AI Inspection and Analysis
Application-Specific Intelligence for Industrial Inspection
LM3 applies machine learning to locate defects, recognize visual conditions, and evaluate manufactured components against defined inspection requirements. Our capabilities address variations in surface appearance, defect characteristics, and assembly conditions that can be difficult to evaluate through fixed inspection rules alone.
Models are developed around application-specific production imagery, part families, and camera views. Their outputs support localized defect identification, condition classification, and coordinated part-level inspection decisions.
AI Defect Detection
Locate Defects Across Complex Components
LM3 applies artificial intelligence to inspection tasks involving variable appearance, complex defect characteristics, and manufacturing conditions that are difficult to express through fixed rules alone. Our approach combines application-specific models with defined inspection criteria, allowing learned visual recognition to contribute to controlled production decisions.
Inspection methods are selected around the required output: classifying a condition, locating a defect, identifying an affected region, or combining multiple findings into a part-level result. Model selection also considers the available training data, feature visibility, production variation, and deployment requirements.
Object Detectors
Models such as YOLO locate and classify defects or components using bounding boxes. This approach supports missing-component checks, defect counting, and localized inspection of scratches, contamination, and assembly features.
Model Training & Deployment
Industrial 3D sensing technologies generate detailed geometric data that can be visualized and analyzed for dimensional verification, surface inspection, and feature measurement.
Training AI for Production
LM3 trains inspection models using labeled images of acceptable parts and relevant defect conditions. Model architecture, dataset balance, and training parameters are tailored to the application’s features, production variation, and required outputs.
Models are evaluated against examples held apart from training to assess recognition performance, missed defects, and false rejects. These results guide targeted refinement and controlled model revisions before production release.