ML for industry
noze applies machine learning techniques to the industrial sector along two main tracks.
Defect detection
Automatic classification and visual analysis models are used for defect detection in manufacturing processes. The systems analyse images and sensor data on the production line, identifying anomalies and non-conformities with higher precision than manual inspection.
Techniques include:
- Image classification for surface defect recognition
- Pattern recognition on multi-dimensional sensor data
- Adaptive thresholds based on statistical and learning models
Production optimisation
In parallel, noze develops predictive models for production process optimisation: analysis of correlations between process parameters and finished product quality, identification of bottlenecks, prediction of machine downtime.
The approach builds on the experience gained since 2006 with the first pattern recognition and automatic classification projects, now extended with more mature machine learning techniques and larger industrial datasets.