Material characterization and precise finite element analysis of fiber reinforced thermoplastic composites for 4D printing

TL;DR

This paper develops a workflow combining physical experiments and finite element analysis to accurately model and simulate fiber reinforced thermoplastic composites used in 4D printing, enhancing structural strength and design precision.

Contribution

It introduces a new material characterization and simulation workflow for fiber reinforced thermoplastic composites in 4D printing, improving accuracy over previous models.

Findings

Material characterization via DMA improves simulation accuracy.

Finite element analysis achieves 95% confidence in deformation predictions.

Composite design enhances structural strength of 4D printed artifacts.

Abstract

Four-dimensional (4D) printing, a new technology emerged from additive manufacturing (3D printing), is widely known for its capability of programming post-fabrication shape-changing into artifacts. Fused deposition modeling (FDM)-based 4D printing, in particular, uses thermoplastics to produce artifacts and requires computational analysis to assist the design processes of complex geometries. However, these artifacts are weak against structural loads, and the design quality can be limited by less accurate material models and numerical simulations. To address these issues, this paper propounds a composite structure design made of two materials - polylactic acid (PLA) and carbon fiber reinforced PLA (CFPLA) - to increase the structural strength of 4D printed artifacts and a workflow composed of several physical experiments and series of dynamic mechanical analysis (DMA) to characterize materials. We apply this workflow to 3D printed samples fabricated with different printed parameters to accurately characterize the materials and implement a sequential finite element analysis (FEA) to achieve accurate simulations. The accuracy of deformation induced by the triggering process is both computationally and experimentally verified with several creative design examples, and the 95% confidence interval of the accuracy is (0.972, 0.985). We believe the presented workflow is essential to the combination of geometry, material mechanism and design, and has various potential applications.