Because stators interact with the rotor to produce torque, they are fundamental to driving motor shafts and propelling vehicles. This application focuses on the inspection of stator cores after they’re formed and before winding.
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What manufacturing defects occur?
Many manufacturing defects can occur during the stator core formation process, such as:
These defects can significantly affect the performance, efficiency, and reliability of electric motors. By detecting them and reworking stators before the winding process, manufacturers can save time and resources.
However, these defects can be difficult to detect– stator core surfaces may reflect light in a way that masks defects. And many stator core defects are subtle and hard to image, especially if the defect’s contrast to the background is low. Traditional machine vision systems struggle to capture clear images and distinguish between actual defects, reflective surfaces, and the background, ultimately missing defects or causing false rejections.
Static cores have complex geometries due to their thin, closely spaced laminations that can be hard for machine vision systems to inspect. And stator core defects are highly variable, each requiring specialized lighting and angles to detect.
And in high-volume manufacturing environments, stators must be inspected quickly to keep up with production rates. Traditional machine vision products may fail to keep up with required cycle times.
The Solution
UnitX’s AI-powered inspection effectively inspects stator core defects where other solutions fail.
First, the OptiX imaging system illuminates and images the stator core. Then, the CorteX Central AI platform is trained on stator core defects and dimensional measurements. Lastly, those AI models are deployed to the CorteX Edge inference system to detect and classify defects and dimensional inaccuracies in-line.
Why UnitX for stator core inspection?
OptiX provides superior images that minimize reflectivity while maximizing defect visibility. It has 32 independently controllable lighting sources that can be optimized for stator core surfaces and various defects via software. Its computational imaging capability can be used to take multiple shots and eliminate hotspots caused by highly reflective stator core surfaces. And its lighting dome design supports a very acute incidence angle of projected light, causing even very tiny defects to cast shadows which increase their visibility.
CorteX accurately detects random, complex defects. It automatically normalizes for variability in positions and orientations and recognizes defects down to the pixel-level. It reduces false positives that lead to scrap and wasted product.
CorteX measures features dimensions to ensure parts conform to dimensional specifications and are produced within the acceptable tolerance limits.
CorteX supports fast AI model development, deployment, and iteration. CorteX AI models are sample efficient– they only require a few images to train on new defect types.
UnitX optimizes yield. In CorteX, can tune quality criteria and visualize the impact on yield before rolling those changes to production. All inspection data is referenceable in one central platform for manufacturers to analyze and identify areas for process improvements.
UnitX provides rapid, 100% inline inspection. OptiX has bright LEDs and fast fly capture speeds of 1m/s for high speed imaging. And CorteX Edge supports high inference speeds (up to 100 MP) to quickly output an OK/NG decision, seamlessly communicating that decision via integration to all major PLC, MES, and FTP systems.
Manufacturers who use UnitX to automate stator core inspection are able to:
- Prevent quality escapes that impact automobile reliability, performance, and safety
- Reduce scrap by minimizing false rejection rates common with traditional machine vision and identifying defects early in the manufacturing process
- Improve yield by analyzing production and quality data for process improvement opportunities
- Automate inspection at the speed of their production to increase stator core manufacturing throughput
