AI-Powered Visual Quality Inspection for Production Lines

// Configure camera integration
const inspectionConfig = {
  cameras: [
    { id: 'cam_01', position: 'top', resolution: '4K', fps: 60 },
    { id: 'cam_02', position: 'side', resolution: '4K', fps: 60 },
    { id: 'cam_03', position: 'bottom', resolution: 'HD', fps: 30 }
  ],
  lighting: 'controlled', // Consistent lighting required
  trigger: 'encoder_pulse' // Sync with line movement
};

// Create defect detection model
const model = await client.models.create({
  name: 'product_inspection_v1',
  type: 'defect_detection',
  defect_types: [
    'scratch', 'dent', 'discoloration',
    'missing_component', 'misalignment',
    'contamination', 'dimension_error'
  ]
});

// Upload training images
await client.models.train(model.id, {
  good_examples: 's3://quality-data/good/',
  defect_examples: 's3://quality-data/defects/',
  validation_split: 0.2
});

// Start real-time inspection
const inspection = await client.inspect.start({
  model_id: 'product_inspection_v1',
  cameras: inspectionConfig.cameras,
  actions: {
    defect_detected: {
      reject: true,
      log_image: true,
      alert_operator: { threshold: 0.90 }
    },
    borderline: {
      queue_for_review: true,
      threshold: [0.70, 0.90]
    }
  }
});

// Push inspection results to MES
await client.integrations.connect({
  type: 'mes',
  platform: 'sap', // or siemens, rockwell, etc.
  endpoint: process.env.MES_ENDPOINT,
  sync: {
    inspection_results: true,
    defect_images: true,
    quality_metrics: true
  }
});