Diagnosis of Missed Ductile Iron Melts with Process Modelling

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The paper presents an application of advanced data-driven (soft) models in finding the most probable particular causes of missed ductile iron melts. The proposed methodology was tested using real foundry data set containing 1020 records with contents of 9 chemical elements in the iron as the process input variables and the ductile iron grade as the output. This dependent variable was of discrete (nominal) type with four possible values: ‘400/18’, ‘500/07’, ‘500/07 special’ and ‘non-classified’, i.e. the missed melt. Several types of classification models were built and tested: MLP-type Artificial Neural Network, Support Vector Machine and two versions of Classification Trees. The best accuracy of predictions was achieved by one of the Classification Tree model, which was then used in the simulations leading to conversion of the missed melts to the expected grades. Two strategies of changing the input values (chemical composition) were tried: content of a single element at a time and simultaneous changes of a selected pair of elements. It was found that in the vast majority of the missed melts the changes of single elements concentrations have led to the change from the non-classified iron to its expected grade. In the case of the three remaining melts the simultaneous changes of pairs of the elements’ concentrations appeared to be successful and that those cases were in agreement with foundry staff expertise. It is concluded that utilizing an advanced data-driven process model can significantly facilitate diagnosis of defective products and out-of-control foundry processes.

[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.

[3] Koonce, D., Fang, C.H. & Tsai, S.C. (1997). Data mining tool for learning from manufacturing systems. Comput Ind Eng. 33, 27-30.

[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.

[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.

Archives of Foundry Engineering

The Journal of Polish Academy of Sciences

Journal Information

CiteScore 2016: 0.42

SCImago Journal Rank (SJR) 2016: 0.192
Source Normalized Impact per Paper (SNIP) 2016: 0.316


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