如今五軸加工系統和CNC工具機已經為高精度加工製造帶來了很大的便利。然而，為了在實際中避免刀具的損壞和斷裂，人們在設計加工策略比較保守，使得加工參數往往較低。這一行為則導致了加工時間的大量增加，影響了生產的效率。所以如何提高生產效率為目前五軸加工的重要議題，尤其是對於加工有複雜曲面的工件而言。
隨著虛擬模擬技術的發展，在加工之前直接優化加工參數被認為是提高效率行之有效的手段。基於這一點，本文提出一種新的銑削製程優化方法以統籌銑削約束條件並調整加工參數，從而最大化五軸銑削的加工效率。在眾多約束條件中，切削力約束最為重要。所以，本文首先從切削力模型中分析出必要的切削力成分，經由模擬採集大量銑削數據來訓練類神經網路以達到預測銑削力的目的。然後，本文將包括運動狀況和切削力在內的所有約束條件建立為銑削約束條件模型，以計算出每段切削區間的最優化主軸轉速和進給率。在得到每一加工區間的最佳結果後，又設計出一整套製程優化算法來評估和整合製程中優化參數。至此，所有的優化過程都在虛擬環境中得以實施。而最終優化後的加工數據可以直接用來修改刀具位置檔（CL）。此外，本文通過幾個加工案例來驗證本文方法的優化性能。驗證結果證實了該方法的有效性和可靠性。

The evolution of the five-axis machining systems and Computer Numerical Control (CNC) machine tools has provided considerable advantages for high-precision manufacturing. However, due to a conservative machining strategy,parameter value shave usually been preset as constants to avoid tool damage or breakage. Unfortunately, this practice leads to a great expense of machining time. So improving production efficiency is an important issue for machining applications such as five-axis milling, especially when machining complex surface parts.

With the development of virtual simulation technology, optimizing machining parameters before machining is now recognized as a feasible method to improve efficiency. Based on this consideration, this thesis proposes a novel milling process optimization method to regulate milling constraints and adjust parameters so as to maximize the five-axis milling efficiency. As cutting force is the primary constraint, the cutting-force model is first analyzed to identify the necessary force components. The employed artificial neural network (ANN) is trained with collected milling data to predict milling force. Then,a model with all constraints, including drive conditions and force,is established to compute the optimal spindle speed and feed rate in each cutting engagement interval. With the optimized results of each milling interval, a series of process optimization algorithms are proposed to evaluate and integrate the optimal parameters in the process. All these processes are carried out in a virtual machining environment. Finally, the new milling data could be used to directly modify the cutter location (CL)file. In Addition, several case examples have been provided to verify the optimization performance of this method, which was found to be effective and reliable.

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