Back to Search Start Over

Using Voxelisation-Based Data Analysis Techniques for Porosity Prediction in Metal Additive Manufacturing

Authors :
Abraham George
Marco Trevisan Mota
Conor Maguire
Ciara O’Callaghan
Kevin Roche
Nikolaos Papakostas
Source :
Applied Sciences, Vol 14, Iss 11, p 4367 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Additive manufacturing workflows generate large amounts of data in each phase, which can be very useful for monitoring process performance and predicting the quality of the finished part if used correctly. In this paper, a framework is presented that utilises machine learning methods to predict porosity defects in printed parts. Data from process settings, in-process sensor readings, and post-process computed tomography scans are first aligned and discretised using a voxelisation approach to create a training dataset. A multi-step classification system is then proposed to classify the presence and type of porosity in a voxel, which can then be utilised to find the distribution of porosity within the build volume. Titanium parts were printed using a laser powder bed fusion system. Two discretisation techniques based on voxelisation were utilised: a defect-centric and a uniform discretisation method. Different machine learning models, feature sets, and other parameters were also tested. Promising results were achieved in identifying porous voxels; however, the accuracy of the classification requires improvement before being applied industrially. The potential of the voxelisation-based framework for this application and its ability to incorporate data from different stages of the additive manufacturing workflow as well as different machine learning models was clearly demonstrated.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.75818ee490c7425d9ecb37865921a94a
Document Type :
article
Full Text :
https://doi.org/10.3390/app14114367