Modelling Power Consumption in Ball-End Milling Operations
作者： G. Quintana , J. Ciurana , J. Ribatallada
DOI: 10.1080/10426910903536824
关键词: Power consumption 、 End milling 、 Ammeter 、 Machine tool 、 Automotive engineering 、 Mechanical engineering 、 Engineering 、 Artificial neural network 、 Ball (bearing)
摘要: Power consumption is a factor of increasing interest in manufacturing due to its obvious impact on production costs and the environment. The aim this work analyze influence process parameters power high-speed ball-end milling operations carried out AISI H13 steel. A total 300 experiments were 3-axis vertical center, Deckel-Maho 105 V linear. consumed by spindle X, Y, Z machine tool axes was measured using four ammeters located respective cables. data collected used develop an artificial neural network (ANN) which predict during operations. results obtained from ANN are very accurate. predictions can help operators determine most effective cutting for saving energy money while bringing closer goal environmentally sensitive has become topic general importance.
sci-hub.se 参考文章(22)
Jiří Tlustý, Manufacturing processes and equipment Prentice-Hall. ,(1999)
Frank Pettersson, Nirupam Chakraborti, Henrik Saxén, A genetic algorithms based multi-objective neural net applied to noisy blast furnace data soft computing. ,vol. 7, pp. 387- 397 ,(2007) , 10.1016/J.ASOC.2005.09.001
J. Vivancos, C.J. Luis, J.A. Ortiz, H.A. González, Analysis of factors affecting the high-speed side milling of hardened die steels Journal of Materials Processing Technology. ,vol. 162, pp. 696- 701 ,(2005) , 10.1016/J.JMATPROTEC.2005.02.155
Shivakumar Raman, A review of: “Manufacturing Processes and Equipment” J. TLUSTY Prentice Hall, Upper Saddle River, NJ ISBN 0-201-49865-0 Iie Transactions. ,vol. 34, pp. 647- 648 ,(2002) , 10.1080/07408170208928901
R. C. Dimitriu, H. K. D. H. Bhadeshia, C. Fillon, C. Poloni, Strength of Ferritic Steels: Neural Networks and Genetic Programming Materials and Manufacturing Processes. ,vol. 24, pp. 10- 15 ,(2008) , 10.1080/10426910802539796
How multi-objective genetic algorithms handle lack of data, sparse data and excess data: evaluation of some recent case studies in the materials domain Statistical Analysis and Data Mining. ,vol. 1, pp. 322- 328 ,(2009) , 10.1002/SAM.V1:5
J.P. Urbanski, P. Koshy, R.C. Dewes, D.K. Aspinwall, High speed machining of moulds and dies for net shape manufacture Materials & Design. ,vol. 21, pp. 395- 402 ,(2000) , 10.1016/S0261-3069(99)00092-8
H.Y Kim, J.H Ahn, S.H Kim, S Takata, Real-time drill wear estimation based on spindle motor power Journal of Materials Processing Technology. ,vol. 124, pp. 267- 273 ,(2002) , 10.1016/S0924-0136(02)00111-5
J. Ciurana, G. Quintana, M.L. Garcia-Romeu, Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach International Journal of Production Economics. ,vol. 115, pp. 171- 178 ,(2008) , 10.1016/J.IJPE.2008.05.009
M. Albertí, J. Ciurana, C.A. Rodriguez, Experimental analysis of dimensional error vs. cycle time in high-speed milling of aluminium alloy International Journal of Machine Tools & Manufacture. ,vol. 47, pp. 236- 246 ,(2007) , 10.1016/J.IJMACHTOOLS.2006.04.009