期刊文献+

铝合金机械设备加工制造新工艺 被引量:4

Aluminum alloy mechanical equipment processing and manufacturing of new technology
下载PDF
导出
摘要 铝合金与传统的工业材料相比,具有高强度、耐腐蚀等优点,已经广泛应用于现代的生产生活中。近些年,铝合金加工的规模越来越大,由于传统的铝合金机械设备加工制造工艺需要采用电炎花进行加工,其流程复杂,延展性差,加工出来的铝合金韧性较低。为此,提出一种新型的铝合金机械设备加工制造工艺,通过单脉冲放电建立铝合金电火花模型,结合遗传算法对参数进行优化,得到理想的加工效果。仿真实验表明,本文提出的加工参数,加工出的铝合金机械设备表面工层好,硬度高,比传统方法工时降低约三分之一,具有广泛的推广性。 Aluminum alloy compared with the traditional industrial materials, which has the advantages of high strength, corrosion resistance, has been widely applied to the production of modern life.In recent years, more and more the size of the aluminum alloy processing, because the traditional aluminum alloy mechanical equipment processing and manufacturing process needs by electric cost for processing, the process is complex, low ductility, processing of aluminum alloy toughness is low.For this, put forward a new type of aluminum alloy mechanical equipment processing and manufacturing process, aluminum alloy electric spark model is established by single pulse discharge, combined with genetic algorithm to optimize the parameters, and get the ideal processing effect.Simulation experiments show that the proposed processing parameters, processing of aluminum alloy mechanical equipment surface layer is good, high hardness, hours lower than traditional methods about one-third, has extensive USES.
作者 宁涛
出处 《世界有色金属》 2016年第9S期25-26,共2页 World Nonferrous Metals
关键词 铝合金 机械设备 加工制造 工艺 Aluminum alloy Mechanical equipment Processing and manufacturing process
  • 相关文献

参考文献7

二级参考文献21

  • 1林胜.铝合金高速切削技术[J].航空制造技术,2004,47(6):61-66. 被引量:12
  • 2Wang X,Feng Jack C X.Development of Empirical Models for Surface Roughness Prediction in Fish Turning.Int.J.Adv.Manuf.Tech.,2002,20(5):348-356.
  • 3Alauddin M,El Baradie M A,Hashmi M S J.Optimization of Surface Finish in End Milling Inconel 718.Journal of Materials Processing Technology,1996,56(1-4):54-65.
  • 4Benardos P G,Vosniakos G C.Prediction of Surface Roughness in CNC Face Milling Using Neural Networks and Taguchi's Design of Experiments.Robotics and Computer-Integrated Manufacturing,2002,18(5-6):343-354.
  • 5Roger Jang J S.ANFIS:Adaptive-network-based Fuzzy Inference System.IEEE Transactionson Systems,Man and Cybernetics,1993,23(3):665-685.
  • 6Roger Jang J S,Sun C T.Neuro-fuzzy Modeling and Control.Proceedings of the IEEE,1995,83(3):378-406.
  • 7Tsai Y H,Chen J C,Lou S J.An Inprocess Surface Recognition System Based on Neural Networks in End Milling Cutting Operations.Int.J.Mach.Tools Manufact.,1999,39(4):583-605.
  • 8Tlusty J,Macneil P.Dynamics of cutting forces in end milling[J].Annals of CIRP,1975,24:21-25.
  • 9Peters J,Vanherck P,Brussel H V.The measurement of the dynamic cutting force coefficients[J].Annals of CIR-P,1971,21(2):129-136.
  • 10Ozel T,Altan T.Process simulation using finite element method-prediction of cutting forces,tool stresses and temperatures in high speed flat end milling[J].International Journal of Machine Tools & Manufacture,2000,40:713-738.

共引文献65

同被引文献19

引证文献4

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部