Modelling Power Consumption in Ball-End Milling Operations

Power consumption is a factor of increasing interest in manufacturing due to its obvious impact on production costs and the environment. The aim of this work is to analyze the influence of process parameters on power consumption in high-speed ball-end milling operations carried out on AISI H13 steel. A total of 300 experiments were carried out in a 3-axis vertical milling center, the Deckel-Maho 105 V linear. The power consumed by the spindle and by the X, Y, and Z machine tool axes was measured using four ammeters located in the respective power cables. The data collected was used to develop an artificial neural network (ANN) which was used to predict power consumption during operations. The results obtained from the ANN are very accurate. Power consumption predictions can help operators to determine the most effective cutting parameters for saving energy and money while bringing the milling process closer to the goal of environmentally sensitive manufacturing which has become a topic of general importance.

[1]  Aitzol Lamikiz,et al.  Machine Tools for High Performance Machining , 2009 .

[2]  Jiří Tlustý,et al.  Manufacturing processes and equipment , 1999 .

[3]  Nirupam Chakraborti,et al.  Analyzing Leaching Data for Low-Grade Manganese Ore Using Neural Nets and Multiobjective Genetic Algorithms , 2009 .

[4]  Nirupam Chakraborti,et al.  Analyzing Sparse Data for Nitride Spinels Using Data Mining, Neural Networks, and Multiobjective Genetic Algorithms , 2008 .

[5]  Anwar Khalil Sheikh,et al.  Use of electrical power for online monitoring of tool condition , 2005 .

[6]  D. Aspinwall,et al.  A review of ultra high speed milling of hardened steels , 1997 .

[7]  J. Ciurana,et al.  Surface Roughness Generation and Material Removal Rate in Ball End Milling Operations , 2010 .

[8]  Carlo Poloni,et al.  Strength of Ferritic Steels: Neural Networks and Genetic Programming , 2008 .

[9]  A. Iasonna,et al.  Power measurements during mechanical milling. An experimental way to investigate the energy transfer phenomena , 1996 .

[10]  J. A. Ortiz,et al.  Analysis of factors affecting the high-speed side milling of hardened die steels , 2005 .

[11]  Joaquim Ciurana,et al.  Experimental analysis of dimensional error vs. cycle time in high-speed milling of aluminium alloy , 2007 .

[12]  Taylan Altan,et al.  Manufacturing of Dies and Molds , 2001 .

[13]  Shozo Takata,et al.  Real-time drill wear estimation based on spindle motor power , 2002 .

[14]  Lawrence E. Whitman,et al.  Data collection framework on energy consumption in manufacturing , 2006 .

[15]  Kishan G. Mehrotra,et al.  Elements of artificial neural networks , 1996 .

[16]  David K. Aspinwall,et al.  High speed machining of moulds and dies for net shape manufacture , 2000 .

[17]  Joaquim Ciurana,et al.  Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach , 2008 .

[18]  Shivakumar Raman,et al.  A review of: “Manufacturing Processes and Equipment” J. TLUSTY Prentice Hall, Upper Saddle River, NJ ISBN 0-201-49865-0 , 2002 .

[19]  N. Chakraborti.  How multi-objective genetic algorithms handle lack of data, sparse data and excess data: evaluation of some recent case studies in the materials domain , 2009, Stat. Anal. Data Min..

[20]  Frank Pettersson,et al.  A genetic algorithms based multi-objective neural net applied to noisy blast furnace data , 2007, Appl. Soft Comput..

[21]  J. Ciurana,et al.  Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel , 2009 .