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The surface inspection of steel parts like rolling elements for roller bearings is an essential component of the quality assurance process in their production. Existing inspection systems require high maintenance cost and allow little flexibility. In this paper, we propose the use of a rapidly retrainable convolutional neural network. Our approach reduces the development and maintenance cost compared to a manually programmed classification system for steel surface defect detection. One of the main disadvantages of neural network approaches is their high demand for labeled training data. To bypass this, we propose the use of simulated defects. In the production of rolling elements, real defects are a rarity. Collecting a balanced dataset thus costs a lot of time and resources. Simulating defects reduces the time required for data collection. It also allows us to automatically label the dataset. This further eases the data collection process compared to existing approaches. Combined, this allows us to train our system faster and cheaper than existing systems. We will show that our system can be retrained in a matter of minutes, minimizing production downtime, while still allowing high accuracy in defect detection.

Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation