Physics-Constrained Neural Network for Design and Feature-Based Optimization of Weave Architectures

TL;DR

This paper introduces a physics-constrained neural network that predicts and designs weave architectures with desired mechanical properties, improving efficiency over traditional methods and incorporating feature-based optimization for near-optimal solutions.

Contribution

The paper presents a novel physics-constrained neural network for predicting and designing weave architectures, integrating feature-based optimization to enhance accuracy and efficiency.

Findings

PCNN outperforms baseline models in predicting weave properties.

The feature-based optimization improves design accuracy.

The framework enables efficient initial design of weave architectures.

Abstract

Woven fabrics play an essential role in everyday textiles for clothing/sportswear, water filtration, and retaining walls, to reinforcements in stiff composites for lightweight structures like aerospace, sporting, automotive, and marine industries. Several possible combinations of weave patterns and material choices, which comprise weave architecture, present a challenging question about how they could influence the physical and mechanical properties of woven fabrics and reinforced structures. In this paper, we present a novel Physics-Constrained Neural Network (PCNN) to predict the mechanical properties like the modulus of weave architectures and the inverse problem of predicting pattern/material sequence for a design/target modulus value. The inverse problem is particularly challenging as it usually requires many iterations to find the appropriate architecture using traditional optimization approaches. We show that the proposed PCNN can effectively predict weave architecture for the desired modulus with higher accuracy than several baseline models considered. We present a feature-based optimization strategy to improve the predictions using features in the Grey Level Co-occurrence Matrix (GLCM) space. We combine PCNN with this feature-based optimization to discover near-optimal weave architectures to facilitate the initial design of weave architecture. The proposed frameworks will primarily enable the woven composite analysis and optimization process, and be a starting point to introduce Knowledge-guided Neural Networks into the complex structural analysis.