TITLE: Industrial Machine Tool Component Surface Defect Dataset 

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.
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.
Industrial Machine Tool Component Surface Defect Dataset
http://arxiv.org/abs/2103.13003v1