Blister Pack Tablet Presence Inspection
Packaging Integrity and Regulatory Compliance
Blister pack tablet presence automated inspection system addresses this challenge with precision, using advanced machine vision technologies that are specifically designed to detect the presence, position, and integrity of each tablet in blister packs. Through real-time image processing, machine learning, and sophisticated optical technologies, this system ensures that each blister pack is thoroughly inspected and all defects are flagged.
Continuous Learning and Self-Calibration
Enhanced Inspection Accuracy The system is not just a static inspection solution; it continually evolves through continuous learning. The deep learning algorithms adapt and optimize based on data collected from each production batch, enhancing their accuracy over time. Additionally, the system performs self-calibration routines to account for environmental changes, variations in lighting, and other external factors that could affect image quality or detection accuracy.
Efficient Tablet Packaging
Real-Time Defect Reporting and Sorting The system features a real-time defect classification and rejection mechanism, which alerts and automatically sorts defective blister packs, directing them to a reject bin. This ensures only correctly packaged products proceed, reducing human error and improving packaging efficiency while maintaining high-quality control.
Multi-Angle Imaging
Complete Detection Coverage The system uses multi-angle, high-resolution cameras to capture 360-degree images of each blister pocket. Advanced optics and lighting detect even the smallest discrepancies, like partially inserted tablets or underfilled pockets, by comparing live images to reference templates, ensuring full coverage and eliminating blind spots.
Achieve Perfect Tablet Packaging
AI-Powered Object Recognition and Verification AI-driven deep learning algorithms to analyze and verify tablet presence in each blister pack. the AI learns from a vast dataset, improving its detection of various tablet shapes, sizes, and orientations. This enables the system to identify missing tablets and defects like tilted tablets, incorrect positioning, or tablets near the cavity edge.
Histom Automated Bottle Neck D
Automated Cap Color Consistenc
Auto Medicine Carton Box Damag
Syringe Scale Line Verificatio