Agarwal, K., and·Shivpuri, R. 2013. “On line prediction of surface defects in hot bar rolling based on Bayesian hierarchical modeling”. Journal of Intelligent Manufacturing 26(4): 785-800.
Chen, W. C., Tseng, S. S., and Wang, C. Y. 2005. “A Novel manufacturing defect detection method using association rule mining techniques”. Expert System with Applications 29: 807-815.
Choudhary, A. K., Tiwari, M. K., and Harding, J. A. 2009. “Data Mining in Manufacturing: A Review Based on the Kind of Knowledge”. Journal of Intelligent Manufacturing 20(5): 501-521.
Deng, Z. H., Zhang, X. H., Liu, W., and Cao, H. 2009. “A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding”. International Journal of Advanced Manufacturing Technology 45(9-10): 859–866.
Kusiak, A. and Kurasek, C., 2001.” Data Mining of Printed Circuit Board defects”. IEEE Transactions on Robotics and Automation 17(2):191-196.
Olsen, D.L.and Delen, D., 2008. “Advanced data mining techniques”, Springer.
Paralikas, J., Salonitis, K., and Chryssolouris, G. 2009. “Optimization of the roll forming process parameters—a semi empirical approach”. International Journal of Advanced Manufacturing Technology 47(9–12): 1041–1052.
Pérez, D., García-Fernández, F.J., Díaz, I., Cuadrado A.A., Ordonez, D.G., Díez, A.B., and Domínguez, M. 2013. “Visual analysis of a cold rolling process using a dimensionality reduction approach”. Engineering Applications of Artificial Intelligence 26: 1865–1871.
Sedighi, M., & Afshari, D. 2010. “Creep feed grinding optimization by an integrated GA-NN system”. Journal of Intelligent Manufacturing 21(6): 657–663.
Tsai, C. Y., Chiu, C. C., and Chen, J. S. 2006. “A Case based reasoning system for PCB defect prediction”. Expert Systems with Applications 28: 813-822.
Tseng, T. L., Jothishanker, M. C., and Wu, T. 2004. Quality Control Problem in Printed Circuit Board Manufacturing–An Extended Rough Set Theory Approach”, Journal of Manufacturing System 23(1): 56-72.
Valavanis, I., and Kosmopoulos, D., 2010. “Multiclass defect detection and classification in weld radiographic images using geometric and texture features”. Expert Systems with Applications 37(12): 7606-7614.
Wang, C. H., Kuo, W., and Bensmail, H., 2006. “Detection and classification of defects patterns on semiconductor wafers”. IIE Transactions 38: 1059-1068.
Wendt, P., Frech, W., and Leifgen, U. 2007. “Cold rolling defect, “stickers” and countermeasures”. Heat processing 5(2): 127-135.
Yang, S. Y., Tansel, I. N., and Kropas-Hughes, C. V. 2003. “Selection of optimal material and operating conditions in composite manufacturing. Part I: Computational tool”. International Journal of Machine Tools & Manufacture 43(2): 169–173.
Yazdchi, M. R., Golibagh Mahyari, A., and Nazeri, A. 2008. “Detection and Classification of Surface Defects of Cold Rolling Mill Steel Using Morphology and Neural Network”. International Conference on Computational Intelligence for Modelling Control & Automation, IEEE.
Za´rate, L.E., and Dias, S.M. 2009. “Qualitative behavior rules for the cold rolling process extracted from trained ANN via the FCANN method”. Engineering Applications of Artificial Intelligence 22: 718–731.
Zhang, X.H., Deng, Z.H., Liu, W., and Cao, H. 2013. “Combining rough set and case based reasoning for process conditions selection in camshaft grinding”. Journal of Intelligent Manufacturing 24: 211–224.