A Soft Computing System for Modelling the Manufacture of Steel Components
In this paper we present a soft computing system developed to optimize the laser milling manufacture of high value steel components, a relatively new and interesting industrial technique. This multidisciplinary study is based on the application of neural projection models in conjunction with identification systems, in order to find the optimal operating conditions in this industrial issue. Sensors on a laser milling centre capture the data used in this industrial case study defined under the frame of a machine-tool that manufactures steel components like high value molds and dies. The presented model is based on a two-phase application. The first phase uses a neural projection model capable of determine if the data collected is informative enough based on the existence of internal patterns. The second phase is focus on identifying a model for the laser-milling process based on low-order models such as Black Box ones. The whole system is capable of approximating the optimal form of the model. Finally, it is shown that the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples, is the most appropriate model to control such industrial task for the case of steel components.
KeywordsTest Piece Angle Error Wall Angle Steel Component Soft Computing Model
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- 1.Wendland, J., Harrison, P.M., Henry, M., Brownell, M.: Deep Engraving of Metals for the Automotive Sector Using High Average Power Diode Pumped Solid State Lasers. In: Proceedings of the 23rd International Conference on Applications of Lasers and Electro-Optics (ICALEO 2005) (2005)Google Scholar
- 6.Seung, H.S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. In: Advances in Neural Information Processing Systems, vol. 10, pp. 350–356 (1998)Google Scholar
- 7.Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: Proc. of the 10th European Symposium on Artificial Neural Networks (ESANN 2002) (2002)Google Scholar
- 9.Ljung, L.: System Identification. Theory for the User. Prentice-Hall, Englewood Cliffs (1999)Google Scholar
- 13.Stoica, P., Söderström, T.: A useful parametrization for optimal experimental design. IEEE Trans. Automatic. Control AC-27 (1982)Google Scholar
- 14.He, X., Asada, H.: A new method for identifying orders of input-output models for nonlinear dynamic systems. In: Proceedings of the American Control Conference (1993)Google Scholar
- 16.Arias, G., Ciurana, J., Planta, X., Crehuet, A.: Analyzing Process Parameters that influence laser machining of hardened steel using Taguchi method. In: Proceedings of 52nd International Technical Conference, SAMPE 2007 (2007)Google Scholar