Industrial Machine Tool Component Surface Defect Dataset
This provides a resource for automating labor-intensive manual inspections in industrial settings, but it is incremental as it focuses on dataset creation rather than new methods.
The authors addressed the lack of real-world data for machine learning in industrial applications by creating a dataset for surface defect classification and wear prognostics of machine tool components, making it publicly available under a DOI.
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.