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    로봇 액추에이터용 하우징의 다이캐스팅 공정 제약을 고려한 히트싱크 형상 설계 방안 = Heatsink Shape Design Method Considering Die-Casting Process Constraints for Robot Actuator Housings

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    https://www.riss.kr/link?id=A110379227

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    This study proposes an optimal heat sink fin design for robot actuator housings under high-pressure die-casting (HPDC) constraints. Three fin geometries — straight, wave, and pin types — were evaluated using steady-state thermal analysis based on Newton's Law of Cooling. Wave-type fins achieved greater heat dissipation area than straight-type fins at equal thickness, enabling approximately 0.4 mm thinner fins and 9.4 g weight reduction under equal-area conditions. To further refine the wave geometry under manufacturability constraints, a Box-Behnken design with 26 cases was conducted across two fin-count groups (n=5, n=6). Multiple regression analysis identified fin thickness, fin count, and wavelength as factors with statistically meaningful effects on filling temperature, while fin count was the dominant factor governing thermal performance (=−2.83 °C, p<0.001). The results indicate that geometry-only optimization is insufficient to secure mass-production margin within current process conditions, and a combined adjustment of process parameters (melt superheat, gate area, injection velocity) is required.
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    This study proposes an optimal heat sink fin design for robot actuator housings under high-pressure die-casting (HPDC) constraints. Three fin geometries — straight, wave, and pin types — were evaluated using steady-state thermal analysis based on ...

    This study proposes an optimal heat sink fin design for robot actuator housings under high-pressure die-casting (HPDC) constraints. Three fin geometries — straight, wave, and pin types — were evaluated using steady-state thermal analysis based on Newton's Law of Cooling. Wave-type fins achieved greater heat dissipation area than straight-type fins at equal thickness, enabling approximately 0.4 mm thinner fins and 9.4 g weight reduction under equal-area conditions. To further refine the wave geometry under manufacturability constraints, a Box-Behnken design with 26 cases was conducted across two fin-count groups (n=5, n=6). Multiple regression analysis identified fin thickness, fin count, and wavelength as factors with statistically meaningful effects on filling temperature, while fin count was the dominant factor governing thermal performance (=−2.83 °C, p<0.001). The results indicate that geometry-only optimization is insufficient to secure mass-production margin within current process conditions, and a combined adjustment of process parameters (melt superheat, gate area, injection velocity) is required.

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