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NTIS 바로가기Journal of Korean Society of Industrial and Systems Engineering = 한국산업경영시스템학회지, v.44 no.4, 2021년, pp.12 - 22
서석준 (창원대학교 스마트제조융합협동과정) , 김흥섭 (창원대학교 스마트제조융합협동과정)
This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturi...
Ahn, Y. S., Lee, Y. H. and Lee, Y. D., Effect of Ti, B, Element and δ-ferrite on the Hot Ductility of STS 304, Korean Journal of Metals and Materials, 1992, Vol. 30, No. 7, pp. 799-806.
Ahn, Y., Lee. Y., and Lee, Y, Effect of Ti, B.Element and δ-ferrite on the Hot Ductility of STS304, Journal of the Korean Inst. of Met. & Mater., 1992, Vol. 30, No. 7, pp. 799-806.
Bae, C. M. and Nam, W. J., Effects of Sulfur and Boron Content on Hot Ductility and Machinability of STS303, Korean Journal of Metals and Materials, 1994, Vol. 32, No. 6, pp. 706-713.
Brinkman, C. and Gavin, H., Properties of austenitic stainless steels and their weld metals (influence of slight chemistry variations), West Conshohocken, PA: ASTM International, 1979.
Cheruvathur, S., Lass, E. A., and Campbell, C. E., Additive manufacturing of 17-4 PH stainless steel: post-processing heat treatment to achieve uniform reproducible microstructure, JOM, 2016, Vol. 68, No. 3, pp. 930-942.
Diaz-Uriarte, R. and De Andres, S. A., Gene selection and classification of microarray data using random forest, BMC Bioinformatics, 2006, Vol. 7, No. 1, pp. 1-13.
Jeong, Y. and Kang, C., Defect Type Prediction in Manufacturing Process using Association Rules, KSIE Spring Conference, 2004, pp. 310-313.
Jung, J., Effects of Homogenization Treatment Mn/S ratio and δ-ferrite on the Hot Workability of Free-Machining 303-series Austenitic Stainless Steels, Korean Journal of Metal and Materials, 2017, Vol. 55, No. 12, pp. 880-887.
Kang, D., Oh, S., Park, J., and Seo, M., Predicting the quality of steelplate products using artificial neural networks, KSQM Autumn Conference, 2019, p. 146.
Lee, S., Kim, Y., and Lee, Y, Effect of S and Mn on the Hot Workability of STS 316L and 309S steels, Journal of the Korean Inst. of Met. & Mater, 1998, Vol. 36, No. 10, pp. 1590-1598.
Lee, Y., Sim, S., and Kim. D., Effect of castring structure and S contents on the how workability of 18-8 stainless steel, Korean Journal of Metal and Materials, 1995, Vol. 1995, No. 1, p. 204.
Liu, H., Hu, D., and Fu, J., Analysis of MnS inclusions formation in resulphurised steel via modeling and experiments, Materials, 2019, Vol. 12, No. 2, p. 2028.
Maciejewski, J., The effects of sulfide inclusions on mechanical properties and failures of steel components, Journal of Failure Analysis and Prevention, 2015, Vol. 15, No. 2, pp. 169-178.
Nazabal, J. L., Urcola, J. J. and Fuentes, M., High-temperature deformation characteristics of free-machining steels, Metals Technology, 1982, Vol. 9, No. 1, pp. 323-326.
Nkonyana, T., Sun, Y., Twala, B., and Dogo, E., Performance evaluation of data mining techniques in steel manufacturing industry, Procedia Manufacturing, Vol. 35, pp. 623-628.
Oliver, J., Jonsson, J. Y. and Talonen, J., Method for manufacturing and utilizing ferritic-austenitic stainless steel with high formability, U.S. Patent Application, 2013.
Osakada, K., Effects of strain rate and temperature in forming processes of metals, Le Journal de Physique IV, 1997, Vol. 7, No. C3, p. C3-XXXVII.
Park, J., Lee, J., and Seo, Kyung., Reliability Evaluation of Small Rolling Process Temperature Prediction Using Machine Learning, Journal of Institute of Control, Robotics and Systems, 2020, Vol. 26, No. 6, pp. 412-422.
Ruiz, E., Ferreno, D., Cuartas, M., Lopez, A., Arroyo, V., and Gutierrez-Solana, F., Machine learning algorithms for the prediction of the strength of steel rods: an example of data-driven manufacturing in steelmaking, International Journal of Computer Integrated Manufacturing, 2020, Vol. 33, No. 9, pp. 880-894.
Sa, G., Lee, S., Jang, Y. and Park, C., Analysis of Data from a Rubber Manufacturing Process Based on Hadoop Ecosystem and Machine Learning for Smart Factor, Journal of KIISE, 2020, Vol. 26, No. 12, pp. 519-527.
Toledo, G. A., Campo, O., and Lainez, E., Influence of sulfur and Mn/S ratio on the hot ductility of steels during continuous casting, Steel Research, 1993, Vol. 64, No. 6, pp. 292-299.
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