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AI Visual Inspection on Existing Hardware | SURRO Inspection
Spot subtle defects with your existing inspection equipment and spend less time responding to quality issues. SURRO Inspection is AI visual inspection software that connects image analysis with data collection, defect detection, shop-floor action and model performance management.
Start with the machine vision equipment and internal infrastructure you already use. Add SURRO Inspection’s machine vision software with modules suited to your production environment, connecting inspection data and quality information to defect decisions and shop-floor response.
Automated visual inspection becomes useful on the shop floor when pass/fail decisions lead to action. Prepare the data and inspection model, then connect AI defect detection results to alerts, equipment controls and records. Explore the six stages below.
01 · Collect Inspection Data
Use Data from Existing Inspection Equipment
Capture inspection data from cameras, microscopes, 3D scanners and automated optical inspection (AOI) equipment. That data feeds SURRO Inspection’s AI visual inspection workflow.
DANC · Inspection Data Collection
Cameras · Microscopes · 3D Scanners · AOI→Collect Inspection Data↓
Integrate the collected data
02 · Integrate the Data
Bring Equipment and Quality Data Together
Standardize inspection images, equipment signals and quality data in one place. Link inspection records by production lot to trace quality history.
DFM · Data Integration
Inspection Images · Equipment Signals · Quality Data→Standardize Data · Link Lot History↓
Prepare a model using the integrated data
03 · Prepare the Inspection Model
Reduce Manual Preparation with Automatic Labeling
Select training data and use automatic labeling to reduce the manual work involved in preparing an AI inspection model.
AQM · Inspection Model Preparation
Select Training Data→Automatic Labeling→Prepare the Inspection Model↓
Use the prepared model to detect defects
04 · Detect Defects with AI
See Defect Types, Locations and Extent
Analyze defect type, location and area to support visual inspection and surface defect detection. The model can also flag potential new defects it has not been trained on.
SVI · AI Defect Detection
Inspection Data→Type · Location · Area Analysis→Flag Potential New Defects↓
Connect detection results to shop-floor action
05 · Respond on the Shop Floor
Turn Defect Results into Alerts and Actions
Connect AI defect detection results to operator alarms and equipment interlock signals. Keep quality reports and inspection history alongside those actions to support shop-floor response.
SVI · Shop-Floor Response
Detection Results→Alarms · Interlock Signals→Reports · Inspection History↓
Monitor performance during operation
06 · Manage Model Performance
Keep the Model Aligned with Process Changes
Detect changes in model performance, select new data and retrain the model. Feed the updated model back into AI defect detection.
AQM · Performance Management
Detect Performance Changes→Select New Data→Retrain · Update the Model↺ Model Retraining Loop
06 Performance Management → Select New Data → Retrain → Update the Model
↳ Back to 04 · AI Defect Detection
Adapt Inspection to Your Environment and Keep Models Current
Model performance can change as materials and process conditions change. AQM detects those shifts, selects training data and supports retraining. It links automatic labeling with model updates, then brings updated models back into AI visual inspection to reduce the effort of ongoing operation.
Apply the workflow to inspection targets and criteria across precision electronics and semiconductor components, biotechnology and medical devices, and precision manufacturing. Connect visual inspection and defect detection with data integration and quality history so teams can review results and respond.
