Manufacturers need consistent methods to identify surface defects and measure critical dimensions. Manual inspection can be time-consuming, while image quality, increasingly fine tolerances, and changing production conditions make reliable automated inspection difficult.
Technica AI combines conventional image processing and deep learning according to the inspection task, supporting defect detection, classification, dimensional measurement, and rule-based pass/fail assessment. We also consider imaging conditions, processing hardware, and integration with production and quality systems.
Reliable automated inspection requires more than an accurate model.
- Manual inspection varies with experience
Interpretation of scratches, dents, discoloration, and other defects can differ among inspectors and operating conditions. - Fine measurements demand suitable imaging
Very small features may require specialized imaging and calibrated measurement methods. - Training and imaging data are imperfect
Noise, low contrast, changes in lighting, and limited annotated samples can affect the robustness of recognition models. - Production deployment adds system constraints
Cameras, lighting, sensors, processing hardware, line integration, and required cycle times must be considered alongside model performance.
Design the complete inspection process around the target.
- Detect defects and unusual patterns
Use task-appropriate image analysis to identify scratches, dents, discoloration, contamination, and other visual anomalies; classify known defects and flag unusual patterns for review. - Measure critical dimensions from images
Extract relevant dimensions—such as thickness, length, area, angle, and curvature—and compare them with specified tolerances using calibrated methods. Prior work in semiconductor measurement has informed our approach to fine-feature analysis. - Adapt preprocessing to challenging images
Improve image clarity and isolate relevant features before analysis, including for microscopy and display-manufacturing images. - Select the right mix of rules and learning
Apply conventional image processing where deterministic methods are effective and deep learning where image variation or complex patterns call for it, balancing accuracy, speed, and computing needs. - Integrate inspection with production workflows
Design the flow from image capture to measurement, review, pass/fail criteria, and reporting, with interfaces to existing equipment and quality management systems as required.
Make inspection more consistent, measurable, and scalable.
- Consistent inspection criteria
Apply defined measurement and defect-classification rules across inspections.
- Less manual measurement work
Automate selected fine-dimensional measurements where validated performance permits.
- Support for sustained operation
Design processing and equipment integration for the production cycle times required.
- Integration with existing lines
Connect image capture, analysis, decisions, and quality records.
- Broader anomaly detection
Flag unusual patterns beyond the set of previously labeled defect classes.
Build inspection systems—not isolated AI models.
A production-ready inspection workflow brings together appropriate imaging, image processing, AI, calibrated measurement logic, and equipment integration. Technica AI designs these components as one system, with performance and acceptance criteria validated for each application.