Abstract
Surface deformation is a multi-factor, laser powder-bed fusion (LPBF) defect that cannot be avoided entirely using current monitoring systems. Distortion and warping, if left unchecked, can compromise the mechanical and physical properties resulting in a build with an undesired geometry. Increasing dwell time, pre-heating the substrate, and selecting appropriate values for the printing parameters are common ways to combat surface deformation. However, the absence of real-time detection and correction of surface deformation is a crucial LPBF problem. In this work, we propose a novel approach to identifying surface deformation problems from powder-bed images in real time by employing a convolutional neural network-based solution. Identifying surface deformation from powder-bed images is a significant step toward real-time monitoring of LPBF. Thirteen bars, with overhangs, were printed to simulate surface deformation defects naturally. The carefully chosen geometric design overcomes problems relating to unlabelled data by providing both normal and defective examples for the model to train. To improve the quality and robustness of the model, we employed several deep learning techniques such as data augmentation and various model evaluation criteria. Our model is 99% accurate in identifying the surface distortion from powder-bed images.
Original language | English |
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Article number | 7166 |
Pages (from-to) | 7166 |
Number of pages | 1 |
Journal | Materials |
Volume | 15 |
Issue number | 20 |
DOIs | |
Publication status | Published - 14 Oct 2022 |
Externally published | Yes |
Keywords
- surface deformation
- LPBF
- metal additive manufacturing
- convolutional neural network
- machine learning
- deep learning
ASJC Scopus subject areas
- General Materials Science
- Condensed Matter Physics