Application of Artificial Intelligence for Poka-Yoke in the Process and Manufacturing of LED Boards in Car Lights: A MATLAB and Python Integration Approach

The automotive sector is progressively adopting error-proofing techniques to uphold stringent production standards, particularly in the fabrication of LED boards utilized in automotive lighting systems. This study explores the application of Artificial Intelligence (AI) to refine the Poka-Yoke (mistake-proofing) methodology in the manufacturing process of LED boards. By integrating MATLAB and Python, we present an advanced framework that leverages AI for defect detection during both the production and testing stages. This innovative system enhances the accuracy of error detection, significantly elevates product quality, and minimizes costs related to manufacturing defects. The research demonstrates that the proposed AI-driven approach can effectively ensure the consistent delivery of high-quality LED boards, thus reinforcing the safety and reliability of automotive lighting systems. Additionally, the incorporation of AI in error-proofing processes offers the potential for scaling production while maintaining high standards, ultimately fostering greater efficiency and reducing the likelihood of defects in automotive lighting components.

Keywords:

Poka-Yoke , Artificial Intelligence , LED boards , Automotive lighting , MATLAB , Python , Manufacturing Process , Error detection.