Industrial machine tool component surface defect dataset

Using machine learning (ML) techniques in general and deep learning techniques in specific needs a certain amount of data often not available in large quantities in technical domains. The manual inspection of machine tool components and the manual end-of-line check of products are labor- intensive t...

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Autores principales:	Tobias Schlagenhauf, Magnus Landwehr
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Condition monitoring Deep learning Machine learning Object detection Semantic segmentation Instance segmentation Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/7b1030773b5d44b2a0fe5a444d56d944
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id	oai:doaj.org-article:7b1030773b5d44b2a0fe5a444d56d944
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spelling	oai:doaj.org-article:7b1030773b5d44b2a0fe5a444d56d9442021-12-04T04:34:38ZIndustrial machine tool component surface defect dataset2352-340910.1016/j.dib.2021.107643https://doaj.org/article/7b1030773b5d44b2a0fe5a444d56d9442021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352340921009185https://doaj.org/toc/2352-3409Using machine learning (ML) techniques in general and deep learning techniques in specific needs a certain amount of data often not available in large quantities in technical domains. The manual inspection of machine tool components and the manual end-of-line check of products are labor- intensive tasks in industrial applications that companies often want to automate. To automate classification processes and develop reliable and robust machine learning-based classification and wear prognostics models, one needs real-world datasets to train and test the models. The presented dataset consists of images of defects on ball screw drive spindles showing the progression of the defects on the spindle surface. The dataset is analysed via an initial object detection model available under: https://github.com/2Obe?tab=repositories. The reuse potential of the dataset lays in the development of failure detection and failure forecasting models for the purpose of condition monitoring and predictive maintenance. The dataset is available under https://doi.org/10.5445/IR/1000129520.Tobias SchlagenhaufMagnus LandwehrElsevierarticleCondition monitoringDeep learningMachine learningObject detectionSemantic segmentationInstance segmentationComputer applications to medicine. Medical informaticsR858-859.7Science (General)Q1-390ENData in Brief, Vol 39, Iss , Pp 107643- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Condition monitoring Deep learning Machine learning Object detection Semantic segmentation Instance segmentation Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390
spellingShingle	Condition monitoring Deep learning Machine learning Object detection Semantic segmentation Instance segmentation Computer applications to medicine. Medical informatics R858-859.7 Science (General) Q1-390 Tobias Schlagenhauf Magnus Landwehr Industrial machine tool component surface defect dataset
description	Using machine learning (ML) techniques in general and deep learning techniques in specific needs a certain amount of data often not available in large quantities in technical domains. The manual inspection of machine tool components and the manual end-of-line check of products are labor- intensive tasks in industrial applications that companies often want to automate. To automate classification processes and develop reliable and robust machine learning-based classification and wear prognostics models, one needs real-world datasets to train and test the models. The presented dataset consists of images of defects on ball screw drive spindles showing the progression of the defects on the spindle surface. The dataset is analysed via an initial object detection model available under: https://github.com/2Obe?tab=repositories. The reuse potential of the dataset lays in the development of failure detection and failure forecasting models for the purpose of condition monitoring and predictive maintenance. The dataset is available under https://doi.org/10.5445/IR/1000129520.
format	article
author	Tobias Schlagenhauf Magnus Landwehr
author_facet	Tobias Schlagenhauf Magnus Landwehr
author_sort	Tobias Schlagenhauf
title	Industrial machine tool component surface defect dataset
title_short	Industrial machine tool component surface defect dataset
title_full	Industrial machine tool component surface defect dataset
title_fullStr	Industrial machine tool component surface defect dataset
title_full_unstemmed	Industrial machine tool component surface defect dataset
title_sort	industrial machine tool component surface defect dataset
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7b1030773b5d44b2a0fe5a444d56d944
work_keys_str_mv	AT tobiasschlagenhauf industrialmachinetoolcomponentsurfacedefectdataset AT magnuslandwehr industrialmachinetoolcomponentsurfacedefectdataset
_version_	1718372976824418304

Industrial machine tool component surface defect dataset

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