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This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals using the regression model and I-kaz method. The detection of tool wear was done automatically using the in-house developed regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out on a CNC turning machine Colchester Master Tornado T4 in dry cutting condition, and Kistler 9255B dynamometer was used to measure the cutting force signals, which then stored and displayed in the DasyLab software. The progression of the cutting tool flank wear land (VB) was indicated by the amount of the cutting force generated. Later, the I-kaz was used to analyze all the cutting force signals from beginning of the cut until the rejection stage of the cutting tool. Results of the IKaz analysis were represented by various characteristic of I-kaz 3D coefficient and 3D graphic presentation. The I-kaz 3D coefficient number decreases when the tool wear increases. This method can be used for real time tool wear monitoring., {"references":["Qiang Liu, Y. Altintas, \"On-line monitoring of flank wear in turning\nwith multilayered feed-forward neural network,\" International Journal\nof Machine Tools and Manufacture, 39, 1999, pp. 1945-1959.","Nuawi M. Z., Lamin F., Nor M. J. M., Jamaluddin N., Abdullah S.,\nNizwan C. K. E., \"Integration of I-kaz Coefficient and Taylor Tool Life\nCurve for Tool Wear Progression Monitoring in Machining Process, \"\nInternational Journal of Mechanics, 3 (1), 2007, pp. 45-50.","Kurada, S. and C. Bradley, \"A review of machine vision sensors for\ntool condition monitoring,\" Computers in Industry, 34(1), 1997, pp. 55\n- 72.","W. H. Wang, G. S. Hong, Y. S. Wong and K. P. Zhu, \"Sensor fusion\nfor on-line tool condition monitoring in milling,\" International Journal\nof Production Research, In Press, 45 (21), 2007, pp. 5095-5116.","Z. Kunpeng, W. Y. San, H. G. Soon, \"Wavelet analysis of sensor\nsignals for tool condition monitoring: A review and some new results,\"\nInternational Journal of Machine Tools and Manufacture, 2009.","Lin S.C and Lin R.J., \"Tool wear monitoring in face milling using force\nsignals,\" Journal of material processing and technology, 1996, pp.\n136-142.","D.E. Dimla Snr., \"Tool wear monitoring using cutting force\nmeasurements,\" 15th NCMR: Advances in Manufacturing Technology\nXIII, University of Bath, 6-8 Sep. 1999, pp. 33-37.","E. Kuljanic, M. Sortino, \"TWEM : A method based on cutting forcesmonitoring\ntool wear in face milling,\" International Journal of\nMachine Tools & Manufacture, 45, 2005, pp. 29-34.","K.J. Lee, T.M. Lee, M.Y. Yang, \"Tool wear monitoring system for\nCNC end milling using a hybrid approach to cutting force regulation,\"\nInternational Journal Advance Manufacturing Technology, 32, 2007,\npp. 8-17.\n[10] Oraby SE, Hayhurst DR., \"Tool life determination based on the\nmeasurement of wear and tool force ratio variation,\" International\nJournal Machine Tools Manufacturing, 44, 2004, pp. 1261-1269.\n[11] Srinivas J., Rama Kotaiah K., \"Tool wear monitoring with indirect\nmethods,\" Manufacturing Technology Today, India 4, 2005, pp. 7-9.\n[12] Nuawi M. Z., Lamin F., Nor M. J. M., Jamaluddin N., Abdullah S.,\nNizwan C. K. E., \"Development of Integrated Kurtosis-Based\nAlgorithm for Z-Filter Technique,\" Journal of Applied Science, 8 (8),\n2008, pp. 1541-1547.\n[13] Che Haron, C. H., Ginting, A., Goh, J. H., \"Tool life and surface\nintegrity in turning titanium alloy,\" Journal of Materials and\nProcessing Technology, 118 (1-3), 2001, pp. 231-237.\n[14] ISO (International Organization for Standardization) (ed.), \"Tool-life\nTesting with Single-Point Turning Tools (ISO 3685),\" 2nd edition,\nReference Number ISO 3685: (1993)(E)."]}

Modeling of a manufacturing system enables one to identify the effects of key design parameters on the system performance and as a result to make correct decision. This paper proposes a manufacturing system modeling approach using a spreadsheet model based on queuing network theory, in which a static capacity planning model and stochastic queuing model are integrated. The model was used to improve the existing system utilization in relation to product design. The model incorporates few parameters such as utilization, cycle time, throughput, and batch size. The study also showed that the validity of developed model is good enough to apply and the maximum value of relative error is 10%, far below the limit value 32%. Therefore, the model developed in this study is a valuable alternative model in evaluating a manufacturing system, {"references":["Fowler, John, W., and Rose, O., \"Grand challenges in modeling and\nsimulation of complex manufacturing system\", Simulation, Vol. 80, No.\n9, pp. 469-476, 2004","Cooper, R.G., \"Third - Generation new product process\", Journal of\nProduct Innovation Management, Vol. 11, No. 1, pp. 3-14, 1994","Koo, P.H., Moodie, C.L., and Talavage, J.J., \"A spreadsheet model\napproach for integrating static capacity planning and stochastic queuing\nmodels\", International Journal of Production Research, Vol. 33, No. 5, pp. 1369-1385, 1995","Law, A.M., and McComas, M.G., \" Simulation of manufacturing\nsystem\", Proceedings of the Winter Simulation Conference, pp.56-59,1998","Taylor, D.G., English, J.R., and Graves, R.J., \"Designing new products:\nCompatibility with existing product facilities and anticipated product\nmix\", Integrated Manufacturing Systems, Vol. 5, No. (4/5), pp. 13-21,1994","Bermon, S., Feigen, G., and Hood, S., \"Capacity analysis of complex\nmanufacturing facilities\", Proceedings of 34th Conference on Decision &\nControl, New Orleans, December 1995.","Soundar, P., Han, P.B., \"Concurrent design of products for manufacturing system performance\", Proceedings IEEE International Engineering Management Conference. Ohio, Dayton. 1994","Shady, R., Spake, G., and Armstrong, B., \"Simulation of a new product\nwork cell\", Proceedings of Winter Simulation Conference, USA. 1997","Hopp, W.J., Mark, L.S., \"Factory Physics\", Irwin/McGraw Hill, Boston.\n1996\n[10] Vollmann, T.E., Berry, W.L., and Whybark, D.C., \"Manufacturing\nplanning and control systems\", 4th edition., Irwin/McGraw-Hill, New\nYork, 1997\n[11] Aomar, R., \"Product-mix analysis with discrete event simulation\",\nProceedings 2000 Winter Simulation Conference, USA. 2000.\n[12] Walid Abdul Kader, \"Capacity improvement of an unreliable production\nline - An analytical approach\", Computer & Operations Research, No.\n33, pp. 1695-1712, 2008\n[13] Chincholkar, M.M., Burroughs, T., and Herrmann, J.W., \"Estimating\nmanufacturing cycle time and throughput in flow shops with process drift\nand inspection\", Institutes of Systems Research and Department of Mechanical Engineering University of Maryland,2004\n[14] Papadopoulos, H.T., Heavey, C., and Browne, J. \"Queuing theory in\nmanufacturing systems analysis and design \", Chapman and Hall,\nLondon, 1993\n[15] Yu-Feng,W. and Thornton, A.C., \"Concurrent design for optimal\nproduction performance\", Paper DETC2002/DFM-34163 in CD-ROM\nProceedings of 2002 ASME Design Engineering Technical Conference,\nMontreal, Canada. 2002.\n[16] Herrmann, J.W., and Chincholkar, M., \"Design for Production: A Tool\nfor reducing manufacturing cycle Time\", Paper DETC2000/DFM-14002\nin CD-ROM Proceedings of DETC 2000, 2000 ASME Design Engineering Technical Conference. Baltimore, September, 2000"]}