Injection Molded Rubber Inspection Using AI

Case

Injection Molded Rubber Inspection

The injection molded rubber process is influenced by various factors, including raw material quality, machine specifications, mold design, and injection parameters. Potential defects can arise, ranging from minor aesthetic issues like stains and scratches to severe structural damage caused by inadequate materials or mishandling during ejection. Effective rubber inspection is crucial for identifying these defects, ensuring quality control, and optimizing production processes with advanced solutions like AI.

Interface of SolVision AI vision system software performing injection molded rubber inspection

Challenge

Limitations of Traditional Inspection Methods

Conventional rule-based vision systems depend on extensive data for training but struggle with the dynamic nature and varying locations of defects in injection molded rubber, resulting in low accuracy rates. Additionally, manual inspection lacks standardization and is often too slow and inconsistent for effective inspection and quality control. These challenges highlight the need for advanced AI solutions to improve rubber inspection processes.

Solution

Precise Quality Inspection with SolVision

SolVision AI employs deep learning to inspect injection molded rubber by analyzing sample images to learn the distinct characteristics of various defects, enabling precise defect detection and identification. As the database is enriched with additional images, the AI model improves, further enhancing the system’s accuracy in defect recognition. This advancement streamlines the rubber inspection process, providing manufacturers with faster, more reliable quality control.

Injection Molded Rubber Defect Detection

Uneven incisions defects on a rubber injection molding part

Uneven Incisions

Missing material defects on a rubber injection molding part

Missing Material

Mold crush defects on a rubber injection molding part

Mold Crush

Stain defects on a rubber injection molding part

Stain

Outcome

Streamlined the quality control process
Enabled precise detection of various injection molded rubber defects
Constantly improved detection recognition through deep learning