Impact of Surface and Pore Characteristics on Fatigue Life of Laser Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models

TL;DR

This paper investigates how surface roughness and pore characteristics influence the fatigue life of LPBF Ti-6Al-4V alloy, using micro-CT analysis and neural network models to predict fatigue behavior and uncertainties.

Contribution

It introduces a neural network model that links surface and pore features to fatigue life, providing accurate predictions and uncertainty estimates.

Findings

Pore size and projected area strongly correlate with fatigue life in machined samples.

Surface treatment affects pore distribution and fatigue behavior.

Neural network models can reliably predict fatigue life and quantify uncertainty.

Abstract

In this study, the effects of surface roughness and pore characteristics on fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were investigated. The 197 fatigue bars were printed using the same laser power but with varied scanning speeds. These actions led to variations in the geometries of microscale pores, and such variations were characterized using micro-computed tomography. To generate differences in surface roughness in fatigue bars, half of the samples were grit-blasted and the other half machined. Fatigue behaviors were analyzed with respect to surface roughness and statistics of the pores. For the grit-blasted samples, the contour laser scan in the LPBF strategy led to a pore-depletion zone isolating surface and internal pores with different features. For the machined samples, where surface pores resemble internal pores, the fatigue life was highly correlated with the average pore size and projected pore area in the plane perpendicular to the stress direction. Finally, a machine learning model using a drop-out neural network (DONN) was employed to establish a link between surface and pore features to the fatigue data (logN), and good prediction accuracy was demonstrated. Besides predicting fatigue lives, the DONN can also estimate the prediction uncertainty.