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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.