利用PSO演算法探討高速銑削最佳化

Optimization of High Speed Milling Using Particle Swarm Optimization Algorithm

鄧永亟(Yung-Chi Teng)

指導教授 ： 戴兢志

大同大學/工程學院/機械工程學系所/碩士(2004年)

摘要

本研究主要目的是探討使用球型端銑刀高速加工自由曲面時，影響表面粗度變異的切削參數。且利用誘導式類神經網路系統建立球型端銑刀之表面粗度模式，並依據此模式探討在不同加工條件下，各切削參數對表面粗度變異的影響。實驗驗證結果顯示，本數學模式具有良好的適用性。
本文採用實驗規劃法規劃實驗。選擇主軸轉速、進給率、軸向切深、徑向切深及工件傾角為實驗因子，並利用實驗規劃法中之表面反應設計為基礎配置實驗。實驗數據由誘導式類神經網路系統進行回歸分析，建立表面粗度之數學模式。並利用最佳化演算法PSO，在本研究探討之切削範圍內，進行切削條件的配置，以提供加工者選擇切削條件之參考。

關鍵字

誘導式類神經 ； 表面粗度 ； 微粒密集最佳化

並列摘要

In this investigation the main goal is discussing the cutting parameters affecting the surface roughness variable when using the ball-end mill to machining the free curves at a high speed. The Abductive Networks is used to establish the model of the roughness and based on this model to discuss the effects of every cutting parameter on the surface roughness variable under the different machining conditions. The experimental results show that this mathematical model is very suitable for the investigation.

The experimental scheme is selected to plan the experiment in this paper. The spindle speed, feed rate, axial depth of cut, radial depth of cut, and rake angle of the workpiece are chosen as the experimental factors. Additionally, the response curve design in the experimental scheme is used to be the basic disposed experiment. The Abductive Networks is used to regress and analysis the experimental data and then the mathematical model of the roughness will be established. In the cutting range discussed in this investigation, the optimal algorithm PSO is used to dispose the cutting conditions for providing the operators a reference to select the cutting parameters.

並列關鍵字

Abductive networks ； Particle Swarm Optimization ； Surface roughness

參考文獻

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