Case Studies
Machine Vision Case Studies show how AI vision detects defects and guides robots. Deployments cover quality inspection, classification, OCR, AR-assisted operations and vision-guided robotics in semiconductor, electronics, automotive, pharmaceutical, medical device and logistics production.
Product Deployments
Case Studies
AccuPick
SolVision
META-aivi
SolMotion
Visual Inspection of Automotive Weld Beads
Powered by AI, SolVision deep learning software trains an AI model by simulating potential weld bead defects in random brightness conditions.
Inspecting Semiconductor Packaging Processes
SolVision enables visual inspection through AI image analysis, strengthening the reliability of displacement and angle information to recognize defective products and errors in the die bonding process.
AI Inspection of Stamped Metal Parts
There are many types of defects that may appear differently each time on the stamped parts, in particular oil or water stains, which are not easily detected.
Automated Visual Inspection for LEDs
Streamlined LED visual inspection with AI SolVision features an Instance Segmentation tool designed to identify various mini LED defects through image processing.
Quality Inspection of Golf Clubs
With AI-based SolVision, production imperfections on club heads can be detected no matter their size, appearance, or location.
Visual Inspection of Stainless Steel Tubes
SolVision’s superior recognition capabilities ensures that OCR of bicycles can be accurately carried out regardless of how the identification numbers look or light refraction levels.
AI Inspection for Aluminum LEDs
SolVision locates and recognizes the smallest defects and production anomalies in aluminum PCBAs through image processing.
AI Visual Identification and Classification of Cells
Deploy SolVision’s advanced AI model to identify and classify cells where traditional AOI systems are insufficient in detecting and determining cell variations.
Quality Control of Wafer Dicing
Using SolVision’s Instance Segmentation tool, irregular lines and multi-drilling defects are labeled in sample images to train the AI model.
Detecting Chipping Defects in Wafer Dicing
The location, size and shape of cracks vary each time, and traditional optical inspection cannot accurately identify such unpredictable defects.