The Optimization for the Titanium Alloys Turning Based on the Kriging Interpolation and Genetic Algorithm
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摘要: 钛合金较差的切削加工性不利于保证好的表面完整性,影响钛合金零件的使用性能。基于田口方法建立钛合金车削试验模型,考察切削用量对表面粗糙度、刀具寿命、切削力和材料去除率的影响规律,以材料去除率为目标函数,以表面粗糙度、刀具寿命和切削力为约束函数,基于Krig-ing插值的响应曲面法和遗传算法构建了钛合金车削参数优化模型。研究结果表明:钛合金车削过程参数最优的水平组合为v3f1ap1r1E3,优化结果与初始试验相比,表面粗糙度、刀具寿命、切削力和材料去除率分别改善了75.86%、65.16%、36.41%和557.91%。Abstract: The poor machinability of titanium alloys has limited good surface integrity which affects the processingand servicing properties.The experimental model for the titanium alloy turning based on the Taguchi method wereproposed to express the correlation between the processing variables and the responses.With material removal rateas objective function and surface roughness, tool life, cutting force as constraint function, the optimization procedure of titanium alloy turning was presented based on the response surface method via Kriging interpolation and genetic algorithm.The results indicate that the optimum parameter levels for different variables have been suggestedas v3 f3a p1 r1 E3.Comparing to the initial experiment, surface roughness, tool life, cutting force and material removalrate of the optimal condition are improved with 75.86%, 65.16%, 36.41% and 557.91%, respectively.
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Key words:
- titanium alloy /
- turning experimental model /
- Kriging interpolation /
- genetic algorithms /
- optimization
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[1] Ulutan D, zel T. Machining induced surface integrity in titanium and nickel alloys: a review[J]. International Journal of Machine Tools and Manufacture, 2011, 51 (3): 250~280 [2] Jawahir I S, et al. Surface integrity in material removal processes: recent advances[J]. Annals of the CIRP-manufacturing Technology, 2011, 60(2): 603~626 [3] Rao B, Dandekar C R, Shin Y C. An experimental and numerical study on the face milling of Ti-6Al-4V alloy: tool performance and surface integrity[J]. Journal of Materials Processing Technology, 2011, 211 (2): 294~304 [4] 田荣鑫等. 面向加工表面粗糙度的钛合金高速铣削工艺参数区间敏感性及优选[J]. 航空学报, 2010, 31 (12): 2464~2470 [5] Elmagrabi N, Che Hassan C H, Jaharah A G, et al. High speed milling of Ti-6Al-4V using coated carbide tools[J]. European Journal of Scientific Research, 2008, 22(2): 153~162 [6] Sun J, Guo Y B. A comprehensive experimental study on surface integrity by end milling Ti-6Al-4V[J]. Journal of Materials Processing Technology, 2009, 209(8): 4036~4042 [7] zel T, et al. Investigations on the effects of multi-layered coated inserts in machining Ti-6Al-4V alloy with experiments and finite element simulations[J]. Annals of the CIRP-Manufacturing Technology, 2010, 59(1): 77~82 [8] zel T, Thepsonthi T, Ulutan D, et al. Experiments and finite element simulations on micro-milling of Ti-6Al-4V alloy with uncoated and cBN coated micro-tools[J]. Annals of the CIRPManufacturing Technology, 2011, 60(1): 85~88 [9] Dandekar C R, et al. Machinability improvement of titanium alloy (Ti-6Al-4V) via LAM and hybrid machining[J]. International Journal of Machine Tools and Manufacture, 2010, 50(2): 174~182 [10] 张烘州等. 响应曲面法在表面粗糙度预测模型及参数优化中的应用[J]. 上海交通大学学报, 2010, 44(4): 447~451 [11] Zareena A R, et al. Tool wear mechanisms and tool life enhancement in ultra-precision machining of titanium[J]. Journal of Materials Processing Technology, 2012, 212(3): 560~570 [12] Zain A M, Haron H, Sharif S. Prediction of surface roughness in the end milling machining using artificial neural network[J].Expert Systems with Applications, 2010, 37(2): 1755~1768 [13] Zain A M, Haron H, Sharif S. Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process[J]. Expert Systems with Applications,2010, 37(6): 4650~4659 [14] Zain A M, et al. Regression and ANN models for estimating minimum value of machining performance[J]. Applied Mathematical Modelling, 2012, 36(4): 1477~1492 [15] Tansel I N, Gülmez S, Demetgul M, et al. Taguchi methodGONNS integration: complete procedure covering from experimental design to complex optimization[J]. Expert Systems with Applications, 2011, 38(5): 4780~4789 [16] ISO Standard 3685. Tool-life Testing with Single-point Turning tools[S]. 1993 [17] Gill S S, Singh J. An adaptive neuro-fuzzy inference system modeling for material removal rate in stationary ultrasonic drilling of sillimanite ceramic[J]. Expert Systems with Applications,2010, 37(8): 5590~5598 [18] 赵威等. 氮气油雾介质下 Ti-6Al-4V 钛合金高速铣削试验研究[J]. 南京航空航天大学学报, 2006, 38(5): 634~638 [19] Sun J H, Hsueh B R. Optical design and multi-objective optimization with fuzzy method for miniature zoom optics[J]. Optics and Lasers in Engineering, 2011, 49(7): 962~971 [20] Kaymaz I. Application of kriging method to structural reliability problems[J]. Structural Safety, 2007, 27(2): 133~151 [21] Lebaal N, Nouari M, Ginting A. A new optimization approach based on Kriging interpolation and sequential quadratic programming algorithm for end milling refractory titanium alloys[J]. Applied Soft Computing, 2011, 11 (8): 5110~5119 [22] 谢延敏. 基于 Kriging 模型和灰色关联分析的板料成形工艺稳健优化设计研究[D]. 上海: 上海交通大学, 2007
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