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Industrial machine tool component surface defect dataset

Schlagenhauf, Tobias; Landwehr, Magnus


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: 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

Verlagsausgabe §
DOI: 10.5445/IR/1000142507
Veröffentlicht am 27.01.2022
DOI: 10.1016/j.dib.2021.107643
Zitationen: 6
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 12.2021
Sprache Englisch
Identifikator ISSN: 2352-3409
KITopen-ID: 1000142507
Erschienen in Data in Brief
Verlag Elsevier
Band 39
Seiten Art.-Nr. 107643
Vorab online veröffentlicht am 26.11.2021
Nachgewiesen in Dimensions
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