Prediction of Fracture Loads in 3D-Printed ASA and Carbon-Fiber Reinforced ASA Notched Specimens Using the Calibrated ASED Criterion

TL;DR

This paper introduces a calibrated method to predict fracture loads in 3D-printed polymers and carbon-fiber composites using the ASED criterion, improving accuracy for non-linear materials.

Contribution

A calibrated ASED criterion is proposed to predict fracture loads in non-linear 3D-printed materials, extending its traditional linear-elastic use.

Findings

The calibrated ASED criterion accurately predicts failure loads in 3D-printed ASA and carbon-fiber reinforced ASA.

The method was validated using SENB specimens with varying raster orientations and U-notch radii.

The approach provides a reliable tool for structural integrity assessment of additively manufactured components.

Abstract

This paper presents an adapted methodology for the prediction of fracture loads in additively manufactured (fused filament fabrication) polymers that exhibit non-linear behavior. The approach is based on the Average Strain Energy Density (ASED) criterion, which is typically limited to materials which develop fully linear-elastic behavior. Thus, in those cases where the material has a certain (non-negligible) amount of non-linear behavior, the ASED criterion needs to be corrected. To extend its applicability, this work proposes a thorough calibration of the ASED characteristic parameters: the critical value of the strain energy and the volume of the corresponding control volume. This enables the extrapolation of the linear-elastic formulation to non-linear situations. The approach is validated using acrylonitrile-styrene-acrylate (ASA) and 10 wt.% carbon-fiber reinforced ASA specimens. Single-edge-notched bending (SENB) specimens with three different raster orientations (0/90, 45/−45, and 30/−60) and four U-notch radii (0.0 mm—crack-like, 0.50 mm, 1.0 mm, and 2.0 mm) were printed and tested. The results demonstrate that the proposed calibration of the ASED criterion allows for accurate predictions of failure loads, providing a reliable tool for the structural integrity assessment of 3D-printed components.

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