About: Development of Real-time Diagnosis Framework for Angular Misalignment of Robot Spot-welding System Based on Machine Learning

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About: Development of Real-time Diagnosis Framework for Angular Misalignment of Robot Spot-welding System Based on Machine Learning Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Development of Real-time Diagnosis Framework for Angular Misalignment of Robot Spot-welding System Based on Machine Learning
Creator	Lee, Sang Jeong, Sang Lee, Jiwoong Lee, Yongho Noh, Inwoong
topic	covid:7f902ad1ad7d86c2d48c0caa127b7f49a4ffe565#this
Source	Elsevier
abstract	Abstract This paper focuses on the real-time online monitoring and diagnosis framework for the angular misalignment of the robot spot-welding system, which can result in significant quality degradation of a weld nugget such as porosity. The data-driven approach is applied by installing the voltage and current sensors, collecting the associated mass data and processing them under normal and abnormal (angular misalignment) conditions. Two categories of features are extracted from the dynamic resistance (DR) and the voltage and current ones that are decomposed by wavelet transform (WT). The DR features are extracted from the DR profile and some critical features are selected by a t-test methodology. In the case of the WT-based features, the critical ones are selected by a max-relevance and min-redundancy (mRMR) and a sequential backward selection (SBS) wrapper. Consequently, three types of critical feature sets, such as DR features, WT features, and hybrid features combining those, are prepared to train machine learning-based models. Support vector machine (SVM) and probabilistic neural network (PNN) are applied to establish the diagnosis models, and the diagnostic accuracy and robustness are evaluated. Finally, the software for the on-line monitoring and diagnosis for angular misalignment of robot spot-welding system is developed and demonstrates its real-time applicability in an industrial site.
has issue date	2020-12-31(xsd:dateTime)
bibo:doi	10.1016/j.promfg.2020.05.140
has license	els-covid
sha1sum (hex)	7f902ad1ad7d86c2d48c0caa127b7f49a4ffe565
schema:url	https://doi.org/10.1016/j.promfg.2020.05.140
resource representing a document's title	Development of Real-time Diagnosis Framework for Angular Misalignment of Robot Spot-welding System Based on Machine Learning
schema:publication	Procedia Manufacturing
resource representing a document's body	covid:7f902ad1ad7d86c2d48c0caa127b7f49a4ffe565#body_text
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is schema:about of	named entity 'current' named entity 'processing' named entity 'WELDING' named entity 'TYPES' named entity 'NORMAL' named entity 'MASS' named entity 'ESTABLISH' named entity 'DATA' named entity 'SELECTION' named entity 'METHODOLOGY' named entity 'PROFILE' named entity 'BASED' named entity 'DIAGNOSIS' named entity 'EXTRACTED' named entity 'diagnosis' named entity 'Support vector machine' named entity 't-test' named entity 'dynamic' named entity 'voltage' named entity 'machine' named entity 'evaluated' named entity 'current' named entity 'decomposed' named entity 'mutual information' named entity 'welding' named entity 'cross validation' named entity 'wavelet' named entity 'welding' named entity 'quality management' named entity 'mutual information' named entity 'on-line' named entity 'off-line' named entity 'vehicle production' named entity 'machine learning' named entity 'wavelet' named entity 'product quality' named entity 'sigmoid function' named entity 'optical microscope' named entity 'welding' named entity 'signal processing' named entity 'vertical angle' named entity 'real-time' named entity 'on-line' named entity 'welding' named entity 'modeling process' named entity 'Ultrasonic' named entity 'on-line' named entity 'real-time software' named entity 'PNN' named entity 'voltage sensor' named entity 'wavelet' named entity 'automated processes' named entity 'probabilistic neural network' named entity 'electrode' named entity 'SVM' named entity 'welding' named entity 'welding' named entity 'mutual information' named entity 'welding' named entity 'inflection points' named entity 'statistical method' named entity 'non-destructive testing' named entity '1, 2' named entity 'welding' named entity 'signal processing' named entity 'histograms' named entity 'sensor' named entity 'robot' named entity 'data collected' named entity 'on-line' named entity 'signal processing' named entity '1, 2' named entity 'sampling frequency' named entity 'probabilistic neural network' named entity 'signal processing' named entity 'welding' named entity 'Ultrasonic' named entity 'wavelet'

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