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Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
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Machine learning-based optimal design of fibrillar adhesives.

Mohammad Shojaeifard1, Matteo Ferraresso1, Alessandro Lucantonio2

  • 1Mechanical Engineering Department, University of British Columbia, Vancouver, BC V6T1Z4, Canada.

Journal of the Royal Society, Interface
|February 25, 2025
PubMed
Summary

We developed a machine learning tool to optimize fibril compliance for stronger adhesion. This approach enhances the design of bio-inspired adhesives and micro-architected materials for improved fracture resistance.

Keywords:
adhesionbioinspired fibrillar adhesivesfracture mechanicsinverse designmachine learningoptimization

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Area of Science:

  • Biomimetics and Materials Science
  • Robotics and Engineering

Background:

  • Fibrillar adhesion in nature (beetles, spiders, geckos) uses micro/nanofibrils for enhanced adhesion via 'contact splitting'.
  • This principle inspires engineering applications in robotics, transportation, and medicine, with functional grading of fibril properties showing potential for improved adhesion.
  • Previous designs focused on simplified geometries, and no prior work has optimized fibril-array scale using machine learning.

Purpose of the Study:

  • To introduce a machine learning (ML) based tool for optimizing fibril compliance distribution to maximize adhesive strength.
  • To demonstrate the tool's capability in recovering known design results for simple geometries and generating novel solutions for complex configurations.
  • To accelerate the design process and reduce errors in creating high-performance fibrillar adhesives.

Main Methods:

  • Utilized a two-neural network (NN) system: a predictor NN to estimate adhesive strength from compliance distributions and a designer NN for gradient-based optimization.
  • Applied ML to optimize the distribution of fibril compliance across an array.
  • Validated the approach on both simple and complex geometric configurations.

Main Results:

  • The ML tool successfully recovered previous design outcomes for simplified geometries.
  • Novel and optimized compliance distributions were identified for complex configurations, leading to enhanced adhesive strength.
  • The method significantly reduced test error and accelerated the optimization process compared to traditional approaches.

Conclusions:

  • The proposed ML-based tool offers a high-performance solution for designing advanced fibrillar adhesives.
  • This approach enables the creation of optimized micro-architected materials for applications requiring superior fracture resistance through equal load sharing.
  • Machine learning provides an efficient pathway for tackling complex design challenges in bio-inspired adhesion.