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Development of modular machine tool structural monitoring system

Potdar, Akshay, Longstaff, Andrew P., Fletcher, Simon and Abdulshahed, Ali (2013) Development of modular machine tool structural monitoring system. In: Proceedings of the International Conference on Advanced Manufacturing Engineering and Technologies. KTH Royal Institute of Technology, Stockholm, pp. 263-272. ISBN 978-91-7501-892-8

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Although designed to be structurally stiff, machine tool deformation takes place due to the various sources of errors such as shifting mass, component weight, temperature etc. In order to facilitate research activities and acquire further scientific insight on the deformation process, a computer-based on-line monitoring system has been developed. A variety of sensors can be used to capture data for numerous parameters like temperature, displacement, strain etc.
This paper presents the design and implementation of a LabVIEW based multi-sensor data acquisition program. It was designed in a three layer modular structure. In addition to data acquisition, the program is also capable of data processing, logging and implementing various error reduction techniques using online communication between LabVIEW and the MATLAB run-time engine for computation purpose. These calculated compensation values are then transferred to the machine controller via Ethernet. This paper also describes an example of application of such a system for a 5-axis CNC machine tool.

Item Type: Book Chapter
Additional Information: NEWTECH 2013, the International Conference on Advanced Manufacturing Engineering and Technologies, Stockholm, Sweden27-30 October 2013
Uncontrolled Keywords: Machine tool structural monitoring system, Multi-sensor data acquisition system, Modular structure, LabVIEW
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering > Centre for Precision Technologies > Engineering Control and Machine Performance Research Group
School of Computing and Engineering
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References: 1] Ramesh, R., M.A. Mannan, and A.N. Poo, Error compensation in machine tools — a review: Part I: geometric, cutting-force induced and fixture-dependent errors. International Journal of Machine Tools and Manufacture, 2000. 40(9): p. 1235-1256. [2] Ramesh, R., M.A. Mannan, and A.N. Poo, Error compensation in machine tools — a review: Part II: thermal errors. International Journal of Machine Tools and Manufacture, 2000. 40(9): p. 1257-1284. [3] Möhring, H.C., K.M. Litwinski, and O. Gümmer, Process monitoring with sensory machine tool components. CIRP Annals - Manufacturing Technology, 2010. 59(1): p. 383-386. [4] Denkena, B., H.-C. Möhring, and K. Litwinski, Design of dynamic multi sensor systems. Production Engineering, 2008. 2(3): p. 327-331. [5] Luo, R.C., Y. Chih-Chen, and S. Kuo Lan, Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sensors Journal, 2002. 2(2): p. 107-119. [6] Banerjee, T.P. and S. Das, Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences, 2012: p. 96-107. [7] Fekih, A., H. Xu, and F.N. Chowdhury, Neural networks based system identification techniques for model based fault detection of nonlinear systems. International Journal of Innovative Computing, Information and Control, 2007. 3(5): p. 1073-1085. [8] Ghosh, N., et al., Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing, 2007. 21(1): p. 466-479. [9] Mahajan, A., K. Wang, and P.K. Ray, Multisensor integration and fusion model that uses a fuzzy inference system. Mechatronics, IEEE/ASME Transactions on, 2001. 6(2): p. 188-196. [10] Aliustaoglu, C., H.M. Ertunc, and H. Ocak, Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mechanical Systems and Signal Processing, 2009. 23(2): p. 539-546. [11] Panopoulou, A., et al., Dynamic fiber Bragg gratings based health monitoring system of composite aerospace structures. Acta Astronautica, 2011. 69(7–8): p. 445-457. [12] Flandorfer, H., F. Gehringer, and E. Hayer, Individual solutions for control and data acquisition with the PC. Thermochimica Acta, 2002. 382(1–2): p. 77-87. [13] Anjos, J.M.S., G.K. Coracini, and E. Villani, A proposal and verification of a software architecture based on LabVIEW for a multifunctional robotic end-effector. Advances in Engineering Software, 2013. 55: p. 32-44. [14] Whitley, K.N., L.R. Novick, and D. Fisher, Evidence in favor of visual representation for the dataflow paradigm: An experiment testing LabVIEW's comprehensibility. International Journal of Human-Computer Studies, 2006. 64(4): p. 281-303. [15] Wang, L., et al., The Application of LabVIEW in Data Acquisition System of Solar Absorption Refrigerator. Energy Procedia, 2012. 16: p. 1496-1502.
Depositing User: Akshay Potdar
Date Deposited: 02 Jun 2014 11:50
Last Modified: 28 Aug 2021 19:10


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