Dr. Dathan Erdahl Profile

Dr. Dathan Erdahl

at Univ of Dayton Research Institute

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Proceedings Article | 20 August 2020 Presentation + Paper

Automated material identification and segmentation using deep learning for laser powder bed fusion

Barath Narayanan Narayanan, Kelly Beigh, Solomon Duning, Dathan Erdahl

Proceedings Volume 11511, 115110P (2020) https://doi.org/10.1117/12.2567007

KEYWORDS: Machine learning, Additive manufacturing, Data visualization, Image segmentation, Manufacturing, Image classification, Sensors, Principal component analysis, Edge detection

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In-situ Laser Powder Bed Fusion (LPBF) sensor packages seek to enable both the commercial and Department of Defense (DoD) supply chains via process monitoring for qualification and machine feedback. An automated material identification and geometric segmentation would be valuable for LPBF process monitoring. In this paper, various segmentation approaches are presented and discussed to determine the best approach. Later, deep learning method(s) to classify the materials as either AlSi10Mg or IN718 are presented. Diverse videos (in terms of shape, size, structure, and camera angle) of both materials are captured and labeled as either AlSi10Mg (24357 frames) or IN718 (9222 frames). A given frame can contain single or multiple parts of a material. The segmentation approach is applied to extract each part and 121,036 images are obtained. The dataset is randomly split into groups of 72%, 8% and 20% for training, validation, and testing respectively. Classification performance(s) using the proposed Convolutional Neural Network (CNN) in addition to transfer learning approaches using established networks such as AlexNet, ResNet, and SqueezeNet are studied. An overall accuracy of 99.6% is obtained on a set of 24,214 test images. In addition, efficacy of the proposed classification model is demonstrated by testing the algorithm on a completely different variant (in terms of shape, size, structure, or camera angle) of either material. The class activation mapping results of these networks are presented, yielding an insight into the network’s decision, and assisting the manufacturers in their decision-making process.

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