Case Studies
Continental Automotive Selected STATISTICA Data Miner to Classify Image Data for Quality Monitoring
Overview
Analytics & Modeling - Machine Learning Analytics & Modeling - Predictive Analytics | |
Automotive Semiconductors | |
Quality Assurance | |
Machine Condition Monitoring Predictive Maintenance Visual Quality Detection | |
Software Design & Engineering Services System Integration | |
Operational Impact
To assess the quality of the SVM algorithm’s output, ROC curves were used. This image shows the quality of the process. The farther away the curve from the lower left to upper right diagonal runs, the better the separation efficiency of the underlying algorithm. | |
In this case, 10% of the good parts are classified incorrectly to ensure a sufficiently large safety margin in relation to any slip of bad components; this is performed according to the required safety procedures regarding misclassifications. Any bad components are subjected to a second, manual visual inspection. Since STATISTICA’s SVM offers the opportunity to constantly adapt from new errors, the partition function is trained with new fault patterns continuously. | |
The model was integrated directly into the production line and automatically started analyzing solder joints. This has led to significant savings in resources over manual review. Long-term studies to verify the system’s assessments are currently underway and, so far, have demonstrated that incorrect classifications are currently at only 79 ppm, a value that is excellent for a statistical model. | |
Quantitative Benefit
Has reduced incorrect quality assessments to 79 ppm | |