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Microstructural banding of directed energy deposition-additively manufactured 316L stainless steel

Journal of Materials Science & Technology 2021年第10期69卷 96-105页

作者： Yoon Hwa Christopher S.kumai Thomas M.Devine Nancy Yang joshua k.yee Ryan Hardwick kai Burgmann Department of Materials Science and Engineering University of CaliforniaBerkeleyCA94720USA Sandia National Laboratory LivermoreCA.94551USA

The microstructures of 316L stainless steel created by rapid solidification are investigated by comparing the similar microstructures of individual hatches of directed energy deposition additive manufacturing(DED-AM)and those of single,laser surface-melted tracks formed on a solid *** recoil pressure,which is exponentially dependent on the laser beam power density,induces convection of the melt pool,which causes formation of microstructural bands in the as-solidified *** microstructural bands are associated with changes in the chromium concentration and are a significant component of the inhomogeneous microstructure of DED-AM.

关键词： Additive manufacturing Directed energy deposition Stainless steel Rapid solidification Recoil pressure Microstructure

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Inertial Entropy and External Validation of Clusterings

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Journal of Harbin Institute of Technology(New series) 2024年

作者： Dan Simovici joshua yee Computer Science Deptartment University of Massachusetts Boston

Axiomatization of Shannon entropy is a subject that has received lots of attention in the information theory literature. While Shannon entropy is defined on probability distribution we define a new type of entropy on the set of partitions of finite subsets of metric spaces， which has a rich algebraic structure as a partially ordered set. We propose an axiomatization of an entropy-like measure of partitions of sets of objects located in metric spaces， and we derive an analytic expression of this new type of entropy referred to as inertial entropy. This approach starts with the notion of inertia of a partition and includes a study of the behavior of the sum of square errors of a partition. In this context， we characterize the chain of partitions produced by the Ward hierarchical clustering *** from inertial entropies of partitions， we introduce conditional entropies which， in turn， generate metrics on partitions of finite sets. These metrics are used as external validation tools for clusterings of labeled data *** metric generated by inertial entropy can be used to validate data clustering for labeled data sets. This type of validation aims to determine to what extend labeling of the data coincides with the clustering obtained algorithmically， and we obtain a high degree of consistency of the data labeling with the results of several hierarchical clusterings.

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