Version 1
: Received: 27 January 2021 / Approved: 28 January 2021 / Online: 28 January 2021 (15:01:50 CET)
How to cite:
Chenang, L.; Rongxuan, W.; Kong, Z.; Suresh, B.; Chase, J.; James, F. Real-Time 3D Surface Measurement in Additive Manufacturing Using Deep Learning. Preprints2021, 2021010587. https://doi.org/10.20944/preprints202101.0587.v1
Chenang, L.; Rongxuan, W.; Kong, Z.; Suresh, B.; Chase, J.; James, F. Real-Time 3D Surface Measurement in Additive Manufacturing Using Deep Learning. Preprints 2021, 2021010587. https://doi.org/10.20944/preprints202101.0587.v1
Chenang, L.; Rongxuan, W.; Kong, Z.; Suresh, B.; Chase, J.; James, F. Real-Time 3D Surface Measurement in Additive Manufacturing Using Deep Learning. Preprints2021, 2021010587. https://doi.org/10.20944/preprints202101.0587.v1
APA Style
Chenang, L., Rongxuan, W., Kong, Z., Suresh, B., Chase, J., & James, F. (2021). Real-Time 3D Surface Measurement in Additive Manufacturing Using Deep Learning. Preprints. https://doi.org/10.20944/preprints202101.0587.v1
Chicago/Turabian Style
Chenang, L., Joslin Chase and Ferguson James. 2021 "Real-Time 3D Surface Measurement in Additive Manufacturing Using Deep Learning" Preprints. https://doi.org/10.20944/preprints202101.0587.v1
Abstract
Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.
Keywords
Additive manufacturing; surface morphology; real-time measurement; deep learning
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
