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

Area of Science:

• Materials Science
• Mechanical Engineering
• Additive Manufacturing

Background:

• Additive manufacturing (AM) enables complex geometries but requires accurate fatigue life prediction for materials like acrylonitrile butadiene styrene (ABS)/graphene nanoplatelet (GNP) composites.
• Existing energy-based models struggle to predict fatigue life across high- and low-cycle regimes, particularly with varying stress ratios (R).

Purpose of the Study:

• To evaluate the efficacy of current energy-based models for predicting the fatigue life of AM ABS/GNP composites.
• To develop an improved fatigue life prediction model that accounts for stress ratio dependence, especially for negative mean stress.

Main Methods:

• Theoretical investigation of GNP weight percentages and filament raster orientations' effects on fatigue life.
• Utilized Neuber and Ramberg-Osgood equations to obtain stress and strain values for energy-based models.
• Developed a novel model by combining existing energy-based models and incorporating stress ratio dependence.

Main Results:

• Standard energy-based models failed to accurately predict fatigue life across high- and low-cycle regimes, showing high dependence on the stress ratio (R).
• The novel combined model demonstrated high reliability, with most predictions falling within a factor of ±2 for R values between -0.22 and 0.
• The proposed model proved effective for various load levels and raster orientations when mean stress is negative.

Conclusions:

• The developed energy-based model offers a reliable method for predicting fatigue life in AM ABS/GNP composites under specific conditions (negative mean stress).
• This research provides a more accurate tool for assessing the durability of 3D-printed materials in engineering applications.