MODELING AND OPTIMIZATION OF THE CUTTING FLUID FLOW AND PARAMETERS FOR INCREASING TOOL LIFE IN SLOT MILLING ON St52

作     者：AMIR MAHYAR KHORASANI ALEX KOOTSOOKOS 

作者机构：Faculty of High Technology and Engineering Iran University of Industries and Mines 578-Hafez StreetKarimkhan Blv.TehranIran School of AerospaceMechanical and Manufacturing Engineering RMIT UniversityG.P.O.Box 2476VMelbourne 3001Australia 

出 版 物：《International Journal of Modeling, Simulation, and Scientific Computing》 (建模、仿真和科学计算国际期刊（英文）)

年 卷 期：2013年第4卷第2期

页      面：63-73页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Cutting fluid flow tool life optimization slot milling artificial neural networks. 

摘      要：In this paper the CNC machining of St52 was modeled using an artificial neural network(ANN)in the form of a four-layer multi-layer perceptron(MLP).The cutting parameters used in the model were cutting fluid flow,feed rate,spindle speed and the depth of cut and the model output was the tool *** obtaining more accuracy and spending less time Taguchi design of experiment(DOE)has been used and correlation between the output of the ANN and the experimental results was 96%.Further optimization process has been done by use of a genetic algorithm(GA).After optimization process tool life was increased about 8%equal to 33 min and was corroborated by experimental *** demonstrates that the coupling of an ANN with the GA optimization technique is a valid and useful approach to use.