# Deformation Prediction of 4D‐Printed Active Composite Structures Based on Data Mining

## TL;DR

This paper introduces a data mining-based method to efficiently predict the deformation of 4D-printed active composite structures.

## Contribution

The novel CSPG algorithm improves deformation prediction accuracy and efficiency compared to traditional and deep learning methods.

## Key findings

- The CSPG algorithm predicts deformation for arbitrary-length voxel encodings efficiently.
- The method outperforms finite element and deep learning approaches in accuracy and generalization.
- A web-based platform enables user customization and end-to-end deformation prediction.

## Abstract

Voxelizing active composite structures and controlling voxel‐level material properties via 4D printing significantly expand design possibilities. However, as the number of voxels increases, the design space grows exponentially, posing significant challenges for predicting structural deformation. Here, a scalable deformation prediction method based on data mining is proposed. This method constructs a feature database using manually extracted features and employs the proposed curvature‐driven sequence point generation (CSPG) algorithm to predict deformations for voxel encodings of arbitrary length. Compared with the traditional finite element (FE) method, this approach significantly improves prediction efficiency, completing a single task within a second. In contrast to deep learning (DL) methods, this method improves prediction accuracy and effectively addresses the limited generalization ability of DL models when applied to structures beyond the training domain. To enhance usability, an interactive web‐based platform is developed that allows users to customize voxel encodings and obtain end‐to‐end predictions. In addition to serving as an efficient tool for deformation prediction of active composite structures, this work introduces a novel pathway for the optimal design of complex intelligent structures.

A curvature‐driven sequence point generation (CSPG) algorithm based on data mining is proposed to predict the deformation of double‐layer voxelized composite structures of arbitrary lengths. In addition, the CSPG algorithm is applied to predict the deformation of 2D and 3D structures assembled from beam elements, and its effectiveness is validated through 4D‐printed hydrogel experiments. This study provides an efficient tool for deformation prediction of active composite structures.