Raptor: A Python Library for Porosity Predictions in Additive Manufacturing
Abstract
Raptor is an efficient Python library for simulating stochastic lack-of-fusion (sLoF) defects in additive manufacturing (AM) processes.These defects arise from stochastic variations in the melt pool boundary leading to undermelting from insufficient overlap between adjacent melt pools or successive layers (Grasso & Colosimo, 2017;Khairallah et al., 2016).Performance variability of AM parts is a pressing challenge in qualification and certification of AM parts; this is in part due to the poorly understood formation rate and statistics of sLoF defects.Raptor is designed to capture the explicit morphologies of sLoF defects and their statistics to accelerate qualification and certification efforts of AM parts in critical applications.
Fields of Study & Subjects
Machine Learning in Materials Science
Additive Manufacturing Materials and Processes
Additive Manufacturing and 3D Printing Technologies
Publication details
- Journal / source
- The Journal of Open Source Software
- Publisher
- The Open Journal
- Volume
- 11
- Issue
- 123
- Pages
- 9874–9874
- Publication date
- 31 July 2026