Atonm Color Sensor Applied in Vehicle Body Color Recognition

Introduction

With continuous technological progress, the automotive industry keeps upgrading and automation becomes mainstream. This article examines a large automotive manufacturer using the Atonm Color Sensor (CL2) for vehicle body color recognition across its plants. After painting, the color sensor automatically identifies body color and routes the vehicle to the appropriate line to improve production efficiency.

Project Requirements

The main challenge is that painted vehicle bodies reflect light in production environments, making color recognition difficult for general sensors. The project needs a color sensor that performs reliably under such lighting conditions.

Applied Products

Atonm Color Sensor CL2

Solution

In this project, the Atonm Color Sensor CL2 was mounted at the designated sensing point. After painting, the body moves to the sampling point where the sensor captures the color and compares it against pre-teached standard colors. Once matched, the system automatically sorts and transports the vehicle to the corresponding assembly line for the next production step.

The core of this solution is the high sensitivity and accuracy of the color sensor. The CL2 sensor effectively captures painted color information and compares it to standards, ensuring highly accurate color recognition and supporting production automation.

Technical Highlights

(1) Light intensity adaptability

The Atonm Color Sensor CL2 is designed to adapt to varying light intensity by using advanced optics and signal processing, providing stable and accurate color recognition even under low light.

(2) High-precision color recognition

CL2 uses multispectral techniques to recognize a broader color range. After painting, subtle color differences are captured to ensure each vehicle is accurately classified to the correct assembly line.

Customer Benefits

(1) Improved production efficiency: Automated color recognition routes vehicles without manual intervention, shortening waiting times and improving throughput.

(2) Reduced manual intervention: Automated recognition and sorting reduce labor needs and improve operation stability and consistency.

(3) Lower error rate: High-precision color recognition reduces human errors, ensuring accurate allocation and improving overall product quality.

Conclusion

In modern automotive production, technology is key to improving efficiency and reducing costs. The successful application of the Atonm Color Sensor CL2 in vehicle body color recognition supports automated production by overcoming lighting and reflection challenges, improving efficiency, reducing manual work, and lowering error rates.