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Characterization of the bulk flow properties of industrial powders from shear tests

Characterization of the bulk flow properties of industrial powders from shear tests

Macri, Domenico, Chirone, Roberto, Salehi Kahrizsangi, Hamid ORCID: 0000-0002-2516-6619, Sofia, Daniele, Materazzi, Massimiliano, Barletta, Diego, Lettieri, Paola and Poletto, Massimo (2020) Characterization of the bulk flow properties of industrial powders from shear tests. Processes, 8 (5):540. ISSN 2227-9717 (doi:https://doi.org/10.3390/pr8050540)

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Abstract

Bulk flow properties from shear analysis of compacted powders can be evaluated following different approaches. Experimental values of shear stresses obtained by conventional shear cells are traditionally used to build yield loci, from which the most relevant flow properties could be found. Such flow properties play an important role in determining their performance under fluidization conditions. In this work, a useful app, named cYield, was developed by using the new Matlab's App Developer environment. This tool enables users to calculate both linear (Coulomb) and non-linear (Warren-Spring) yield loci as the best fitting of the σ-τ experimental shear points. It also provides a wide range of statistical information related to the quality of the outcomes obtained. The different features of the tool are presented, and the crucial steps for the execution of its calculations are illustrated. Moreover, it has been applied for the yield loci analysis of four different materials traditionally used in manufacturing processes. The results confirm that the flow behavior of many industrial powders, especially if cohesive, is better described by a non-linear yield locus.

Item Type: Article
Uncontrolled Keywords: Powder flow, powder characterization, flowability, Matlab App, yield locus, tensile strength.
Subjects: T Technology > T Technology (General)
T Technology > TP Chemical technology
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 15 Jul 2021 08:49
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/31613

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