Gear Tooth Alignment Analytics is built to bring precision, consistency, and objectivity to ring gear and pinion alignment.
This vision-based system combines advanced computer vision with deep learning AI algorithms to analyze contact patterns on a rouged ring gear. It evaluates pattern length, width, and location on each tooth, delivering clear analytic insight to ensure proper alignment between the pinion and ring gear.
Instead of relying on subjective visual interpretation, the system provides measurable, repeatable results that support confident decision-making on the production floor.
As each gear is inspected, the system automatically analyzes the contact pattern and generates output signals based on overall status and required corrections. It can pass gears based on tolerance across multiple teeth, flag single-tooth deviations, and export inspection data directly to a CSV file for documentation and traceability.
The result is faster inspection, improved consistency, and data-backed alignment verification.
Benefits
Gear Tooth Alignment Analytics delivers objective, AI-driven evaluation of gear tooth contact patterns, reducing reliance on manual interpretation and improving alignment accuracy and repeatability. By automating inspection, the system speeds up inspection cycles while ensuring consistent results across production.
The process also requires minimal rouge, helping reduce material usage and contamination within the gear case. At the same time, inspection data is captured and organized, providing clear documentation that supports traceability and ongoing process improvement.
Key Features
The system uses computer vision and deep learning to automatically analyze gear tooth contact patterns and evaluate alignment. Pass/fail logic is applied based on defined tolerance thresholds, while the system can also detect and flag deviations on individual teeth.
Inspection results are visualized through charted data with X-deviation reporting, and all inspection data can be automatically exported to CSV format for reporting, recordkeeping, and quality tracking.
Core competencies
Ring gear and pinion alignment
Automated inspection
Computer vision and deep learning
Statistical analysis and reporting
Result tracking
