Most cold rolling mills are prone to chatter problem. Chatter marks are often observed on the strip surface in cold rolling mill leading to downgrade and rejection of rolled material. Chatter impact product quality as well as productivity of mill. In absence of online chatter detection no corrective action can be taken immediately and whole campaign gets affected. Most conventional approach for online chatter detection is by using vibration measurement of mill stands in time & frequency domain. Present work proposes two approaches to detect chatter in cold rolling mill using a statistical technique called Principal Component Analysis (PCA). In this paper two methods are used for chatter detection. First method applies PCA on Fast Fourier Transform (FFT) to differentiate between chatter and non-chatter condition. Second method applies PCA on statistical parameters calculated from raw vibration data to detect chatter.

An attempt has been made to develop Virtual Training Platform for Pickling Line Tandem Cold Mill (PLTCM) intended to deliver faster and enhanced learning experience of the process operator by leveraging Advanced Data Modelling, Data Science and Digital Twin Technology. Virtual Training Platform has modules for virtual tour of the plant, setup simulation, real-time rolling simulation, performance evaluation. Rolling Simulation has ANN based Automatic gauge control model and DNN based shape prediction model. Platform also comprises of a Natural Language Processing based chatbot for online query related to process line. Keywords: tandem cold Mill; Deep Neural Network (DNN); Virtual tour; Automatic Gauge Control (AGC), shape Control; chatbot