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Verus® Research's $1 Million contract monitors anomalies for Space Vehicles Directorate of the Air Force Research Laboratory – SatNews

Verus® Research's $1 Million contract monitors anomalies for Space Vehicles Directorate of the Air Force Research Laboratory – SatNews
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United States , J Mark Delgrande , Jacob Lutz , Verus Research , Harris Corporation , Navigation Technology , Phase Ii Small Business Technology Transfer , University Of Colorado , Research Laboratory Space Vehicles Directorate , Verus Research Satellite Software For Anomaly Monitoring , New Mexico Based , Space Vehicles Directorate , Air Force Research Laboratory , Business Technology Transfer , Satellite Software , Anomaly Monitoring , Northrop Grumman ,

"Machine Learning in Process Monitoring and Control for Wire-Arc Additi" by Yuxing Li, Haochen Mu et al.

Wire-arc additive manufacturing (WAAM) is an arc-based directed energy deposition approach that uses an electrical arc as a source of fusion to melt the wire feedstock and deposit layer by layer. It’s applicable in fabricating large-scale components. At this stage, there are still some issues that need to be researched deeply, such as manufacturing accuracy control, process parameters optimization, path planning, and online monitoring. Machine learning is a new emerging artificial intelligence technology, which is more and more applied in modern industry. In this study, a machine learning based control algorithm was applied in melt pool width control. To monitor the WAAM process, deep learning algorithms were applied in anomalies recognition. At the same time, machine learning methods were employed to predict the deposited surface roughness during the WAAM process. ....

Additive Manufacturing , Anomaly Monitoring , Deep Learning , Machine Learning ,

"Vision-based melt pool monitoring for wire-arc additive manufacturing " by Chunyang Xia, Zengxi Pan et al.

Wire-arc additive manufacturing (WAAM) technology has been widely recognized as a promising alternative for fabricating large-scale components, due to its advantages of high deposition rate and high material utilization rate. However, some anomalies may occur during the deposition process, such as humping, spattering, robot suspend, pores, cracking and so on. This study proposed to apply deep learning in the visual monitoring to diagnose different anomalies during WAAM process. The melt pool images of different anomalies were collected for training and validation by a visual monitoring system. The classification performance of several representative CNN (convolutional neural network) architectures, including ResNet, EfficientNet, VGG-16 and GoogLeNet, were investigated and compared. The classification accuracy of 97.62%, 97.45%, 97.15% and 97.25% was achieved by each model. The results proved that the CNN models are effective in classifying different types of melt pool images of WAAM. ....

Additive Manufacturing , Anomaly Monitoring , Computer Vision , Deep Learning ,

Artificial Intelligence of Things Market Predictable to Booming Worldwide | Tachyus, SAS, IBM, HPE, SAP, Microsoft

Artificial Intelligence of Things Market Predictable to Booming Worldwide | Tachyus, SAS, IBM, HPE, SAP, Microsoft
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United States , Research Media , Cloud It Services , Research Newswire , Key Development , Services Robot , Technology Big Data , Major Companies , Sentrain Inc , Offerings Software , Industrial Inspection Services , See Campaign , Key Companies Breakdown , Iot Financial Services , Global Artificial Intelligence , Augury Systems , Artificial Intelligence , Things Market Study , Robot Based Industrial Inspection Services , Natural Language Processing , Smart Home Monitoring Systems , End Users , Manufacturing Sector , Big Data , Cognitive Computing , Deep Learning ,