- 學位論文
SUS316不銹鋼的微鑽孔毛邊最小化之研究
Study of Burr Size Minimization in Micro Drilling of SUS316 Stainless Steel
摘要
毛邊對鑽孔加工後工件的表面品質影響扮演一個重要的角色，所以在生產階段，使毛邊最小化是必要的工作。本研究主要的目的是探討使用HSS微鑽頭鑽削SUS316不銹鋼工件產生最小毛邊的最佳化加工參數與鑽頭幾何。本研究首先運用田口實驗計劃法的異變數分析，探討毛邊尺寸最佳化問題，再利用AIM誘導式類神經網路技術，將田口實驗計劃法規劃的實驗結果，建立毛邊尺寸的數學模式。利用遺傳基因演算法將所建立的多項式，找出已知鑽頭直徑產生最小毛邊高度的最佳製程參數與鑽頭幾何角度。遺傳基因演算法是一個以為仿效自然界中物競天擇，適者生存自然演化法則所發展出來的一種最佳化搜尋方法。田口式ANOVA變異數分析的最佳化結果為主軸轉速為3000 rpm、進給率為2 mm/min、靜點角為140 ，第一間隙角為15 ，遺傳演算法最佳化分析的結果為主軸轉速為3360 rpm、進給率為2.1 mm/min、靜點角為120 、第一間隙角為 。最後確認實驗結果，利用田口式ANOVA變異數分析的最佳化參數加工後毛邊高度的平均值為0.0476 mm，用遺傳演算法GA做最佳化參數組加工後毛邊高度的平均值為0.0328mm, 標準用加工參數的毛邊高度平均值為0.099mm，經遺傳演算法GA最佳化後比標準加工參數毛邊高度降低67%。
並列摘要
Burr in drilling plays an important role on surface quality and hence it is essential to minimize the burr size at the production stage. In this research the main goal is to determine the optimum machining parameters and drill geometry to minimize the burr size in drilling of SUS316 stainless steel using HSS micro drill. The approach of Taguchi design based on ANOVA analysis is first utilized to investigate the burr size optimization problem. Experimental results planned as DOE were used to develop the mathematical model for burr size using an adductive network AIM. The developed AIM polynomial equations were then employed with GA, which is a search algorithm based on natural selection and natural genetics, to determine the optimal process parameters and drill geometry for a given drill diameter that results in minimum burr height. The data resulted from optimal analysis are spindle speed 3000 rpm, feed rate 2 mm/min, point angle 140°, first clearance angle 15° for Taguchi optimization method and spindle speed 3360 rpm, feed rate 2.1 mm/min, point angle 15°, first clearance angle 120° for GA optimization approach. The experiment results reveal that the average value of burr size is 0.0476 mm for Taguchi optimization method, 0.0328 mm for GA optimization approach and 0.099 mm for standard use, the maximal improvement rate is 67%.