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AI-powered Defects Detection System for Alloy Wheel Manufacturer

POC ALIGNED WITH GM<br> STANDARDS<br> PILOT EXECUTION ON A<br> SINGLE LINE<br> SCALE-UP ACROSS ALL<br> PRODUCTION LINES<br> DEPLOYMENT OF<br> INDUSTRIAL HIGH<br>RESOLUTION CAMERAS<br> CONTROLLED LIGHTING FOR<br> CONSISTENT IMAGING<br> USE OF CNN AND SEMANTIC<br> SEGMENTATION MODELS<br> REAL-TIME PROCESSING<br> WITH EDGE COMPUTING<br> AUTOMATED DEFECT<br> DETECTION AND<br> CATEGORIZATION<br> TRIGGERED REJECTION FOR<br> IDENTIFIED DEFECTS<br>

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AI-powered Defects Detection System for Alloy Wheel Manufacturer

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  1. AI-POWERED DEFECTS DETECTION SYSTEMFOR ALLOYWHEEL MANUFACTURER

  2. MANUAL INSPECTIONPROCESSES IN ALLOYWHEELMANUFACTURING WERE INCONSISTENT,SLOW, AND ERROR-PRONE,LEADING TO HIGH DEFECTRATES AND COMPROMISED QUALITY. PROBLEMSTATEMENT AND CHALLENGES

  3. POCALIGNEDWITHGM STANDARDS PILOTEXECUTIONONA SINGLELINE SOLUTION APPROACH SCALE-UPACROSSALL PRODUCTIONLINES DEPLOYMENTOF INDUSTRIALHIGH- RESOLUTIONCAMERAS CONTROLLEDLIGHTINGFOR CONSISTENTIMAGING USEOFCNNANDSEMANTIC SEGMENTATIONMODELS REAL-TIMEPROCESSING WITHEDGECOMPUTING AUTOMATEDDEFECT DETECTIONAND CATEGORIZATION TRIGGEREDREJECTIONFOR IDENTIFIEDDEFECTS

  4. VALUECREATION Quantified Benefitsin6Months: 95%+defectdetectionaccuracy;70%dropinfalsenegatives. 90%reductioninmanualinspectionlabor. 30%increaseinproductionthroughput. 25%dropinreturnsandrework. EnhancedtraceabilityandauditreadinessforOEMs. ROI Projection: FinalROI willbedeterminedwithin9–12months,factoring savingsfromlabor,reducedscrap,andcompliance improvements.

  5. GOAL1 AI&ComputerVisionStack: CV&MLLibraries:OpenCV, YOLOv8,TensorFlow,PyTorch ModelTypes:CustomCNNs, SemanticSegmentation,Transfer Learning GOAL1 Hardware&Infrastructure: 4KIndustrialCameras,GPenabled EdgeDevices(NVIDIAJetson) LightingSystem:1000±100Lux Annotation:CVAT,Labelbox Integration:MES,SecureEncrypted DataFlow&AccessControls TECHNOLOGIES USED

  6. SCAN HERE TOSEE A DEMOONYOUR DATASETS THANK YOU

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