# 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.

## Key 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.