Multi‐Objective Bayesian Optimization for Laminate‐Inspired Mechanically Reinforced Piezoelectric Self‐Powered Sensing Yarns

Author:

Yang Ziyue1,Park Kundo23,Nam Jisoo1,Cho Jaewon1,Choi Yong Jun1,Kim Yong‐Il4,Kim Hyeonsoo1,Ryu Seunghwa2,Kim Miso15ORCID

Affiliation:

1. Department of Advanced Materials Science and Engineering Sungkyunkwan University (SKKU) Suwon 16419 Republic of Korea

2. Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea

3. Department of Mechanical Engineering University of California Berkeley CA 94720 USA

4. Korea Research Institute of Standards and Science Daejeon 34113 Republic of Korea

5. SKKU Institute of Energy Science and Technology (SIEST) Sungkyunkwan University (SKKU) Suwon 16419 South Korea

Abstract

AbstractPiezoelectric fiber yarns produced by electrospinning offer a versatile platform for intelligent devices, demonstrating mechanical durability and the ability to convert mechanical strain into electric signals. While conventional methods involve twisting a single poly(vinylidene fluoride‐co‐trifluoroethylene)(P(VDF‐TrFE)) fiber mat to create yarns, by limiting control over the mechanical properties, an approach inspired by composite laminate design principles is proposed for strengthening. By stacking multiple electrospun mats in various sequences and twisting them into yarns, the mechanical properties of P(VDF‐TrFE) yarn structures are efficiently optimized. By leveraging a multi‐objective Bayesian optimization‐based machine learning algorithm without imposing specific stacking restrictions, an optimal stacking sequence is determined that simultaneously enhances the ultimate tensile strength (UTS) and failure strain by considering the orientation angles of each aligned fiber mat as discrete design variables. The conditions on the Pareto front that achieve a balanced improvement in both the UTS and failure strain are identified. Additionally, applying corona poling induces extra dipole polarization in the yarn state, successfully fabricating mechanically robust and high‐performance piezoelectric P(VDF‐TrFE) yarns. Ultimately, the mechanically strengthened piezoelectric yarns demonstrate superior capabilities in self‐powered sensing applications, particularly in challenging environments and sports scenarios, substantiating their potential for real‐time signal detection.

Funder

National Research Foundation of Korea

Publisher

Wiley

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