential for continued advancement in soft robotics.

Description

It demonstrates the creation of custom-shaped, monolithic ionic polymer-metal composite (IPMC) devices with integrated sensors and overcomes dynamic challenges through Bayesian optimization.

Technical Information

  • File Format: PDF
  • File Size: 2.84 MB
  • Pages: 17
  • Language: EN
  • Author: James D. Carrico
  • Total Downloads: 274
  • Last Updated: 3 hours ago

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