Total precision inspection of machine tools with virtual metrology

References

• Abellan-Nebot, J. V., and F. R., Subirón. 2010. “A Review of Machining Monitoring Systems Based on Artificial Intelligence Process Models.” The International Journal of Advanced Manufacturing Technology 47 (1–4): 237–257. doi:10.1007/s00170-009-2191-8.
   Web of Science ®Google Scholar
• Chandrasekaran, M., M. Muralidhar, C. M. Krishna, and U. S. Dixit. 2010. “Application of Soft Computing Techniques in Machining Performance Prediction and Optimization: A Literature Review.” The International Journal of Advanced Manufacturing Technology 46 (5–8): 445–464. doi:10.1007/s00170-009-2104-x.
   Web of Science ®Google Scholar
• Cheng, F.-T., C.-F. Chen, Y.-S. Hsieh, H.-H. Huang, and C.-C. Wu. 2015. “Intelligent Sampling Decision Scheme Based on the AVM System.” International Journal of Production Research 53 (7): 2073–2088. doi:10.1080/00207543.2014.955924.
   Web of Science ®Google Scholar
• Cheng, F.-T., Y.-T. Chen, Y.-C. Su, and D.-L. Zeng. 2008. “Evaluating Reliance Level of a Virtual Metrology System.” IEEE Transactions on Semiconductor Manufacturing 21 (1): 92–103. doi:10.1109/TSM.2007.914373.
   Web of Science ®Google Scholar
• Cheng, F.-T., and Y.-C. Chiu. 2013. “Applying the Automatic Virtual Metrology System to Obtain Tube-to-Tube Control in a PECVD Tool.” IIE Transactions 45 (6): 670–681. doi:10.1080/0740817X.2012.725507.
   Web of Science ®Google Scholar
• Cheng, F.-T., H.-C. Huang, and C.-A. Kao. 2007. “Dual-Phase Virtual Metrology Scheme.” IEEE Transactions on Semiconductor Manufacturing 20 (4): 566–571. doi:10.1109/TSM.2007.907633.
   Web of Science ®Google Scholar
• Cheng, F.-T., H.-C. Huang, and C.-A. Kao. 2012. “Developing an Automatic Virtual Metrology System.” IEEE Transactions on Automation Science and Engineering 9 (1): 181–188. doi:10.1109/TASE.2011.2169405.
   Web of Science ®Google Scholar
• Cheng, F.-T., C.-A. Kao, C.-F. Chen, and W.-H. Tsai. 2015. “Tutorial on Applying the VM Technology for TFT-LCD Manufacturing.” IEEE Transactions on Semiconductor Manufacturing 28 (1): 55–69. doi:10.1109/TSM.2014.2380433.
   Web of Science ®Google Scholar
• Daubechies, I. 1992. “Ten Lectures on Wavelets.” Philadelphia: Society for Industrial and Applied Mathematics 61: 198–202.
   Google Scholar
• Donoho, D. L. 1995. “De-Noising by Soft-Thresholding.” IEEE Transactions on Information Theory 41 (3): 613–627. doi:10.1109/18.382009.
   Web of Science ®Google Scholar
• Ghosh, N., Y. B. Ravi, A. Patra, S. Mukhopadhyay, S. Paul, A. R. Mohanty, and A. B. Chattopadhyay. 2007. “Estimation of Tool Wear during CNC Milling Using Neural Network-Based Sensor Fusion.” Mechanical Systems and Signal Processing 21 (1): 466–479. doi:10.1016/j.ymssp.2005.10.010.
   Web of Science ®Google Scholar
• Haber, R. E., J. E. Jiménez, C. R. Peres, and J. R. Alique. 2004. “An Investigation of Tool-wear Monitoring in a High-Speed Machining Process.” Sensors and Actuators A: Physical 116 (3): 539–545. doi:10.1016/j.sna.2004.05.017.
   Web of Science ®Google Scholar
• Huang, Y.-T., and F.-T. Cheng. 2011. “Automatic Data Quality Evaluation for the AVM System.” IEEE Transactions on Semiconductor Manufacturing 24 (3): 445–454. doi:10.1109/TSM.2011.2154910.
   Web of Science ®Google Scholar
• Huang, Y.-T., F.-T. Cheng, Y.-H. Shih, and Y.-L. Chen. 2014. “Advanced ART2 Scheme for Enhancing Metrology-data-quality Evaluation.” Journal of the Chinese Institute of Engineers 37 (8): 1064–1079. doi:10.1080/02533839.2014.912773.
   Web of Science ®Google Scholar
• Jemielniak, K., T. Urbański, J. Kossakowska, and S. Bombiński. 2012. “Tool Condition Monitoring based on Numerous Signal Features.” The International Journal of Advanced Manufacturing Technology 59 (1–4): 73–81. doi:10.1007/s00170-011-3504-2.
   Web of Science ®Google Scholar
• Kaiser, W., R. P. Marques, and A. F. Correa. 2006. “Impact of Current Crest Factor at High and Low Frequency Operation on Fluorescent Lamp Electrodes.” In Proceedings of 41st IAS Annual Meeting. Conference Record of the 2006 IEEE Industry Applications Conference, Tampa, Florida, United States, 8–12 October 2006: 236–241. New York: IEEE.
   Google Scholar
• Kim, G. D., and C. N. Chu. 2001. “In-Process Tool Fracture Monitoring in Face Milling Using Spindle Motor Current and Tool Fracture Index.” The International Journal of Advanced Manufacturing Technology 18 (6): 383–389. doi:10.1007/s001700170047.
   Web of Science ®Google Scholar
• Lahdelma, S., and E. Juuso. 2011. “Signal Processing and Feature Extraction by Using Real Order Derivatives and Generalised Norms. Part 1: Methodology.” International Journal of Condition Monitoring 1 (2): 46–53. doi:10.1784/204764211798303805.
   Google Scholar
• Lei, Y., Z. He, Y. Zi, and Q. Hu. 2008. “Fault Diagnosis of Rotating Machinery based on a New Hybrid Clustering Algorithm.” The International Journal of Advanced Manufacturing Technology 35 (9–10): 968–977. doi:10.1007/s00170-006-0780-3.
   Web of Science ®Google Scholar
• Li, X. 2001. “Detection of Tool Flute Breakage in End Milling Using Feed-Motor Current Signatures.” IEEE/ASME Transactions on Mechatronics 6 (4): 491–498. doi:10.1109/3516.974863.
   Web of Science ®Google Scholar
• Lin, T.-H., F.-T. Cheng, W.-M. Wu, C.-A. Kao, A.-J. Ye, and F.-C. Chang. 2009. “NN-Based Key-Variable Selection Method for Enhancing Virtual Metrology Accuracy.” IEEE Transactions on Semiconductor Manufacturing 22 (1): 204–211. doi:10.1109/TSM.2008.2011185.
   Web of Science ®Google Scholar
• Lu, C. 2008. “Study on Prediction of Surface Quality in Machining Process.” Journal of Materials Processing Technology 205 (1–3): 439–450. doi:10.1016/j.jmatprotec.2007.11.270.
   Web of Science ®Google Scholar
• Mallat, S. G. 1989. “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (7): 674–693. doi:10.1109/34.192463.
   Web of Science ®Google Scholar
• Mou, J., and C. R. Liu. 1992. “A Method for Enhancing the Accuracy of CNC Machine Tools for On-Machine Inspection.” Journal of Manufacturing Systems 11 (4): 229–237. doi:10.1016/0278-6125(92)90023-9.
   Web of Science ®Google Scholar
• Su, Y.-C., F.-T. Cheng, M.-H. Hung, and H.-C. Huang. 2006. “Intelligent Prognostics System Design and Implementation.” IEEE Transactions on Semiconductor Manufacturing 19 (2): 195–207. doi:10.1109/TSM.2006.873512.
   Web of Science ®Google Scholar
• Teti, R., K. Jemielniak, G. O’Donnell, and D. Dornfeld. 2010. “Advanced Monitoring of Machining Operations.” CIRP Annals-Manufacturing Technology 59 (2): 717–739. doi:10.1016/j.cirp.2010.05.010.
   Web of Science ®Google Scholar
• Tukey, J. W. 1977. Exploratory Data Analysis. Boston, MA: Addison-Wesley.
   Google Scholar
• Valiño, G., C. M. Suárez, J. C. Rico, B. J. Álvarez, and D. Blanco. 2012. “Comparison between a Laser Micrometer and a Touch Trigger Probe for Workpiece Measurement on a CNC Lathe.” Advanced Materials Research 498: 49–54. doi:10.4028/www.scientific.net/AMR.498.49.
   Google Scholar
• Walpole, R. E., R. H. Myers, S. L. Myers, K. Ye, and K. Yee. 2002. Probability and Statistics for Engineers and Scientists. 7th ed. Boston, MA: Prentice Hall.
   Google Scholar
• Wang, H., and P. Chen. 2009. “Fault Diagnosis Method Based on Kurtosis Wave and Information Divergence for Rolling Element Bearings.” WSEAS Transactions on Systems 8 (10): 1155–1165.
   Google Scholar
• Witten, I. H., and E. Frank. 2005. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, CA: Morgan Kaufman.
   Google Scholar