Abstract
In this paper, a method to characterize the chatter phenomenon in a cold rolling process is proposed. This approach is based on obtaining a global nonlinear dynamical MISO model, relating four input variables and the exit strip thickness as the output variable. In a second stage, local linear models are obtained for all working points using sensitivity analysis on the nonlinear model to get input/output small signal models. Each local model is characterized by a high dimensional vector containing the frequency response functions (FRF) of the four SISO resulting models. Finally, the FRF’s are projected on a 2D space, using the t-SNE algorithm, in order to visualize the dynamical changes of the process. Our results show a clear separation between chatter condition and other vibration states, allowing an early detection of chatter as well as being a visual analysis tool to study the chatter phenomenon.
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Pérez, D. et al. (2012). Visual Analysis of a Cold Rolling Process Using Data-Based Modeling. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_25
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DOI: https://doi.org/10.1007/978-3-642-32909-8_25
Publisher Name: Springer, Berlin, Heidelberg
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