高精度旋轉工作台熱誤差冷卻分析及節能研究

近幾年來，因應半導體、生醫、光學、航太等應用發展隨著加工效率及精度的需求不斷地提升，造就高精密五軸工具機成為高附加價值產業需求。本研究將直驅式(Direct Drive, DD)旋轉工作台進行熱誤差多重物理耦合分析及冷卻性能優化改善並透過實際機台進行實驗及計算提升模擬分析邊界條件的準確性，在模擬分析下，得到了各入口流量及入口溫度設定之變形量及結構溫度結果，能有效的找出適合的邊界條件，研究中透過流量差異最高降低了30 %以上的變形量，透過溫度控制最高降低了40 %以上的變形量，使未來在設定各項邊界條件時有更多的依據及數據去做參考。另外也針對目前低碳化的節能議題進行探討，而能耗及碳排放的研究上，在ISO 230-3:2007實驗方法下，兩種冷卻流量控制方式，冷卻機產生的能源消耗及成本還有碳排放量計算，計算後發現進行了流量控制之整年用電量，相比未控制情況下，減少了1525.33度(kWh)的用電量，相當於減少755.036 kgCO2e的碳排放量，有效減少37.89 %的能耗成本及碳排放。不僅提升了機台精度，也在成本及碳排放量上有明顯的成效。

In recent years, the demand for machining efficiency and precision has steadily increased alongside advancements in applications such as semiconductors, biomedical engineering, optics, and aerospace. This trend has driven the development of high-precision five-axis machine tools, which are essential for high-value-added industries. This study focuses on the multi-physics coupling analysis of thermal errors and the optimization of cooling performance for direct-drive (DD) rotary worktables. Experiments and simulations were conducted on actual machines to enhance the accuracy of boundary condition settings in simulation analyses. The simulation results provided insights into deformation and structural temperature distributions under various inlet flow rates and temperatures, facilitating the identification of optimal boundary conditions. Through flow rate adjustments, the deformation was reduced by more than 30%, while temperature control achieved a reduction of over 40 %. These findings offer robust data and a methodological basis for future boundary condition settings in similar applications. Furthermore, this study addresses energy efficiency and carbon reduction, critical topics in the transition towards low-carbon manufacturing. Following the experimental guidelines of ISO 230-3:2007, the energy consumption, operational costs, and carbon emissions associated with two cooling flow control methods were evaluated. The implementation of flow control reduced annual electricity consumption by 1,525.33 kWh compared to uncontrolled scenarios, corresponding to a reduction of 755.036 kgCO2e in carbon emissions. This represents a 37.89 % decrease in both energy costs and carbon emissions.