Quality Prediction

Overview

Quality prediction systems use computer vision and sensor fusion to detect defects, assess product quality, and predict process outcomes in real-time. Our solutions integrate seamlessly into production lines, providing instant feedback to operators and enabling automated reject mechanisms—reducing defects, minimizing waste, and ensuring consistent product quality.

Key Features

Technical Approach

Our quality prediction pipeline ensures accurate, reliable detection:

• Data Collection: Capture high-resolution images and sensor data under controlled lighting and positioning
• Labeling & Annotation: Build comprehensive defect taxonomies with expert-validated ground truth labels
• Model Training: Train CNNs (ResNet, EfficientNet) with data augmentation and class balancing for rare defects
• Calibration: Adjust detection thresholds to balance false positives vs. false negatives for operational requirements
• Deployment: Edge deployment on inspection stations with <10ms inference for real-time line speeds

Use Cases

Quality prediction transforms manufacturing operations:

• Surface Inspection: Detect scratches, dents, and surface irregularities on automotive parts, electronics, and consumer goods
• Dimensional Verification: Measure part dimensions and verify tolerances using computer vision metrology
• Assembly Verification: Confirm correct assembly, component presence, and proper orientation
• Print Quality: Inspect labels, packaging, and printed materials for legibility and accuracy

Expected Outcomes

Quality prediction delivers immediate quality improvements:

• 99%+ defect detection accuracy with <0.5% false positive rate
• 50-70% reduction in customer complaints through improved quality
• 30-40% reduction in manual inspection labor costs
• 100% inspection coverage vs. statistical sampling with manual QA