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Diagnosis of Missed Ductile Iron Melts with Process Modelling

Diagnosis of Missed Ductile Iron Melts with Process Modelling References[1] Harding, J.A., Shahbaz, M., Srinivas, M. & Kusiak, A. (2006). Data mining in manufacturing: a review. Trans. ASME, J Mfg Sci Engng/. 128, 969-976.[2] Kusiak, A. (2006). Data mining: manufacturing and service applications. Int. J. Prod. Res. 44, 4175-4191. 10.1080/00207540600632216[3] Koonce, D., Fang, C.H. & Tsai, S.C. (1997). Data mining tool for learning from manufacturing systems. Comput Ind Eng. 33, 27-30.10.1016/S0360-8352(97)00033-8[4] Tsang, K.F., Lau, H.C.W. & Kwok, S.K. (2007). Development of a data mining system for continual process quality improvement. Proc Inst Mech Eng Part B: J Eng Manuf, 221, 179-193.[5] Tseng, T.L., Jothishankar, M.C., Wu, T., Xing, G. & Jiang, F. (2004). Applying data mining approaches for defect diagnosis in manufacturing industry. In IIE Annual Conference and Exhibition, Institute of Industrial Engineers, 2004 (pp. 1441-1447). Houston, USA.[6] Vazan, P., Tanuska, P., Kebisek, M., Moravcik, O. (2012). Data Mining Model Building as a Support for Decision Making in Production Management. In Advances in Computer Science, Engineering & Applications, Proc. Second International Conference on Computer Science, Engineering and Applications (ICCSEA 2012), Vol. 1, (pp. 695-701). New Delhi, India, May 25 27, 2012. [7] Ghosh, S. & Maiti, J. (2014). Data mining driven DMAIC framework for improving foundry quality - a case study. Production Planning & Control. 25, 478-493.10.1080/09537287.2012.709642[8] Perzyk, M. & Kochanski, A. (2003). Detection of causes of casting defects assisted by artificial neural networks. Journal of Engineering Manufacture, Proceedings of the Institution of Mechanical Engineers, Part B. 217, 1279-1284[9] Zhang, G. (1990). A new diagnosis theory with two kinds of quality. Total Quality Management. 1(2), 249-257.[10] Perzyk, M. & Kozlowski, J. (2016). Methodology of Fault Diagnosis in Ductile Iron Melting Process. Archives of Foundry Engineering, 16(4), 101-108[11] Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Foundry Engineering de Gruyter

Diagnosis of Missed Ductile Iron Melts with Process Modelling

Archives of Foundry Engineering , Volume 17 (4): 4 – Dec 20, 2017

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Publisher
de Gruyter
Copyright
© by M. Perzyk
ISSN
2299-2944
eISSN
2299-2944
DOI
10.1515/afe-2017-0142
Publisher site
See Article on Publisher Site

Abstract

References[1] Harding, J.A., Shahbaz, M., Srinivas, M. & Kusiak, A. (2006). Data mining in manufacturing: a review. Trans. ASME, J Mfg Sci Engng/. 128, 969-976.[2] Kusiak, A. (2006). Data mining: manufacturing and service applications. Int. J. Prod. Res. 44, 4175-4191. 10.1080/00207540600632216[3] Koonce, D., Fang, C.H. & Tsai, S.C. (1997). Data mining tool for learning from manufacturing systems. Comput Ind Eng. 33, 27-30.10.1016/S0360-8352(97)00033-8[4] Tsang, K.F., Lau, H.C.W. & Kwok, S.K. (2007). Development of a data mining system for continual process quality improvement. Proc Inst Mech Eng Part B: J Eng Manuf, 221, 179-193.[5] Tseng, T.L., Jothishankar, M.C., Wu, T., Xing, G. & Jiang, F. (2004). Applying data mining approaches for defect diagnosis in manufacturing industry. In IIE Annual Conference and Exhibition, Institute of Industrial Engineers, 2004 (pp. 1441-1447). Houston, USA.[6] Vazan, P., Tanuska, P., Kebisek, M., Moravcik, O. (2012). Data Mining Model Building as a Support for Decision Making in Production Management. In Advances in Computer Science, Engineering & Applications, Proc. Second International Conference on Computer Science, Engineering and Applications (ICCSEA 2012), Vol. 1, (pp. 695-701). New Delhi, India, May 25 27, 2012. [7] Ghosh, S. & Maiti, J. (2014). Data mining driven DMAIC framework for improving foundry quality - a case study. Production Planning & Control. 25, 478-493.10.1080/09537287.2012.709642[8] Perzyk, M. & Kochanski, A. (2003). Detection of causes of casting defects assisted by artificial neural networks. Journal of Engineering Manufacture, Proceedings of the Institution of Mechanical Engineers, Part B. 217, 1279-1284[9] Zhang, G. (1990). A new diagnosis theory with two kinds of quality. Total Quality Management. 1(2), 249-257.[10] Perzyk, M. & Kozlowski, J. (2016). Methodology of Fault Diagnosis in Ductile Iron Melting Process. Archives of Foundry Engineering, 16(4), 101-108[11] Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.

Journal

Archives of Foundry Engineeringde Gruyter

Published: Dec 20, 2017

References