dr. J. Hazrati Marangalou (Javad)

A micro-mechanical model for roughness transfer in cold rolling of steel strips

Emerging of the “smart” industry 4.0 technologies challenges the conventional manufacturing processes such as rolling of the steel strips. In this regard, automotive and packaging industries requirements on surface texture of the steel strips are becoming increasingly stringent. Surface texture determines the visual appearance and tribological behavior of the strips. The goal of this project is to understand the fundamental physics of the roughness transfer from a textured roll surface to a metal strip surface and to develop a physical roughness transfer model that predicts the final surface properties of the strip based on the rolling process parameters.

Multi-scale Friction Modeling in Aluminum Sheet Forming

FORMALUB

Friction is one of the key parameters in sheet metal forming processes which can directly affect the formability of the product. In a typical forming process, the coefficient of friction is a transient parameter and varies from location to location throughout the part due to evolution of the contact conditions at the tool-sheet metal interface. The contact condition in turn is a function of the topography of the contacting surfaces, contact loads, material and lubricant behavior and temperature. Nowadays, finite element (FE) analysis is a standard tool to study the formability of sheet metals in forming processes. However, a constant coefficient of (Coulomb) friction is generally used in the FE simulations which does not account for the evolving contact condition due to the change in contact loads during the forming process. This leads to inaccurate and unreliable results. Therefore, accurate prediction of coefficient of friction is of utmost importance for the accuracy of the formability analyses and thus a zero-defect production. In this project, a multi-scale friction model is developed that accounts for the significant variables that can influence the coefficient of friction.

Digital Twin: dealing with uncertainties in manufacturing processes

Model (simulation)-based optimization is nowadays a common tool in designing manufacturing processes where optimal use of material and process capabilities are utilized. However, this strategy is subjected to different types of uncertainties. As these uncertainties are neglected, very often an optimal deterministic process design is found which is at the limits of the material, process capabilities and the accuracy of the models. The uncertainties in the model-based optimization strategies are related to the inherent variations in the process inputs such as material properties, geometry, process settings (machine dynamics, lubrication) or changing environmental conditions (temperature). The other source of uncertainty in these strategies originates from the inaccurate or unphysical assumptions in the (Multi-scale FE or surrogate) models. The main goal of the project is to find the best model updating strategy based on measured input and output data. Such strategy is able to realize different sources of uncertainties, update the necessary parameters and reduce the discrepencies between predicted results and the measured output data.