Automobile

Project Details:

In the automotive manufacturing process, the quality of accessory production is intrinsically linked to the safety and reliability of the entire vehicle. Automotive accessories, such as brackets and supports, typically require multiple hole positions to facilitate assembly and connection. However, issues such as double punching, missing holes, and misalignment are prevalent defects in production that can not only hinder proper assembly but also compromise the overall safety of the vehicle. Traditional manual inspection methods struggle to meet the demands of modern production due to their inefficiency and susceptibility to error, making the introduction of machine vision inspection systems particularly crucial.

This project employs high-resolution industrial cameras, complemented by oblique lighting techniques, to ensure the acquisition of clear images while mitigating interference from light reflections. Advanced image processing technologies and deep learning algorithms can automatically identify and detect hole positions on the accessories, accurately distinguishing between normal holes, double punches, missing holes, and misalignments.

By enabling real-time monitoring of the production process, this system not only enhances inspection efficiency but also facilitates data traceability, ensuring that the quality status of each component is effectively managed. Following the implementation of this system, the inspection efficiency for automotive accessories has significantly improved, with accuracy surpassing 98%, thereby considerably reducing both missed detections and false positives, and effectively minimizing labor costs and material waste.