# Energy-Based Approach for Fatigue Life Prediction of Additively Manufactured ABS/GNP Composites

## TL;DR

This paper proposes a new energy-based model to predict the fatigue life of 3D-printed ABS/GNP composites, which works well for materials under negative mean stress.

## Contribution

A novel fatigue life prediction model combining two energy-based models was developed, incorporating stress ratio dependence for negative mean stress.

## Key findings

- Existing energy-based models failed to accurately predict fatigue life across all stress regimes.
- The new combined model predicted fatigue life within ±2 bounds for R values between −0.22 and 0.
- The proposed model is reliable for a wide range of stress ratios with negative mean stress.

## Abstract

This study examines the effectiveness of energy-based models for fatigue life prediction of additively manufactured acrylonitrile butadiene styrene (ABS)/graphene nanoplatelet (GNP) composites. The effects of varying GNP weight percentages and filament raster orientations on the fatigue life of the samples were investigated theoretically. The required stress and strain values for use in energy-based models were obtained by solving two sets of Neuber and Ramberg–Osgood equations, utilizing the available values of notch strength reduction factors at each load level and the average Young modulus for each composite material. Results revealed that none of the studied energy-based models could accurately predict the fatigue life of the samples across the entire high- and low-cycle fatigue regimes, with strong dependence on the stress ratio (R). Instead, a novel fatigue life prediction model was developed by combining two existing energy-based models, incorporating stress ratio dependence for cases with negative mean stress. This model was tested for R values roughly between −0.22 and 0. Results showed that, for all samples at each raster orientation, most of the predicted fatigue lives fell within the upper and lower bounds, with a factor of ±2 across the entire range of load levels. These findings highlight the reliability of the proposed model for a wide range of R values when mean stress is negative.