Industrial Machine Tool Component Surface Defect Dataset

TL;DR

This paper introduces a dataset of surface defects on industrial machine tool components to facilitate the development of machine learning models for automated defect detection and wear prognosis in manufacturing.

Contribution

It provides a real-world dataset specifically designed for training and testing machine learning models in industrial defect classification tasks.

Findings

Dataset enables improved ML model training for defect detection.

Facilitates research in automated quality control.

Supports development of robust wear prognostics models.

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.