A computational framework for efficient porosity analysis and process parameter optimization in powder bed fusion with laser beam

The fast-paced development of new alloys for metal additive manufacturing (AM) makes it imperative to quickly quantify how material properties depend on the processing parameters. For instance, minimizing the porosity of the printed parts can help reduce corrosion, improve strength, and increase fatigue life. Porosity is also the first property optimized during process parameter development in laser beam powder bed fusion (PBF-LB). This paper demonstrates an approach for efficiently mapping the parameter space of additively manufactured components with a minimal number of samples, using porosity as an example. The workflow consists of two main steps: a neural network for efficient evaluation of the objective and a Gaussian process for an interpretable model of the process-structure-property relationship. Specifically, a convolutional neural network, U-net, segments microscopy images to quickly calculate the porosity without manual evaluation. A Gaussian process then models the porosity as a function of its process parameters, which also allows one to quantify the uncertainty of the porosity throughout the component with minimal samples. This work applies this methodology to WE43, a magnesium alloy of specific interest to biomedical applications, to find the combination of process parameters that minimize the porosity based on the laser power, scan speed, and hatch distance. This research identified a process parameter combination leading to a porosity of 0.06%, and a robust alternative yielding a porosity of 0.07% with reduced standard deviation. Additionally, the paper examines the effect of sample size during model construction and provides guidelines for the development of low-data machine learning models.