Usage of I++ Simulator to Program Coordinate Measuring Machines when Common Programming Methods are difficult to apply

Open access

Abstract

Nowadays, simulators facilitate tasks performed daily by the engineers of different branches, including coordinate metrologists. Sometimes it is difficult or almost impossible to program a Coordinate Measuring Machine (CMM) using standard methods. This happens, for example, during measurements of nano elements or in cases when measurements are performed on high-precision (accurate) measuring machines which work in strictly air-conditioned spaces and the presence of the operator in such room during the programming of CMM could cause an increase in temperature, which in turn could make it necessary to wait some time until conditions stabilize. This article describes functioning of a simulator and its usage during Coordinate Measuring Machine programming in the latter situation. Article also describes a general process of programming CMMs which ensures the correct machine performance after starting the program on a real machine. As an example proving the presented considerations, measurement of exemplary workpiece, which was performed on the machine working in the strictly air-conditioned room, was described

[1] Choi, K.S., Soo, S., Chung, F.L. (2009). A virtual training simulator for learning cataract surgery with phacoemulsification. Computers in Biology and Medicine, 39 (11), 1020-1031.

[2] Longo, F., Massei, M., Nicoletti, L. (2012). An application of modeling and simulation to support industrial plants design. International Journal of Modeling, Simulation, and Scientific Computing, 3, 1240001.

[3] Zheng, W., Zhenyu, L., Jianrong, T., Yun, F., Changjiang, W. (2006). A virtual environment simulator for mechanical system dynamics with online interactive control. Advances in Engineering Software, 37 (10), 631-642.

[4] Klingstam, P., Gullander, P. (1999). Overview of simulation tools for computer-aided production engineering. Computers in Industry, 38 (2), 173-186.

[5] Fowler, J.W., Rose, O. (2004). Grand challenges in modeling and simulation of complex manufacturing systems. Simulation, 80 (9), 469-476.

[6] Banks, J. (1998). Handbook of Simulation: Principles, Methodology, Advances, Applications and Practice. Wiley.

[7] Sładek, J., Szewczyk, D. (2012). Usage of I++ Simulator for didactic and research activities. Measurements Automation Robotics [Pomiary Automatyka Robotyka], 4, 66-70.

[8] Gómez, E., Maresca, P., Caja, J., Barajas, C., Berzal, M. (2011). Developing a new interactive simulation environment with Macromedia Director for teaching applied dimensional metrology. Measurement, 44 (9), 1730-1746.

[9] Messtechnik Wetzlar GmbH. I++ Simulator Manual.

[10] Hong, S., Jung, M., Lee, K. (2006). An analytic method for detecting collisions to develop simulator of coordinate measuring machine. In IJCC Workshop 2006 on Digital Engineering, February 8-9, 2006. South Korea.

[11] Sładek, J. (2011). Accuracy of coordinate measurement. Cracow University of Technology.

[12] Ramu, P., Yagüe, J.A., Hocken, R.J., Miller, J. (2011). Development of a parametric model and virtual machine to estimate task specific measurement uncertainty for a five-axis multi-sensor coordinate measuring machine. Precision Engineering, 35 (3), 431-439.

[13] Wilhelm, R.G., Hocken, R., Schwenke, H. (2001). Task specific uncertainty in coordinate measurement. CIRP Annals - Manufacturing Technology, 50 (2), 553-563.

[14] Trapet, E. et al. (1999). Traceability of coordinate measuring machines according to the method of the virtual measuring machine. PTB-Bericht F-35, Braunschweig, Germany.

[15] Sładek, J., Gąska, A. (2012). Evaluation of coordinate measurement uncertainty with use of virtual machine model based on Monte Carlo method. Measurement, 45 (6), 1564-1575.

Measurement Science Review

The Journal of Institute of Measurement Science of Slovak Academy of Sciences

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IMPACT FACTOR 2017: 1.345
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CiteScore 2017: 1.61

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