Journal of Guangdong University of Technology ›› 2020, Vol. 37 ›› Issue (05): 87-93.doi: 10.12052/gdutxb.200048
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Tang Chao-lan, Xie Yi
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[1] 于信伟, 冯明军, 王学惠. 高速铣削参数对铝合金零件表面粗糙度的影响[J]. 黑龙江科技学院学报, 2010, 20(2): 91-93 YU X W, FENG M J, WANG X H. Influence of high-speed milling parameters on aluminum alloy work-piece surface roughness [J]. Journal of Heilongjiang Institute of Science and Technology, 2010, 20(2): 91-93 [2] 王云霞, 漆小敏. 汽车6061铝合金材料切削加工理论及实验研究[J]. 机械强度, 2019, 41(6): 1345-1350 WANG Y X, QI X M. Theory and experimental research on cutting processing of automobile 6061 aluminum alloy materials [J]. Journal of Mechanical Strength, 2019, 41(6): 1345-1350 [3] 周志恒, 张超勇, 谢阳, 等. 数控车床切削参数的能量效率优化[J]. 计算机集成制造系统, 2015, 21(9): 2410-2418 ZHOU Z H, ZHANG C Y, XIE Y, et al. Cutting parameters optimization for processing energy and efficiency in CNC lathe [J]. Computer Integrated Manufacturing Systems, 2015, 21(9): 2410-2418 [4] 黄晓明, 孙杰. 高速铣削7050-T7451铝合金表面粗糙度研究[J]. 中国工程机械学报, 2014, 12(3): 248-251 HUANG X M, SUN J. Research on surface roughness of 7050-T7451 aluminum alloy by high speed milling [J]. Chinese Journal of Construction Machinery, 2014, 12(3): 248-251 [5] 丁涛. 6061铝合金铣削加工表面粗糙度研究[J]. 农业装备与车辆工程, 2018, 56(12): 60-62 DING T. Research on surface roughness of 6061 aluminum alloy by milling [J]. Agricultural Equipment & Vehicle Engineering, 2018, 56(12): 60-62 [6] 伍文进, 徐中云, 严帅, 等. 基于正交试验的6061铝合金铣削工艺研究[J]. 机床与液压, 2018, 46(14): 27-30 WU W J, XU Z Y, YAN S, et al. Study of milling process for 6061 aluminum alloy based on orthogonal experiment [J]. Machine Tool & Hydraulics, 2018, 46(14): 27-30 [7] AJITH ARUL DANIEL S, PUGAZHENTHI R, KUMAR R. Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis [J]. Defence Technology, 2019, 15(4): 545-556 [8] 姚倡锋, 张定华, 黄新春, 等. TC11钛合金高速铣削的表面粗糙度与表面形貌研究[J]. 机械科学与技术, 2011, 30(9): 1573-1578 YAO C F, ZHANG D H, HUANG X C, et al. Exploring surface roughness and surface morphology of high-speed milling TC11 titanium alloy [J]. Mechanical Science and Technology for Aerospace Engineering, 2011, 30(9): 1573-1578 [9] LI J, YANG X, REN C. Multi-objective optimization of cutting parameters in Ti-6Al-4V milling process using nondominated sorting genetic algorithm-II [J]. International Journal of Advanced Manufacturing Technology, 2015, 76(5-8): 941-953 [10] 王立新, 张程焱, 俎晓莉, 等. 切削参数对高强铝合金干切削加工表面形貌的影响[J]. 工具技术, 2019, 53(11): 29-33 WANG L X, ZHANG C Y, ZU X L, et al. Effects of cutting parameters on machined surface morphology of high strength aluminium alloy under dry cutting [J]. Tool Engineering, 2019, 53(11): 29-33 [11] 谢黎明, 张威, 靳岚. 6061铝合金高速铣削切削参数对表面粗糙度的影响分析[J]. 机械设计与制造工程, 2018, 47(3): 56-60 XIE L M, ZHANG W, JIN L. The analysis on the influence of cutting parameters to surface roughness in the high speed milling 6061 aluminum alloy [J]. Machine Design and Manufacturing Engineering, 2018, 47(3): 56-60 [12] 邓朝晖, 符亚辉, 万林林, 等. 面向绿色高效制造的铣削工艺参数多目标优化[J]. 中国机械工程, 2017, 28(19): 2365-2372 DENG C H, FU Y H, WANG L L, et al. Multi objective optimization of milling process parameters for green high-performance manufacturing [J]. China Mechanical Engineering, 2017, 28(19): 2365-2372 [13] 梁爽, 唐晓, 江磊, 等. GA-BP神经网络预测钛合金表面粗糙度[J]. 机械设计与制造, 2019(8): 265-268 LIANG S, TANG X, JIANG L, et al. GA-BP neural network optimized by genetic algorithm predict the surface roughness of titanium alloy [J]. Machinery Design & Manufacture, 2019(8): 265-268 [14] BANDAPALLI C, SUTARIA B M, BHATT D V. Experimental investigation and estimation of surface roughness using ANN, GMDH & MRA models in high speed micro end milling of titanium alloy (Grade-5) [J]. Materials Today Proceedings, 2017, 4(2): 1019-1028 [15] 李帆, 闫献国, 陈峙, 等. 基于遗传算法优化BP神经网络的YG8硬质合金耐磨性预测模型[J]. 金属热处理, 2019, 44(12): 244-248 LI F, YAN X G, CHEN Z, et al. Prediction model of wear resistance of YG8 cemented carbide based on BP neural network optimized by genetic algorithm [J]. Heat Treatment of Metals, 2019, 44(12): 244-248 [16] MIA M, DHAR N R. Prediction of surface roughness in hard turning under high pressure coolant using artificial neural network [J]. Measurement, 2016, 92: 464-474 [17] 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 [18] 郗伟杰, 李东辉. 基于遗传算法优化BP神经网络的接触网磨耗预测[J]. 电气化铁道, 2019, 30(S1): 47-49 XI W J, LI D H. BP neural network based genetic algorithm optimization for prediction of OCS wear [J]. Electric Railway, 2019, 30(S1): 47-49 [19] 段少军. 基于遗传算法的LVDT性能参数多目标优化[D]. 武汉: 武汉科技大学, 2016. |
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