scholarly journals Process Monitoring Dataset from the Additive Manufacturing Metrology Testbed (AMMT): “Three-Dimensional Scan Strategies”

2019 ◽  
Vol 124 ◽  
Author(s):  
Brandon Lane ◽  
Ho Yeung
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Process Monitoring ◽  
Three Dimensional ◽  
Monitoring Data ◽  
Real Time Monitoring ◽  
3D Scan ◽  
Input Command

This document provides details on the files available in the dataset "20180708-HY-3D Scan Strategies" pertaining to a 3D additive manufacturing build performed on the Additive Manufacturing Metrology Testbed (AMMT)by Ho Yeung on July 8, 2018. The files include the input command files and in-situ process monitoring data, and metadata. This data is the first of future planned "AMMT Process Monitoring Reference Datasets," as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project.

scholarly journals Process Monitoring Dataset from the Additive Manufacturing Metrology Testbed (AMMT): Overhang Part X4

2020 ◽  
Vol 125 ◽  
Author(s):  
Brandon Lane ◽  
Ho Yeung
Keyword(s):  
Computed Tomography ◽  
Additive Manufacturing ◽  
Real Time ◽  
Process Monitoring ◽  
Three Dimensional ◽  
Ex Situ ◽  
Web Page ◽  
X Ray ◽  
X Ray Computed

This document provides details on the files available in the dataset “Overhang Part X4” pertaining to a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on June 28, 2019. The files include the input command files, materials data, in-situ process monitoring data, and metadata. This data is one of a set of “AMMT Process Monitoring Datasets”, as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project at the National Institute of Standards and Technology (NIST). Ex-situ part characterization data, including X-ray computed tomography (XCT) measurements, will be provided as it is made available. Readers should refer to the AMMT datasets web page for updates.

scholarly journals Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis

2021 ◽  
Author(s):  
Matteo Bugatti ◽  
Bianca Maria Colosimo
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Process Monitoring ◽  
Defect Detection ◽  
High Speed ◽  
Data Extraction ◽  
In Situ Monitoring ◽  
Imaging Data ◽  
Computational Speed

AbstractThe increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart data extraction and analysis methods are fundamental to monitor the process with the required accuracy while keeping the computational effort to a reasonable level for real-time application. In this work, a new framework for the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras is presented: a new data extraction method is developed to efficiently extract only the relevant information from the regions of interest identified in the high-speed imaging data stream and to reduce the dimensionality of the anomaly detection task performed by three competitor machine learning classification methods. The defect detection performance and computational speed of this approach is carefully evaluated through computer simulations and experimental studies, and directly compared with the performance and computational speed of other existing methods applied on the same reference dataset. The results show that the proposed method is capable of quickly detecting the occurrence of defects while keeping the high computational speed that would be required to implement this new process monitoring approach for real-time defect detection.

scholarly journals Application of the STRAY statistical learning algorithm for the evaluation of in-situ process monitoring data during L-PBF additive manufacturing.

2021 ◽  
Vol 54 ◽  
pp. 250-256
Author(s):  
Aoife C. Doyle ◽  
Darragh S. Egan ◽  
Caitríona M. Ryan ◽  
Andrew C. Parnell ◽  
Denis P. Dowling
Keyword(s):  
Additive Manufacturing ◽  
Statistical Learning ◽  
Process Monitoring ◽  
Learning Algorithm ◽  
Monitoring Data

Phononic Crystal Artifacts for Real-Time In Situ Quality Monitoring in Additive Manufacturing

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
Xiaochi Xu ◽  
Chaitanya Krishna Prasad Vallabh ◽  
Zachary James Cleland ◽  
Cetin Cetinkaya
Keyword(s):  
Wave Propagation ◽  
Additive Manufacturing ◽  
Real Time ◽  
Phononic Crystal ◽  
Production Method ◽  
Structure Design ◽  
Full Potential ◽  
Real Time Monitoring ◽  
Bond Quality

Additive manufacturing (AM) is rapidly becoming a local manufacturing modality in fabricating complex, custom-designed parts, providing an unprecedented form-free flexibility for custom products. However, significant variability in part geometric quality and mechanical strength due to the shortcomings of AM processes has often been reported. Presently, AM generally lacks in situ quality inspection capability, which seriously hampers the realization of its full potential in delivering qualified practical parts. Here, we present a monitoring approach and a periodic structure design for developing test artifacts for in situ real-time monitoring of the material and bonding properties of a part at fiber/bond-scale. While the production method used in current work is filament based, the proposed approach is generic as defects are always due to materials in a bonding zone and their local bonding attributes in any production modality. The artifact design detailed here is based on ultrasonic wave propagation in phononic coupons consisting of repeating substructures to monitor and eventually to assess the bond quality and placement uniformity—not only for geometry but also for defect states. Periodicity in a structure leads to the dispersion of waves, which is sensitive to geometric/materials properties and irregularities. In this proof-of-concept study, an experimental setup and basic artifact designs are described and off-line/real-time monitoring data are presented. As a model problem, the effects of printing speed on the formation of stop bands, wave propagation speeds and fiber placement accuracy in samples are detected and reported.

scholarly journals Using in-situ process monitoring data to identify defective layers in Ti-6Al-4V additively manufactured porous biomaterials

2021 ◽  
Vol 64 ◽  
pp. 1248-1254
Author(s):  
Darragh S. Egan ◽  
Caitríona M. Ryan ◽  
Andrew C. Parnell ◽  
Denis P. Dowling
Keyword(s):  
Process Monitoring ◽  
Monitoring Data ◽  
Porous Biomaterials

scholarly journals Real-time monitoring in three-dimensional hepatocytes reveals that insulin acts as a synchronizer for liver clock

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Daisuke Yamajuku ◽  
Takahiko Inagaki ◽  
Tomonori Haruma ◽  
Shingo Okubo ◽  
Yutaro Kataoka ◽  
...  
Keyword(s):  
Real Time ◽  
Three Dimensional ◽  
Real Time Monitoring
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