Study on surface roughness in high-speed milling of AlMn1Cu using factorial design and partial least square regression
详细信息 查看全文
- 作者：Z. H. Wang (1)
J. T. Yuan (1)
T. T. Liu (1)
J. Huang (1)
L. Qiao (1)
1. School of Mechanical Engineering ; Nanjing University of Science and Technology ; 200 Xiao Ling Wei Street ; Nanjing ; 210094 ; Jiangsu ; People鈥檚 Republic of China - 关键词：Surface roughness ; Cutting parameters ; AlMn1Cu ; Factorial design ; Partial least square regression
- 刊名：The International Journal of Advanced Manufacturing Technology
- 出版年：2015
- 出版时间：February 2015
- 年：2015
- 卷：76
- 期：9-12
- 页码：1783-1792
- 全文大小：878 KB
- 参考文献：1. Benardos PG, Vosniakos G-C (2003) Prediction surface roughness in machining: a review. Int J Mach Tool Manuf 43:833鈥?44 lass="external" href="http://dx.doi.org/10.1016/S0890-6955(03)00059-2" target="_blank" title="It opens in new window">CrossRef
2. Geoffrey B, Winston AK (1989) Fundamentals of machining and machine tools. Marcel dekker, INC, New York, US
3. Fang XD, Sfai-Jahanshaki H (1997) A new algorithm for developing a reference model for prediction surface roughness in finish machining of steels. Int J Prod Res 35:179鈥?97 lass="external" href="http://dx.doi.org/10.1080/002075497196046" target="_blank" title="It opens in new window">CrossRef
4. Hasan 脰, Tuncay E, Mustafa 脟 (2006) A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces. Int J Adv Manuf Technol 28:694鈥?00 lass="external" href="http://dx.doi.org/10.1007/s00170-004-2435-6" target="_blank" title="It opens in new window">CrossRef
5. Chen JC, Lee SS (2003) An on-line surface roughness recognition system using an accelerometer in turning operations. J Eng Technol 20:12鈥?8
6. El-Axir MH, Othman OM, Abodiena AM (2008) Study on the inner surface finishing of aluminum alloy 2014 by ball burnishing process. J Mater Process Technol 202:435鈥?42 lass="external" href="http://dx.doi.org/10.1016/j.jmatprotec.2007.10.040" target="_blank" title="It opens in new window">CrossRef
7. Erol K, Mesut H, Ahmet Y (2011) Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning. Int J Adv Manuf Technol 52:79鈥?8 lass="external" href="http://dx.doi.org/10.1007/s00170-010-2710-7" target="_blank" title="It opens in new window">CrossRef
8. Wang SJ, To S, Cheung CF (2013) An investigation into material-induced surface roughness in ultra-precision milling. Int J Adv Manuf Technol 68:607鈥?16 lass="external" href="http://dx.doi.org/10.1007/s00170-013-4781-8" target="_blank" title="It opens in new window">CrossRef
9. Cakir CM, Ensrioglu C, Demirayak I (2009) Mathematical modeling of surface roughness for evaluating the effects of cutting parameters and coating material. J Mater Process Technol 209:102鈥?09 lass="external" href="http://dx.doi.org/10.1016/j.jmatprotec.2008.01.050" target="_blank" title="It opens in new window">CrossRef
10. Chinnasamy NS, Muthu PK (2011) Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 57:1043鈥?051 lass="external" href="http://dx.doi.org/10.1007/s00170-011-3343-1" target="_blank" title="It opens in new window">CrossRef
11. Karayel D (2009) Prediction and control of surface roughness in CNC lathe using artificial neural network. J Mater Process Technol 209:3125鈥?137 lass="external" href="http://dx.doi.org/10.1016/j.jmatprotec.2008.07.023" target="_blank" title="It opens in new window">CrossRef
12. Huang BP, Chen JC, Li Y (2008) Artificial-neural-networks-based surface roughness Pokayoke system for end-milling operations. Neurocomputing 71:544鈥?49 lass="external" href="http://dx.doi.org/10.1016/j.neucom.2007.07.029" target="_blank" title="It opens in new window">CrossRef
13. 脰ktem H (2009) An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation. Int J Adv Manuf Technol 43:852鈥?61 lass="external" href="http://dx.doi.org/10.1007/s00170-008-1763-3" target="_blank" title="It opens in new window">CrossRef
14. Lee BY, Liu HS, Tarng YS (1998) Modeling and optimization of drilling process. J Mater Process Technol 74:149鈥?57 lass="external" href="http://dx.doi.org/10.1016/S0924-0136(97)00263-X" target="_blank" title="It opens in new window">CrossRef
15. 脟olak O, Kurbanoglu C, Kayacan MC (2007) Milling surface roughness prediction using evolutionary programming methods. Mater Des 28:657鈥?66 lass="external" href="http://dx.doi.org/10.1016/j.matdes.2005.07.004" target="_blank" title="It opens in new window">CrossRef
16. Ding TC, Zhang S, Wang YW, Zhu XL (2010) Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel. Int J Adv Manuf Technol 51:45鈥?5 lass="external" href="http://dx.doi.org/10.1007/s00170-010-2598-2" target="_blank" title="It opens in new window">CrossRef
17. Basak H, Goktas Haldun H (2009) Burnishing process on Al-alloy and optimization of surface roughness and surface hardness by fuzzy logic. Mater Des 30:1275鈥?281 lass="external" href="http://dx.doi.org/10.1016/j.matdes.2008.06.063" target="_blank" title="It opens in new window">CrossRef
18. Oktem H, Erzurumlu T, Erzincanli F (2006) Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mater Des 27:735鈥?44 lass="external" href="http://dx.doi.org/10.1016/j.matdes.2005.01.010" target="_blank" title="It opens in new window">CrossRef
19. Bozdemir M, Aykut 艦 (2012) Optimization of surface roughness in end milling Castamide. Int J Adv Manuf Technol 62:495鈥?03 lass="external" href="http://dx.doi.org/10.1007/s00170-011-3840-2" target="_blank" title="It opens in new window">CrossRef
20. Palani S, Natarajan U (2011) Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform. Int J Adv Manuf Technol 54:1033鈥?042 lass="external" href="http://dx.doi.org/10.1007/s00170-010-3018-3" target="_blank" title="It opens in new window">CrossRef
21. Dhokia VG, Kumar S, Vichare P, Newman ST (2008) An intelligent approach for the prediction of surface roughness in ball-end machining of polypropylene. Robotics Comput-Integ Manuf 24:835鈥?42 lass="external" href="http://dx.doi.org/10.1016/j.rcim.2008.03.019" target="_blank" title="It opens in new window">CrossRef
22. Lu C (2008) Study on prediction of surface quality in machining process. J Mater Process Technol 205:439鈥?50 lass="external" href="http://dx.doi.org/10.1016/j.jmatprotec.2007.11.270" target="_blank" title="It opens in new window">CrossRef
23. Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi鈥檚 design of experiments. Robot Comput Integ Manuf 18:343鈥?54 lass="external" href="http://dx.doi.org/10.1016/S0736-5845(02)00005-4" target="_blank" title="It opens in new window">CrossRef
24. 脟ayda艧 U, Has莽alik A (2008) A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 202:574鈥?82 lass="external" href="http://dx.doi.org/10.1016/j.jmatprotec.2007.10.024" target="_blank" title="It opens in new window">CrossRef
25. EI-Sonbaty IA, Khashaba UA, Selmy AI, Ali AI (2008) Prediction of surface roughness profiles for milled surface using an artificial neural network and fractal geometry approach. J Mater Process Technol 200:271鈥?78 lass="external" href="http://dx.doi.org/10.1016/j.jmatprotec.2007.09.006" target="_blank" title="It opens in new window">CrossRef - 刊物类别：Engineering
- 刊物主题：Industrial and Production Engineering
Production and Logistics
Mechanical Engineering
Computer-Aided Engineering and Design - 出版者：Springer London
- ISSN：1433-3015
文摘
Anti-rust aluminum is widely used in aviation, aerospace, communications, as well as weapons with non-corrosion, light, and other fine characteristics. In this study, in order to improve the machined surface quality and find the functional relation between cutting parameters and surface roughness, a series of cutting experiments for AlMn1Cu were conducted, and the surface roughness values in high-speed milling were obtained. Firstly, according to the analysis of variance (ANOVA) of factorial experiments, the cutting parameters significantly influencing the surface roughness were presented. Secondly, the mathematical prediction models of surface roughness based on the cutting parameters were established by using the partial least squares regression. Finally, experiments are further designed and carried out to validate the accuracy of the proposed prediction model.