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Soft Magnetic Composites (SMCs) typically consist of large iron particles coated with a fairly thin inorganic layer. The combination of soft particles with a brittle layer causes, however, a rather poor mechanical behaviour of the SMCs. The particle boundaries of the specific SMC Somaloy 700 3P can be classified into four different types according to the complexity of their layers. Tests on both micro- and macroscale showed that the particle-boundary interface is critical in terms of failure.
The main objective of this work is to significantly deepen the understanding of the material and the structural behaviour of continuous-discontinuous SMC composites, following a holistic approach to investigate microscopic aspects, macroscopic mechanical behaviour as well as failure evolution at the coupon, structure and component level. In addition, criteria to evaluate the effect of hybridisation are introduced and modelling approaches are presented and discussed.
Hierarchically structured active materials in electrodes of lithium-ion cells are promising candidates for increasing gravimetric energy density and improving rate capability of the system. To investigate the influence of cathode structures on the performance of the whole cell, efficient tools for calculating effective transport properties of granular systems are developed and their influence on the electrochemical performance is investigated in specially adapted cell models.
During the production of fiber-reinforced thermosets, the resin material undergoes a reaction that can lead to damage. A two-stage polymerization reaction is modeled using molecular dynamics and evaluations of the system including a fiber surface are performed. In addition, a phase-field model for crack propagation in heterogeneous systems is derived. This model is able to predict crack growth where established models fail. Finally, the model is used to predict crack formation during curing.
This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework. - This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework.
Water diffusing into silica surfaces gives rise for several effectson diffusion behaviour and mechanical properties. Water added to silica glass increases its specific volume so that the silica expands near the surface. Mechanical boundary conditions give rise for compressive “swelling stresses”. This fact provides a tool for the interpretation of many experimental observations from literature.
Water diffusing into silica surfaces gives rise for several effects on diffusion behaviour and mechanical properties. In a preceding booklet, we focused on diffusion and fiber strengths and deformations which were obtained by water soaking under external loading. In the present booklet we deal with results and interpretations of strength increase in the absence of applied stresses.
Recent studies of deformation mechanisms of metals and alloys pioneer the better investigation of the friction and wear behavior of materials with well-defined initial microstructures. Within this scope, in this work, the effect of sub-surface deformations on the resulting friction and wear behavior has been searched by means of a systematic experimental study on Au-Ni metallic multilayer model alloy system.
Most storage materials exhibit phase changes, which cause stresses and, thus, lead to damage of the electrode particles. In this work, a phase-field model for the cathode material NaxFePO4 of Na-ion batteries is studied to understand phase changes and stress evolution. Furthermore, we study the particle size and SOC dependent miscibility gap of the nanoscale insertion materials. Finally, we introduce the nonlocal species concentration theory, and show how the nonlocality influences the results.