銑削製程中，除工具機空間定位誤差外，因切削力所產生刀具偏移之製程誤差亦是影響工件尺寸誤差之重要因素。由於精密微機械系統功能需求日益嚴苛，對微型加工機與加工件之精度要求均需同時提升，為能更精確預測加工件尺寸或藉由加工件尺寸誤差評估微型工具機之空間誤差，同時考慮並掌握工具機空間誤差與製程誤差對尺寸誤差之影響實為一重要之課題。
本論文旨在探討銑削動態力與工具機空間誤差對加工件尺寸誤差之影響。研究利用銑削力捲積模式，引入表面生成窗之概念，獲得加工面產生時之動態銑削力與刀具偏擺，並藉由刀具與工件綜合剛性計算出製程誤差後，再結合工具機空間誤差，完成加工件尺寸誤差之預測模式。在兩不同構型工具機上實驗結果發現此模式除了可正確預測尺寸誤差外，更提供一能準確評估工具機空間誤差之方法。此外，本研究更進一步分析探討銑削動態力變異、刀具偏擺變異、工具機空間誤差變異與加工件尺寸誤差變異之關係，實驗結果顯示銑削力變異為產生加工件尺寸誤差變異之主因。最後，利用本論文結合尺寸誤差及變異分析之結果成功預測銑削加工面之平行度。

Except the spatial errors of machine tool, the force-induced tool displacement is also an important factor related to the workpiece dimensional error in milling process. As the demands for the functions of micro-machine system are increasing, the accuracy of micro-machine tool and workpiece must be upgrade to a higher level simultaneously. Furthermore, in order to accurately predict the workpiece dimensional errors and measured the machine tool spatial error by means of the workpiece dimensional errors, considering and handling the spatial errors of machine tool and process errors in the same time are important and necessary task.
The workpiece dimension error model comprising of the spatial errors of machine tool and process errors in milling process is presented in this thesis. The spatial errors of machine tool are analyzed by vector method with Laser Doppler Displacement Measurement (LDDM). The important factors of process errors considered in this model include the dynamic milling force, tool stiffness, workpiece stiffness and tool runout. The surface generation window is proposed on the basis of chip width density function of convolution modeling method to relate the generation of workpiece surface to the dynamic milling force. This model not only predicts workpiece dimensional accuracy, but also serves as a new tool to calculate spatial errors of machine tool. Moreover, the effects of all the parametric variance of this model are investigated. It is shown that the dimensional error is most affected by the variance of the dynamic milling force. Finally, the parallelism of the machined surface is shown to be accurately predicted by combining the workpiece dimensional error model and variance analysis.

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