Because engine water pumps prevent overheating, they are fundamental to an engine’s performance and longevity. This application focuses on engine water pump inspection after the casting process.
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What manufacturing defects occur?
Engine water pumps can encounter defects throughout the manufacturing process that impact their functionality and lifespan. Common defects resulting from the casting process include:
Quality control measures are essential to casting components that, once assembled into a water pump unit, are strong, leak-proof, and support the proper functioning and longevity of the cooling system. By catching these defects early after the casting process, manufacturers avoid wasting resources and prevent quality escapes before water pumps are installed within engines and become harder to reach.
However, these defects can be difficult to detect– many engine water pump components are made of cast iron and steel, surface materials that may reflect light in a way that masks defects. And many 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.
The Solution
UnitX’s AI-powered inspection effectively detects engine water pump casting defects where other solutions fail.
First, the OptiX imaging system illuminates and images pumps. Then, the CorteX Central AI platform is trained on casting defects. Lastly, those AI models are deployed to the CorteX Edge inference system to detect and classify defects in-line.
Alternatively, manufacturers can use just CorteX AI if they have existing imaging systems. For example, if a manufacturer wants to detect internal water pump defects, they can deploy just CorteX AI and integrate it with existing X-Ray and CT Scanners for fast deployment of improved defect detection.
Why UnitX for pump casting inspection?
UnitX’s AI-powered inspection effectively automates engine water pump casting inspection.
OptiX provides superior images that minimize reflectivity while maximizing defect visibility. It has 32 independently controllable lighting sources that can be optimized for engine water pump surfaces and various defects via software. Its computational imaging capability can be used to take multiple shots and eliminate hotspots caused by highly reflective metal 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 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 engine water pump casting inspection are able to:
- Prevent quality escapes that impact automobile reliability and performance
- Reduce scrap by minimizing false rejection rates common with traditional machine vision
- Improve yield by analyzing production and quality data for process improvement opportunities
- Automate inspection at the speed of their production to increase pump manufacturing throughput
