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.
Metamodel-based optimization of hot rolling processes in the metal industry
To maximize the throughput of a hot rolling mill, the number of passes has to be reduced. This can be achieved by maximizing the thickness reduction in each pass. For this purpose, exact predictions of roll force and torque are required. Hence, the predictive models that describe the physical behavior of the product have to be accurate and cover a wide range of different materials. Due to market requirements, a lot of new materials are tested and rolled. If these materials are chosen to be rolled more often, a suitable flow curve has to be established. It is not reasonable to determine those flow curves in laboratory, because of costs and time. A strong demand for quick parameter determination and the optimization of flow curve parameter with minimum costs is the logical consequence. Therefore, parameter estimation and the optimization with real data, which were collected during previous runs, is a promising idea. Producers benefit from this data-driven approach and receive a huge gain in flexibility when rolling new materials, optimizing current production, and increasing quality. This concept would also allow to optimize flow curve parameters, which have already been treated by standard methods. In this article, a new data-driven approach for predicting the physical behavior of the product and setting important parameters is presented. We demonstrate how the prediction quality of the roll force and roll torque can be optimized sustainably. This offers the opportunity to continuously increase the workload in each pass to the theoretical maximum while product quality and process stability can also be improved.
Compressive and flexural properties of hemp fiber reinforced concrete
The compressive and flexural properties of hemp fiber reinforced concretes (FRC) were examined in this paper. Natural hemp fiber was mixed using dry and wet mixing methods to fabricate the FRC. Mechanical properties of the FRC were investigated. The main factors affecting compressive and flexural properties of the FRC materials were evaluated with an orthogonal test design. Fiber content by weight has the largest effect. The method for casting hemp FRC has been optimised. Under the optimum conditions, compressive strength increased by 4 %, flexural strength increased by 9 %, flexural toughness increased by 144 %, and flexural toughness index increased by 214 %.