front car headlight on a metallic yellow car

META-aiviCase Study

Automotive Parts Quality Control Using AI


The customer is an automotive LED lighting manufacturer and supplier for major international car brands. Their product range includes headlights, taillights, turn signal lights, clearance marker lights, backup lights, and license lights, for various vehicle types including cars, trucks, motorcycles, and ATVs.


Optimizing Headlight Quality Control After Assembly

The customer sought to optimize the quality control process for car headlights after assembly, ensuring the presence and correctness of all fixtures on individual headlight units.

front car headlight isolated on a white background


Human Error and Fatigue Risks During Product Inspection

Manual inspection of car headlights after assembly presents significant challenges, including the risk of human error in identifying intricate components and detecting incorrectly colored parts. The repetitive nature of the task and volume of units that need to be inspected also increases the likelihood of fatigue-induced oversights. Overcoming these challenges is essential for improving accuracy and minimizing errors in the quality control process.


Quality Control Enhanced By META-aivi

Solomon’s AR + AI vision system, META-aivi, seamlessly integrates into the inspection workstation, utilizing a fixed IP camera for AI-driven supervision during the post-assembly quality inspection process. META-aivi digitizes the procedure with the precision and efficiency of AI, detecting the presence or absence of fixtures and ensuring the confirmation of correctly colored parts. This markedly reduces inspection time and mitigates the risk of human error, providing a streamlined and reliable quality control process.


Minimized human error during inspection
Reduced inspection cycle time
Digitized quality control inspection process

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