Contributions Regarding the Possibility of Increasing the Milling Process Efficiency

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The main purpose of the paper is to develop a neural network application that could predict the tool-workpiece vibration. Increase efficiency by decreasing vibrations has been imposed by the cutting progresses theory and the fields related directly to the cutting process. Thus, this procedure aims to an increasing efficiency, lowering costs and execution time and also improving the quality of parts.

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219-224

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October 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Lin, Shih-Chieh. The use of variable spindle speed for vibration control in face milling process AAI9010937, (UMI)AAI9010937, 2011-05-07.

Google Scholar

[2] Altintas, Y. Manufacturing Automation. United States of America: 1. Ed. Cambridge University Press, 288 pg, (2000).

Google Scholar

[3] Information on http: /www2. unitbv. ro/LinkClick. aspx?fileticket=s7zzua6eTbk%3D&tabid=4579.

Google Scholar

[4] C. E. O. da Silva, E. Villani, J. de Oliveira Gomes, Direct access of CNC data for vibration control, ABCM Symposium Series in Mechatronics - Vol. 4 - pp.553-558 (2010).

Google Scholar

[5] Julie Z.Z. and Joseph C.C. Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system. International Journal of Advanced Manufacturing Technology; 39: 118-128 (2008).

DOI: 10.1007/s00170-007-1186-6

Google Scholar

[6] Jemielniak K. Commercial tool condition monitoring systems. International Journal of Advanced Manufacturing Technology, 15 (4): 711–721 (1999).

DOI: 10.1007/s001700050123

Google Scholar

[7] Dimla D.E. Sensors signals for tool-wear monitoring in metal cutting operations - A Review of methods. International Journal of Machine tools and Manufacture; 40: 1073–1098, (2000).

DOI: 10.1016/s0890-6955(99)00122-4

Google Scholar

[8] Xiaoli Li. A brief review: acoustic emission method for tool wears monitoring during turning. International Journal of Machine tools and Manufacture; 42: 157–165 (2002).

DOI: 10.1016/s0890-6955(01)00108-0

Google Scholar

[9] Information on: http: /www. mecanica. scire. coppe. ufrj. br/util/b2evolution/media/blogs/annacarla/A_review_machining_monitoring. pdf.

Google Scholar

[10] Chen JC., Chen WL., A tool breakage detection system using an accelerometer sensor. J Intell Manuf 10: 187–197 (1999).

Google Scholar

[11] Bahr B., Motavalli S., Arfi T., Sensor fusion for monitoring machine tool conditions. Int J Comput Integr Manuf 10: 314–323 (1997).

Google Scholar

[12] Ertekin YM., Kwon Y., Tseng TL., Identification of common sensory features for the control of CNC milling operations under varying cutting conditions. Int J Mach Tools Manuf 43: 897–904, (2003).

DOI: 10.1016/s0890-6955(03)00087-7

Google Scholar

[13] http: /www. sv-jme. eu/data/upload/2013/06/01_2012_856_Ostasevicius_03. pdf.

Google Scholar

[14] Simon Haykin, Neural networks and learning machines, Volume 10, Prentice Hall, (2009).

Google Scholar

[15] Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth, Neural Network Toolbox™ User's Guide.

Google Scholar

[16] Campean E., Morar L., Baru P., Bordea C., Pop D., Aspects regarding some simulation models for logistic management, Procedia Economics and Finance 00 (2012) 000–000, EFQM, Targu Mures, Romania.

DOI: 10.1016/s2212-5671(12)00270-5

Google Scholar