Industrial Machine Tool Component Surface Defect Dataset

• URL: http://arxiv.org/abs/2103.13003v1
• Date: Wed, 24 Mar 2021 06:17:21 GMT
• Title: Industrial Machine Tool Component Surface Defect Dataset
• Authors: Tobias Schlagenhauf, Magnus Landwehr, Juergen Fleischer
• Abstract summary: Using machine learning (ML) techniques in general and deep learning techniques in specific needs a certain amount of data. The manual inspection of machine tool components and the manual end-of-line check of products are labor-intensive tasks. One needs real-world datasets to train and test the models.
• Score: 0.3170655320696991
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 dataset is available under https://doi.org/10.5445/IR/1000129520.

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