Earticle

초록

영어

Heat dissipation testing for automobile LED lamp design is a crucial step to ensuring optimal lighting performance and extending product lifespan. We propose a machine learning-based real-time interactive system for assessing heat dissipation in LED lamp designs. Unlike traditional methods that require expertise in computational fluid dynamics (CFD), our system allows designers to directly evaluate whether their designs meet thermal requirements without specialized CFD knowledge. We designed an interactive system that enables real-time adjustments of design parameters, such as the number of LED diodes or the size of the heat dissipation plate, providing immediate feedback and optimization. It significantly reduces the production cycle by streamlining the design validation process, thereby enhancing manufacturing efficiency. By enabling rapid iteration and adaptation to market trends, the system improves business competitiveness in the automotive manufacturing and parts production industry. We conducted experimental validation that confirms that our method provides accurate thermal dissipation assessments at a fraction of the computational cost of conventional approaches. These findings highlight the potential of machine learning-driven design tools in accelerating innovation in the automotive manufacturing sector.

목차

Abstract
1. Introduction
2. Related work
3. Machine Learning-based assessment system for LED dissipation
3.1 System architecture
3.2 Prediction model
3.3 Interactive User Interface
4. Conclusion
References

키워드

저자

• Hyebong Choi [ Associate Professor, Handong Global University, Gyeongbuk, South Korea ] Corresponding Author
• Suhui Jung [ Bachelor, Handong Global University, Gyeongbuk, South Korea ]
• Krishna Sharma Sutihar [ aster Student, Handong Global University, Gyeongbuk, South Korea ]
• Kwanphil Cho [ Assistant Professor, Handong Global University, Gyeongbuk, South Korea ]
• Yun Seon Kim [ Associate Professor, Handong Global University, Gyeongbuk, South Korea ]
• Daeyoung Na [ Associate Professor, Handong Global University, Gyeongbuk, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380