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Cast Mold Monitoring and Defect Detection

High-volume cast parts in the aerospace and defense industry rely on the dependability of molds. Since molds are often custom-made and proprietary, preventing crashes and detecting defects on the tooling is critical. Cognex vision systems detect when a part has been ejected from a mold, preventing crashes before they happen, and can identify mold defects such as residue and scratches. 

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Mold Monitoring

The aerospace and defense industries rely on mold making to produce a variety of high-strength, durable, complex parts. Die and permanent casting molds are reusable, expensive, and often proprietary to a specific part or Original Equipment Manufacturer (OEM). Ensuring that tooling is defect-free and operational is fundamental to maintaining quality control, throughput, and managing costs.

Cognex machine vision systems automate mold monitoring by verifying insert seating and confirming part clearance after molding. Powerful image formation technology recognizes components in complex backgrounds while edge AI learns to recognize part characteristics and features for:

  • streamlined processes
  • greater uptime
  • safeguarded operations 
The Essential Guide For Automated Assembly Verification | English

Essential Guide for Automated Assembly Verification

Discover how machine vision can streamline assembly processes and presence/absence checks, minimizing human error and boosting efficiency. 

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Mold Defect Detection

Inspecting molds for subtle defects such as residual material, scratches, and chips is resource-intensive and causes significant downtime. However, failing to recognize these issues can lead to defective parts and complicate downstream processes. Manual inspection is unreliable and subjective, while traditional machine vision and other inspection methods cannot reliably identify defects on reflective, complex molds.

Using advanced AI and robust imaging technology, Cognex machine vision systems separate and classify faint defects. These systems use example-based learning to distinguish between complex mold features and flaws, protecting tooling and optimizing uptime.