Currently, the self-driving technology of smart cars has once again fallen into the debate between LiDAR and pure vision solutions. Although both aim to achieve autonomous driving, the logic behind them is vastly different.

In automotive factories, various sensors are crucial for automation. Today, we are in the era of artificial intelligence, encompassing AI data analysis on production lines, visual recognition, robotic control systems, and more, which share similarities with current autonomous driving systems.

As automotive production automation continues to advance, the proliferation of vision systems and their underlying technologies such as visual recognition and AI intelligence systems is profoundly impacting various aspects of automotive factories, including part identification, appearance, dimensions, and quality inspection processes.

1. Main application scenarios of vision systems in automotive factories

Quality inspection: High-resolution cameras, advanced image processing algorithms, and AI algorithms enable real-time monitoring of defects in various components and body structures on the production line (e.g., poor welding, surface imperfections) for efficient detection and classification, ensuring product quality.

For paint defects during automotive painting (e.g., scratches, dirt, craters, orange peel, runs), vision systems combined with AI technology achieve efficient and stable automated inspection. Through multi-directional ultra-high-definition cameras and image processing algorithms, the system can identify and mark paint defects in real-time.

Automated robot assembly: Vision recognition technology enables robots to "see" and accurately identify and grasp different components. Robots can accurately locate parts, their orientations, and types for more flexible automated assembly, supporting automakers in creating flexible production lines.

Vision systems are widely used in automotive manufacturing automation lines to guide robots for precise grasping and assembly. For instance, vision recognition technology enables robots to stably grasp and efficiently assemble components like engine blocks and battery housings.

2. Classic application cases of vision systems in automotive factories

3D vision body inspection: BMW Group introduced 3D vision systems in its German factories for body quality inspection. Using multi-dimensional scanning technology, it captures minute deformations and surface defects, compares them with design models, enhancing inspection accuracy and efficiency.

Intelligent robotic vision system: Ford deployed intelligent robotic vision systems in its Michigan, USA, factory. Combining 2D and 3D vision technologies, robots autonomously identify and grasp complex parts on the production line for precise assembly. The robots also adjust gripping force and position based on subtle part variations, improving assembly efficiency and accuracy.

AI vision system for welding quality inspection: Toyota deployed an AI-based vision inspection system in its Japanese factories for welding quality monitoring. Through high-speed cameras and deep learning algorithms, the system analyzes welding joint quality in real-time, detecting potential defects like undersized or mispositioned welds, enhancing welding consistency and reducing defects.

3. Deep integration of vision and robotic control

Japan's Mujin specializes in universal integrated solutions for industrial robots. Its core technology, the Mujin Controller, connects via Ethernet or interfaces with robot manufacturers' APIs for real-time robotic system control, including collision detection and avoidance.

Here are some Mujin application cases:

JD.com's Asia No. 1 Automated Warehouse: Mujin's technology automates the entire process from warehousing, picking, and packing. It plays a crucial role in solving logistics automation challenges like overhead scanning, dynamic stacking calculations, and automatic mixed code sorting, enhancing warehouse and logistics efficiency and accuracy.

ASKUL (Japan's top three e-commerce platforms) Automated Picking Solution: This system upgrades from goods-to-person to goods-to-robot, making fully automated picking of massive SKUs possible with Mujin's technology, significantly improving picking efficiency and accuracy.

Denmark's Scape focuses on 3D vision systems for industrial robots. Its flagship product, the SCAPE Bin-Picking System, is widely used in smart manufacturing. It integrates with standard robotic arms from world-class robot companies (e.g., KUKA, ABB, Kawasaki) for loading and unloading in smart automated production lines.

Here are some Scape application cases in factories:

BMW Leipzig Factory: Scape's 3D vision system is deployed in BMW Leipzig's smart automated production line, enabling random grasping of workpieces, enhancing automation and efficiency. The system is also widely used by world-class automakers like Mercedes-Benz, Volkswagen, and Honda.

Chinese Market: Scape has partnered with companies like Sirter Robotics, Guangzhou CNC, Dongfeng Honda, Toyota, and Hitachi Compressors, supporting their smart manufacturing upgrades.

4. Challenges and development prospects

Seamlessly integrating vision systems, AI algorithms, and hardware devices from different vendors onto a unified platform remains a challenge. Additionally, compatibility issues between production lines and production data integration need to be addressed.

Vision systems and AI performance depend on data quality and algorithm training. In complex factory environments with varying light conditions, part shapes, and material reflectivity, ensuring vision system accuracy, stability, and avoiding misjudgments is a significant technical challenge.

In many cases, implementing vision systems isn't technically infeasible but may not match human operation efficiency. Real-time production line operations pose challenges to vision system processing speed and stability.

While vision systems and AI intelligence can significantly improve production efficiency and quality, initial equipment investment, system installation, and maintenance costs are high considerations for businesses.

Furthermore, vision and AI systems require regular algorithm updates and hardware calibrations. Balancing benefits with costs is a critical challenge.

With deep learning and AI advancements, vision systems will gain stronger adaptability and recognition accuracy. For instance, they'll identify more complex part shapes and materials and use AI learning algorithms to predict and maintain equipment faults, reducing downtime.

Vision systems promise enhanced environmental perception and decision-making capabilities, further advancing automotive manufacturing intelligence, supporting flexible production, and enabling rapid market demand responses.

Vision systems and their underlying technologies like visual recognition and AI intelligence hold promising applications in automotive factories. As technology progresses and application scenarios expand, these advancements will propel automotive manufacturing towards a smarter, more automated, and efficient future.