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Detecting anomalies in aerospace additive manufacturing: A Convolutional Long Short-term Memory approach

Nguyen Dam Khanh, Linh LU (2024) DABN01 20241
Department of Economics
Department of Statistics
Abstract
As additive manufacturing technology within the aerospace sector advances rapidly, optimizing process monitoring methods has become increasingly critical, given that existing techniques remain costly and time-consuming. This study presents a novel approach to monitoring the laser metal deposition (LMD) process, emphasizing real-time anomaly detection through the analysis and prediction of frames from melt pool videos captured by top-view in-situ cameras. By training the Convolutional Long Short-Term Memory autoencoder on anomaly-free videos, the study has developed a model capable of forecasting future anomaly-free frames and identifying deviations from expected patterns. The model’s performance was evaluated using both stable and unstable... (More)
As additive manufacturing technology within the aerospace sector advances rapidly, optimizing process monitoring methods has become increasingly critical, given that existing techniques remain costly and time-consuming. This study presents a novel approach to monitoring the laser metal deposition (LMD) process, emphasizing real-time anomaly detection through the analysis and prediction of frames from melt pool videos captured by top-view in-situ cameras. By training the Convolutional Long Short-Term Memory autoencoder on anomaly-free videos, the study has developed a model capable of forecasting future anomaly-free frames and identifying deviations from expected patterns. The model’s performance was evaluated using both stable and unstable process datasets, demonstrating its effectiveness in detecting a range of anomalies through pixel-wise difference comparison between predicted and actual frames. The results underscore the value of integrating advanced deep learning techniques into the LMD monitoring system, contributing to the development of real-time quality control, preventive maintenance, post-production analysis, and process optimization. (Less)
Please use this url to cite or link to this publication:
author
Nguyen Dam Khanh, Linh LU
supervisor
organization
course
DABN01 20241
year
type
H1 - Master's Degree (One Year)
subject
keywords
anomaly detection, autoencoder, convolutional long short-term memory
language
English
id
9173373
date added to LUP
2024-09-24 08:38:32
date last changed
2024-09-24 08:38:32
@misc{9173373,
  abstract     = {{As additive manufacturing technology within the aerospace sector advances rapidly, optimizing process monitoring methods has become increasingly critical, given that existing techniques remain costly and time-consuming. This study presents a novel approach to monitoring the laser metal deposition (LMD) process, emphasizing real-time anomaly detection through the analysis and prediction of frames from melt pool videos captured by top-view in-situ cameras. By training the Convolutional Long Short-Term Memory autoencoder on anomaly-free videos, the study has developed a model capable of forecasting future anomaly-free frames and identifying deviations from expected patterns. The model’s performance was evaluated using both stable and unstable process datasets, demonstrating its effectiveness in detecting a range of anomalies through pixel-wise difference comparison between predicted and actual frames. The results underscore the value of integrating advanced deep learning techniques into the LMD monitoring system, contributing to the development of real-time quality control, preventive maintenance, post-production analysis, and process optimization.}},
  author       = {{Nguyen Dam Khanh, Linh}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Detecting anomalies in aerospace additive manufacturing: A Convolutional Long Short-term Memory approach}},
  year         = {{2024}},
}