# Intelligent Vision Based Wear Forecasting on Surfaces of Machine Tool Elements

arXiv: https://arxiv.org/abs/2106.06839 · HTML: https://mlsanity.com/paper/2106.06839/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [One-Shot Recognition of Manufacturing Defects in Steel Surfaces](https://mlsanity.com/paper/2005.05815/) (2020)
- [Automated Robotic Monitoring and Inspection of Steel Structures and Bridges](https://mlsanity.com/paper/1705.04888/) (2017)

## Abstract (arXiv)

This paper addresses the ability to enable machines to automatically detect failures on machine tool components as well as estimating the severity of the failures, which is a critical step towards autonomous production machines. Extracting information about the severity of failures has been a substantial part of classical, as well as Machine Learning based machine vision systems. Efforts have been undertaken to automatically predict the severity of failures on machine tool components for predictive maintenance purposes. Though, most approaches only partly cover a completely automatic system from detecting failures to the prognosis of their future severity. To the best of the authors knowledge, this is the first time a vision-based system for defect detection and prognosis of failures on metallic surfaces in general and on Ball Screw Drives in specific has been proposed. The authors show that they can do both, detect and prognose the evolution of a failure on the surface of a Ball Screw Drive.