Journal ArticleDOI
Online Convolutional Neural Network-based anomaly detection and quality control for Fused Filament Fabrication process
Jiaqi Lyu,Souran Manoochehri +1 more
TLDR
Two developed PID-based online closed-loop control systems are implemented which can significantly reduce the height deviation errors between the fabricated part measurements and design values, and correct the in-plane surface anomalies.Abstract:
Additive Manufacturing (AM) technologies are experiencing rapid growth in the past decades. Critical objectives for the AM processes are how to ensure product quality and process consistency. The d...read more
Citations
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Journal ArticleDOI
Key parameters controlling surface quality and dimensional accuracy: a critical review of FFF process
TL;DR: In this article , a cause-and-effect diagram exhibits the process parameters' categorization with the surface quality (SQ) and dimensional accuracy (DA) of the manufactured parts.
Journal ArticleDOI
Machine learning for 3D printed multi-materials tissue-mimicking anatomical models
Guo Dong Goh,Swee Leong Sing,Yuan Fang Lim,Jia Li Janessa Thong,Zhen Kai Peh,Sreenivasulu Reddy Mogali,Wai Yee Yeong +6 more
TL;DR: In this article, a composite layering design was used to tune the shore hardness and compressive modulus of the polyjet-printed parts in an attempt to mimic the properties of human tissues.
Journal ArticleDOI
Real-time anomaly detection using convolutional neural network in wire arc additive manufacturing: molybdenum material
TL;DR: In this article , a real-time anomaly detection method that uses a convolutional neural network (CNN) in wire arc additive manufacturing (WAAM) is presented, which enables creation of CNN-based models that detect abnormalities by learning from the melt pool image data, which are pre-processed to increase learning performance.
Journal ArticleDOI
On the critical technological issues of CFF: enhancing the bearing strength
TL;DR: In this paper, the authors deal with 3D printing composites fabricated by Continuous Fiber/Filament Fabrication with an innovative thermoplastic matrix infilled with micro carbon fiber, i.e., Onyx, and...
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Posted Content
Improving neural networks by preventing co-adaptation of feature detectors
TL;DR: The authors randomly omits half of the feature detectors on each training case to prevent complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors.
Proceedings ArticleDOI
3D is here: Point Cloud Library (PCL)
Radu Bogdan Rusu,Steve Cousins +1 more
TL;DR: PCL (Point Cloud Library) is presented, an advanced and extensive approach to the subject of 3D perception that contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
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