Melt pool level flaw detection in laser hot wire directed energy deposition using a convolutional long short-term memory autoencoder

While additive manufacturing has seen rapid proliferation in recent years, process monitoring and quality assurance methods capable of detecting common flaws have seen little improvement and remain largely expensive and time-consuming. This work focuses on process monitoring using video data for a l...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Additive manufacturing Jg. 79; S. 103843
Hauptverfasser: Abranovic, Brandon, Sarkar, Sulagna, Chang-Davidson, Elizabeth, Beuth, Jack
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 05.01.2024
Schlagworte:
Additive manufacturing
CNN
ConvLSTM
Deep learning
Laser welding
LSTM
Machine learning
Process monitoring
Robotic welding
Video analysis
Deep learning
Process monitoring
CNN
LSTM
Video analysis
Machine learning
Additive manufacturing
Laser welding
ConvLSTM
Robotic welding
ISSN:2214-8604, 2214-7810
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:While additive manufacturing has seen rapid proliferation in recent years, process monitoring and quality assurance methods capable of detecting common flaws have seen little improvement and remain largely expensive and time-consuming. This work focuses on process monitoring using video data for a large-scale directed energy deposition process, laser hot wire additive manufacturing. This data was collected using a video camera mounted on the robot arm pointed at the melt pool. A deep learning enabled video-based process monitoring method was applied to the data, which leveraged a combined convolutional long short-term memory (ConvLSTM) artificial neural network. The trained architecture was used to predict future frames of footage. The model’s application in flaw detection is based on its ability to faithfully construct future frames in the video. A metric of process stability was quantified by computing a regularity score between the raw frames and model outputs, with low regularity scores being indicative of an anomaly. Results have demonstrated that the flaw formation mechanisms of interest to this study, including wire dripping, arcing, melt pool oscillation, and wire stubbing are detectable using this method. This implementation of a ConvLSTM-based deep learning enabled melt pool video process monitoring technique is therefore potentially a viable quality assurance method as well as a tool to guide traditional quality assurance methods.
ISSN:2214-8604
2214-7810
DOI:10.1016/j.addma.2023.103843