Metamodelling the hot deformation behaviour of titanium alloys using a mean-field approach

During the thermomechanical processing of titanium alloys in the β-domain, the β-phase undergoes restoration phenomena. This work describes them by a mean-field physical model that correlates the flow stress with the microstructural evolution. To reduce the computational time of process simulations, metamodels are developed for specific outputs of the mean-field physical model using Artificial Neural Network (ANN) and Decision Tree Regression (DTR). The performance of the obtained metamodels is evaluated in terms of the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean relative error (MRE). No significant difference was observed between R2training and R2testing, meaning that all the metamodels correctly generalise the overall behaviour of the outputs for a wide range of inputs. The evolution of the metamodel outputs is compared with the model predictions in two different situations: 1) at a constant strain rate and temperature, and 2) during Finite Element (FE) simulations of the hot deformation of a hat-shaped sample, where temperature and effective strain rate vary at each element during deformation. The evolution of the outputs at constant and non-constant strain rates and temperature demonstrated the robustness of the metamodels in predicting the heterogeneous deformation within a workpiece. The computational time required by the metamodels to calculate selected outputs can be more than 100 times less than that of the model itself at a constant strain rate using MATLAB® and up to 19% less when coupled with FE simulations. The simulation results combined with microstructural analysis are used to visualise the different restoration mechanisms occurring in different regions of the hat-shaped sample as a function of the local thermomechanical history. The changes in strain rate and temperature during deformation influence the evolution of the wall dislocation density and the immobilisation rate of mobile dislocations at subgrain boundaries, leading to different kinetics of microstructure evolution.Fil: Miller Branco Ferraz, Franz. Graz University Of Technology.; AustriaFil: Sztangret, Lukasz. AGH University of Science and Technology; PoloniaFil: Carazo, Fernando Diego. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Mecanica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Buzolin, Ricardo Henrique. Graz University Of Technology.; AustriaFil: Wang, Peng. Graz University Of Technology.; AustriaFil: Szeliga, Danuta. AGH University of Science and Technology; PoloniaFil: dos Santos Effertz, Pedro. No especifíca;Fil: Macio, Piotr. AGH University of Science and Technology; PoloniaFil: Krumphals, Alfred. No especifíca;Fil: Poletti, Maria Cecilia. Graz University Of Technology.; Austri

Computational automation for efficient design of acoustic metamaterials

Acoustic metamaterials (AMMs) are an exciting technology because they are capable of responding to vibrations in ways that are impossible to achieve with conventional materials. However, realization of AMMs requires engineering design to provide a connection between first-principles research and production of parts that perform as expected. Designing AMMs is a challenging endeavor because evaluating designs is costly and manufacturing metamaterials requires precise techniques with small minimum resolutions. To address these challenges, new computational tools are necessary to aid design. This work proposes three tasks that improve the capabilities of design for AMM while being extensible to other engineering design automation tasks. The first task is to develop a design exploration tool that improves the computational efficiency of identifying sets of high-performing designs in a design space that is sparse and comprises mixed discrete/continuous data. The second task is to develop a process for designers to evaluate manufacturability of difficult-to-manufacture parts and drive co-development of manufacturing methods and AMM. In the final task, a machine learning based method is developed to efficiently model AMM with heterogeneous arrangements of their microstructures such that strict homogenization is infeasible. The outcomes from completing these tasks will provide a significant and novel improvement over existing methods of designing AMMs.Mechanical Engineerin

SOLID-SHELL FINITE ELEMENT MODELS FOR EXPLICIT SIMULATIONS OF CRACK PROPAGATION IN THIN STRUCTURES

Crack propagation in thin shell structures due to cutting is conveniently simulated using explicit finite element approaches, in view of the high nonlinearity of the problem. Solidshell elements are usually preferred for the discretization in the presence of complex material behavior and degradation phenomena such as delamination, since they allow for a correct representation of the thickness geometry. However, in solid-shell elements the small thickness leads to a very high maximum eigenfrequency, which imply very small stable time-steps. A new selective mass scaling technique is proposed to increase the time-step size without affecting accuracy. New ”directional” cohesive interface elements are used in conjunction with selective mass scaling to account for the interaction with a sharp blade in cutting processes of thin ductile shells