Brno University of Technology

About: Brno University of Technology is a education organization based out in Brno, Czechia. It is known for research contribution in the topics: Computer science & Fracture mechanics. The organization has 6339 authors who have published 15226 publications receiving 194088 citations. The organization is also known as: Vysoké učení technické v Brně & BUT.

Geometrically Non-linear Finite Element Reliability Analysis of Steel Plane Frames With Initial Imperfections

Abstract: The random load carrying capacity of steel plane frames with bracing stiffness is studied. The load carrying capacity is evaluated using the geometrically non-linear FEM analysis. The incremental stiffness matrix of a slightly curved element utilized in the non-linear incremental analysis is listed. Initial imperfections are considered as random variables. Statistical analysis and Sobol sensitivity analysis are performed using the Latin Hypercube Sampling method. The effect of initial random imperfections on the load carrying capacity is studied, whilst assuming constant slenderness of the columns. The evaluation parameters are the pair of non-random values of elastic bracing stiffness, and system length of the columns. The paper illustrates that the load carrying capacity is very sensitive to initial crookedness of the columns in the event that the non-sway (symmetric) and sway (anti-symmetric) buckling modes coincide. In this case, the design load carrying capacity obtained from statistical ana...

Annealing strategies for enhancing mechanical properties of additively manufactured 316L stainless steel deposited by cold spray

Abstract: Cold spray is a solid-state metal deposition and additive manufacturing (AM) technology. The low processing temperatures in cold spraying result in less favorable properties of the deposits as compared to their fusion-based AM counterparts, particularly for materials with high strength such as 316L stainless steel. Therefore, annealing is generally required for the cold sprayed deposits to improve their mechanical properties. In this paper, three different annealing strategies are systematically investigated in order to optimize the microstructure and mechanical properties of cold sprayed 316L stainless steel deposits: air annealing, vacuum annealing, and hot isostatic pressing (HIP), the latter representing a method widely used for densification of components in fusion-based metal AM. The results indicate that the three annealing treatments improve the mechanical properties of the cold sprayed 316L stainless steel deposits through grain recrystallization and diffusion at oxide-free inter-particle interfaces. Such improvement is less pronounced for the air annealing as a formation of oxide inclusions impedes full inter-particle metallurgical bonding. This effect is suppressed in the vacuum annealing, resulting in a significant improvement in the tensile strength and ductility. Despite the significant improvement in the deposits' density, the HIP results in mechanical properties equivalent to those after vacuum annealing. The experimental results suggest that the strengthening of cold sprayed deposits is mainly dominated by the improved inter-particle bonding and particle grain structure rather than through a reduction of porosity.

Self-Reconfigurable Evolvable Hardware System for Adaptive Image Processing

Abstract: This paper presents an evolvable hardware system, fully contained in an FPGA, which is capable of autonomously generating digital processing circuits, implemented on an array of processing elements (PEs). Candidate circuits are generated by an embedded evolutionary algorithm and implemented by means of dynamic partial reconfiguration, enabling evaluation in the final hardware. The PE array follows a systolic approach, and PEs do not contain extra logic such as path multiplexers or unused logic, so array performance is high. Hardware evaluation in the target device and the fast reconfiguration engine used yield smaller reconfiguration than evaluation times. This means that the complete evaluation cycle is faster than software-based approaches and previous evolvable digital systems. The selected application is digital image filtering and edge detection. The evolved filters yield better quality than classic linear and nonlinear filters using mean absolute error as standard comparison metric. Results do not only show better circuit adaptation to different noise types and intensities, but also a nondegrading filtering behavior. This means they may be run iteratively to enhance filtering quality. These properties are even kept for high noise levels (40 percent). The system as a whole is a step toward fully autonomous, adaptive systems.