AiV Completes Deployment of Deep Learning Solution for Defect Inspection at Automotive Parts Manufacturer Central

AiV Successfully Deploys Deep Learning Inspection Solution for Ball Joint Manufacturing at Central Corporation

AiV (CEO: Sung Min-soo), a leading provider of deep learning-based machine vision solutions, announced on the 15th that it has successfully completed the deployment of its AI-powered inspection solution for ball joints at Central Corporation, a global automotive component manufacturer. Ball joints inspected using AiV's AI solution are supplied to major automotive manufacturers worldwide, including Hyundai Motor Group, General Motors (GM), Ford, Mercedes-Benz, and Porsche.

The deep learning inspection solution supplied by AiV is designed to inspect ball joints—critical components of automotive steering systems—for manufacturing defects using advanced deep learning technology. The solution consists of proprietary AI inspection software integrated with a fully automated inspection system.

The AI inspection software utilizes AiV's proprietary deep learning neural network developed specifically for manufacturing inspection applications. The model was trained using image datasets containing irregular surface defects found on ball joints. In addition, AiV designed, engineered, and manufactured a customized automated inspection system tailored to the customer's production environment by combining advanced hardware design with proprietary optical engineering technologies.

AiV's deep learning inspection solution is capable of detecting a wide range of defects occurring in both machined metal components and rubber components that make up ball joints, including dents, foreign materials, deformation, and other surface abnormalities, with greater accuracy than conventional manual visual inspection. The solution also applies deep learning-based optical character recognition (OCR) to identify mechanically stamped alphanumeric characters while simultaneously detecting subtle overlapping or double-stamping defects. Furthermore, the system incorporates a six-axis articulated robot and rotational inspection methodology, enabling complete 360-degree inspection of complex three-dimensional products.

The deployed inspection solution achieved a 100% defect detection rate while maintaining a false positive rate below 0.5%, significantly outperforming conventional manual inspection in both accuracy and consistency. The implementation has resulted in improved production efficiency, streamlined manufacturing processes, enhanced product quality, and reduced manufacturing costs.

Building upon this successful mass-production deployment, AiV plans to expand its inspection solution business by focusing on manufacturers in the materials and components industry that require highly optimized, precision inspection systems. The company also aims to expand its AI inspection software business by providing software solutions to large manufacturers and specialized equipment suppliers, where market demand continues to increase.

In addition, AiV is developing a universal 3D metrology system capable of replacing conventional physical inspection fixtures used for dimensional verification of complex-shaped products, further broadening its smart manufacturing solution portfolio.

Kang Sang-woo, President of Central Corporation, commented:

"By introducing AiV's deep learning inspection solution into the quality inspection process for ball joints—which consist of multiple subcomponents including machined metal and extruded rubber parts—we have dramatically improved inspection accuracy for defects such as dents, foreign materials, and deformation. To further enhance quality assurance and strengthen our cost competitiveness, multiple affiliates within the Central Group are actively deploying AiV's deep learning inspection solutions."

Sung Min-soo, CEO of AiV, added:

"One of the key strengths of AiV's deep learning inspection solution is our ability to provide fully customized software and hardware systems optimized for each customer's smart factory environment. We will continue expanding into broader manufacturing sectors, including automotive components and display equipment, while accelerating our global expansion to become a leading deep learning machine vision company."