Deformation dilatometry has been used to simulate controlled hot rolling followed by controlled cooling of a group of low-and ultralow-carbon microalloyed steels containing additions of boron and/or molybdenum to enhance hardenability. Each alloy was subjected to simulated recrystallization and nonrecrystallization rolling schedules, followed by controlled cooling at rates from 0.1 ЊC/s to about 100 ЊC/s, and the corresponding continuous-cooling-transformation (CCT) diagrams were constructed. The resultant microstructures ranged from polygonal ferrite (PF) for combinations of slow cooling rates and low alloying element contents, through to bainitic ferrite accompanied by martensite for fast cooling rates and high concentrations of alloying elements. Combined additions of boron and molybdenum were found to be most effective in increasing steel hardenability, while boron was significantly more effective than molybdenum as a single addition, especially at the ultralow carbon content. Severe plastic deformation of the parent austenite (Ͼ0.45) markedly enhanced PF formation in those steels in which this microstructural constituent was formed, indicating a significant effective decrease in their hardenability. In contrast, in those steels in which only nonequilibrium ferrite microstructures were formed, the decreases in hardenability were relatively small, reflecting the lack of sensitivity to strain in the austenite of those microstructural constituents forming in the absence of PF.
, as part of the special issue ''Nanostructured Materials-Processing, structures, properties and applications''. An older, uncorrected proof version of the same article was republished in error in Volume 42, Issue 21, pages 9097-9111,
(Ti-Low. Ti-High) were microalloyed wlth two levels of titanium while the base composition was kept constant and the total nitrogen level maintained at I 10 ppm.The steel cornpositions are shown in Table 1
Embedded components can be found in structures such as multi-material additively manufactured structures and sensors embedded for health monitoring. The embedded components often have different materials from the base structure. Variation of Young’s modulus from the base structure to the embedded components can lead to variation in stress values. Variation of material strengths leads to the load bearing capacity of the component being different from the base structure. The location of embedded components has a significant influence on the stress in these components. An appropriate choice of component location could significantly reduce its stress. In this paper, a location optimization method is developed to reduce the stress in components embedded inside the structures. The sensitivity of maximum von Mises stress around the component with respect to its placement is calculated through the adjoint method. A gradient-based solver is applied to solve the optimization problem. A cantilever beam with one component was used to demonstrate the feasibility of the method. The method was applied to two case studies and significant reduction in the maximum von Mises stress of component was achieved.
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