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

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

doi:10.1117/12.2567007

20 August 2020 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, Applications of Machine Learning 2020; 115110P (2020) https://doi.org/10.1117/12.2567007
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Barath Narayanan Narayanan, Kelly Beigh, Solomon Duning, and Dathan Erdahl "Automated material identification and segmentation using deep learning for laser powder bed fusion", Proc. SPIE 11511, Applications of Machine Learning 2020, 115110P (20 August 2020); https://doi.org/10.1117/12.2567007

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available