FEM Simulation of the Roll Deformation of Six-high CVC Mill in Cold Strip Rolling
Accurate prediction for the deformation of the roll system is a key to improve the accuracy of the preset model and on-line control model of strip cold rolling processes. A non-linear finite element contact model to simulate the roll system deformation has been developed for the CVC-6h cold strip rolling mill. The elastic-plastic deformation of the strip and the elastic deformation of the rolls are coupled in this model by the contact and friction between the roll and strip. The effects of the work roll bending force, the middle roll bending force and the middle roll transverse shifting on the roll gap profile have been analyzed systematically. Results show that the work roll bending is the most effective mean in these strip shape controlling means. The calculated roll gap profile is close to the measured values. This model can improve the accuracy of final products through a correct preset model and on-line control model of strip shape.
Analysis of Cold-Rolled Strip Profile in UCM Mill by Finite Element Method
In order to analyze the effect of entry strip thickness, entry strip crown and intermediate roll shifting on strip profile and rolling force distribution, a three dimension finite element model of rolls-strip in UCM mill was established and employed. The results showed that the increases of entry strip thickness and entry strip crown would lead to the increase of exit strip crown and edge drop under the same total rolling force; the strip crown and edge drop would be decreased if the intermediate roll shifting δi was decreased. Moreover, the rolling force distribution also was analyzed in details by changing the entry strip thickness, the entry strip crown and the intermediate roll shifting.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.