Macrosegregation in direct-chill casting of aluminium alloys

This is the post-print version of the final paper published in Progress in Materials Science. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.Semi-continuous direct-chill (DC) casting holds a prominent position in commercial aluminium alloy processing, especially in production of large sized ingots. Macrosegregation, which is the non-uniform chemical composition over the length scale of a casting, is one of the major defects that occur during this process. The fact that macrosegregation is essentially unaffected by subsequent heat treatment (hence constitutes an irreversible defect) leaves us with little choice but to control it during the casting stage. Despite over a century of research in the phenomenon of macrosegregation in castings and good understanding of underlying mechanisms, the contributions of these mechanisms in the overall macrosegregation picture; and interplay between these mechanisms and the structure formation during solidification are still unclear. This review attempts to fill this gap based on the published data and own results. The following features make this review unique: results of computer simulations are used in order to separate the effects of different macrosegregation mechanisms. The issue of grain refining is specifically discussed in relation to macrosegregation. This report is structured as follows. Macrosegregation as a phenomenon is defined in the Introduction. In “Direct-chill casting – process parameters, solidification and structure patterns” section, direct-chill casting, the role of process parameters and the evolution of structural features in the as-cast billets are described. In “Macrosegregation in direct-chill casting of aluminium alloys” section, macrosegregation mechanisms are elucidated in a historical perspective and the correlation with DC casting process parameters and structural features are made. The issue of how to control macrosegregation in direct-chill casting is also dealt with in the same section. In “Role of grain refining” section, the effect of grain refining on macrosegregation is introduced, the current understanding is described and the contentious issues are outlined. The review is finished with conclusion remarks and outline for the future research.The Netherlands Institute for Metals Researc

Phase separation dynamics in colloid-polymer mixtures: the effect of interaction range

Colloid-polymer mixtures may undergo either fluid-fluid phase separation or gelation. This depends on the depth of the quench (polymer concentration) and polymer-colloid size ratio. We present a real-space study of dynamics in phase separating colloid-polymer mixtures with medium- to long-range attractions (polymer-colloid size ratio q_R=0.45-0.89, with the aim of understanding the mechanism of gelation as the range of the attraction is changed. In contrast to previous studies of short-range attractive systems, where gelation occurs shortly after crossing the equilibrium phase boundary, we find a substantial region of fluid-fluid phase separation. On deeper quenches the system undergoes a continuous crossover to gel formation. We identify two regimes, `classical' phase separation, where single particle relaxation is faster than the dynamics of phase separation, and `viscoelastic' phase separation, where demixing is slowed down appreciably due to slow dynamics in the colloid-rich phase. Particles at the surface of the strands of the network exhibit significantly greater mobility than those buried inside the gel strand which presents a method for coarsening.Comment: 8 page

Spectrally approximating large graphs with smaller graphs

How does coarsening affect the spectrum of a general graph? We provide conditions such that the principal eigenvalues and eigenspaces of a coarsened and original graph Laplacian matrices are close. The achieved approximation is shown to depend on standard graph-theoretic properties, such as the degree and eigenvalue distributions, as well as on the ratio between the coarsened and actual graph sizes. Our results carry implications for learning methods that utilize coarsening. For the particular case of spectral clustering, they imply that coarse eigenvectors can be used to derive good quality assignments even without refinement---this phenomenon was previously observed, but lacked formal justification.Comment: 22 pages, 10 figure