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Journal ArticleDOI

Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates

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TLDR
In this paper, a multi-sensor system, including an auxiliary illumination (AI) visual sensor system, an UVV band visual sensor, a spectrometer, and two photodiodes, is established to capture signals of the welding status during high-power disk laser welding.
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This article is published in Journal of Manufacturing Systems.The article was published on 2019-04-01. It has received 88 citations till now. The article focuses on the topics: Welding & Spectrometer.

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Citations
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Journal ArticleDOI

Automated defect inspection system for metal surfaces based on deep learning and data augmentation

TL;DR: A new convolutional variational autoencoder (CVAE) and deep CNN-based defect classification algorithm to solve the problem of automatic defect inspection in the metal manufacturing industry.
Journal ArticleDOI

Real-time penetration state monitoring using convolutional neural network for laser welding of tailor rolled blanks

TL;DR: An innovative monitoring system capable of diagnosing the penetration state during the laser welding process is introduced, which consists of a coaxial visual monitoring platform and a penetration state diagnosis unit based on a convolution neural network.
Journal ArticleDOI

Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature

TL;DR: This fundamental work aims to review the research progress in laser welding monitoring and provide a basis for follow-on research on the potential research problems and challenges based on real-time intelligent monitoring.
Journal ArticleDOI

A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence

TL;DR: This paper surveys the recent advances in vision-based defect recognition and presents a systematical review from a feature perspective, and divides the recent methods into designed- feature based methods and learned-feature based methods, and summarizes the advantages, disadvantages and application scenarios.
Journal ArticleDOI

DeepWelding: A Deep Learning Enhanced Approach to GTAW Using Multisource Sensing Images

TL;DR: A novel framework that applies deep learning techniques to improve gas tungsten arc welding process monitoring and penetration detection using multisource sensing images is presented and it is found that the quality of model prediction is heavily influenced by the data stream collection environment.
References
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Journal ArticleDOI

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Journal Article

Deep Neural Networks for Acoustic Modeling in Speech Recognition

TL;DR: This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.
Journal ArticleDOI

A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

TL;DR: A comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM–FD) based on the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail.
Journal ArticleDOI

Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization

TL;DR: Two online schemes for an integrated design of fault-tolerant control (FTC) systems with application to Tennessee Eastman (TE) benchmark are proposed.
Journal ArticleDOI

Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP]

TL;DR: The purpose of this article is to introduce the readers to the emerging technologies enabled by deep learning and to review the research work conducted in this area that is of direct relevance to signal processing.
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