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Journal ArticleDOI: 10.1080/17452759.2021.1905858

Online Convolutional Neural Network-based anomaly detection and quality control for Fused Filament Fabrication process

04 Mar 2021-Virtual and Physical Prototyping (Informa UK Limited)-Vol. 16, Iss: 2, pp 160-177
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...

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Open access
01 Jan 1984-

151 Citations


Journal ArticleDOI: 10.1080/10426914.2021.1954195
Abstract: This research activity deals with 3D printing composites fabricated by Continuous Fiber/Filament Fabrication with an innovative thermoplastic matrix infilled with microcarbon fiber, i.e., Onyx, and...

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Topics: Fiber (57%), Bearing (mechanical) (51%)

3 Citations


Open accessDOI: 10.1016/J.MTBIO.2021.100165
Kun Xue1, FuKe Wang1, Ady Suwardi1, Ming-Yong Han1  +6 moreInstitutions (1)
23 Nov 2021-
Abstract: Biomaterials is an interdisciplinary field of research to achieve desired biological responses from new materials, regardless of material type. There have been many exciting innovations in this discipline, but commercialization suffers from a lengthy discovery to product pipeline, with many failures along the way. Success can be greatly accelerated by harnessing machine learning techniques to comb through large amounts of data. There are many potential benefits of moving from an unstructured empirical approach to a development strategy that is entrenched in data. Here, we discuss the recent work on the use of machine learning in the discovery and design of biomaterials, including new polymeric, metallic, ceramics, and nanomaterials, and how machine learning can interface with emerging use cases of 3D printing. We discuss the steps for closer integration of machine learning to make this exciting possibility a reality.

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Open accessJournal ArticleDOI: 10.1016/J.MATDES.2021.110125
01 Dec 2021-Materials & Design
Abstract: Polyjet, a material jetting 3D printing technique, has been widely used for the fabrication of patient-specific anatomical models owing to the toolless fabrication technique and its ability to print multiple materials in a single part. Although the fabrication of anatomical models with high dimensional accuracy has been demonstrated, 3D printed anatomical models with tissue-mimicking properties have not been realized. In this study, 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. 216 specimens (with 72 combinations of design parameters) were printed and tested to develop the material library for the anatomical models. An analytical model was developed to estimate the effective compressive modulus and shore hardness of the composite laminate. A neural network was used to learn the multi-dimensional relationship between the design parameters and mechanical properties. The 5-33-2 network size is found to be the optimum neural network structure with a mean square error of 0.98% for the compressive modulus, lower than the traditional response surface method model. A genetic algorithm was used to search the design space for the most optimum design parameters for the targeted effective shore hardness.

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52 results found


Open accessProceedings Article
03 Dec 2012-
Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, 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. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

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Topics: Convolutional neural network (61%), Deep learning (59%), Dropout (neural networks) (54%) ... show more

73,871 Citations


Open accessProceedings Article
Karen Simonyan1, Andrew Zisserman1Institutions (1)
01 Jan 2015-
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) 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. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

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49,857 Citations


Open accessProceedings Article
Karen Simonyan1, Andrew Zisserman1Institutions (1)
04 Sep 2014-
Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) 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. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

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38,283 Citations


Open accessPosted Content
Abstract: When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random "dropout" gives big improvements on many benchmark tasks and sets new records for speech and object recognition.

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Topics: Dropout (neural networks) (61%), Feature (computer vision) (58%), Overfitting (57%) ... show more

6,184 Citations


Open accessProceedings ArticleDOI: 10.1109/ICRA.2011.5980567
Radu Bogdan Rusu1, Steve Cousins1Institutions (1)
09 May 2011-
Abstract: With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced point cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of point cloud perception: PCL (Point Cloud Library - http://pointclouds.org). PCL presents an advanced and extensive approach to the subject of 3D perception, and it's meant to provide support for all the common 3D building blocks that applications need. The library contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. PCL is supported by an international community of robotics and perception researchers. We provide a brief walkthrough of PCL including its algorithmic capabilities and implementation strategies.

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Topics: Point cloud (52%)

3,777 Citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20221
20215
19841