Validating Porosity Prediction for Aluminum Laser Powder Bed Fusion Components

The present work highlights a collaborative effort aimed at enhancing porosity and surface roughness prediction in metal laser powder bed fusion (L-PBF) additive manufacturing (AM) components through the application of analytical multi-physics models. The intersection of material, process parameters, and geometry results in varying conditions within the build and can lead to undesirable outcomes. The ability to identify the risk of porosity and surface roughness under varying process parameters and take steps to mitigate those conditions can significantly accelerate the L-PBF AM process development cycle.

The collaboration focuses on predicting print quality for Constellium Aheadd® CP1 aluminum alloy which was designed specifically for the L-PBF AM process. Data from ultrasonic testing (UT), X-ray computed tomography (XCT), large-field-of-view synchrotron tomography, and high-speed X-ray imaging. The team compares real-world results combining multiple techniques with Simufact Additive’s simulated predictions. For validation, large field-of-view synchrotron tomography provides high-resolution insights into internal structures at the scale of the full component, enabling a detailed examination of porosity and other defects.
The outcomes will demonstrate the accuracy of Simufact Additive in predicting porosity and other critical defects, offering a clearer understanding of the factors contributing to AM defects. By predicting these issues early in the design phase, manufacturers can enhance process reliability, optimize material performance, and scale production more confidently.

Attendees will learn how this collaboration combined materials expertise from Constellium, with novel meso-scale process modeling from Hexagon and state-of-the-art characterization methods to advance quality prediction methods in AM and enable the production of sustainable, high-performance components with greater precision and reliability.