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Chem-prep PZT 95/5 for neutron generator applicatios : powder preparation characterization utilizing design of experiments.
Author(s) -
Steven Lockwood,
EMILY D. RODMAN-GONZALES,
James A. Voigt,
Diana L. Moore
Publication year - 2003
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/917119
Subject(s) - materials science , design of experiments , factorial experiment , process engineering , computer science , engineering , statistics , mathematics , machine learning
Niobium doped PZT 95/5 (lead zirconate-lead titanate) is the material used in voltage bars for all ferroelectric neutron generator power supplies. In June of 1999, the transfer and scale-up of the Sandia Process from Department 1846 to Department 14192 was initiated. The laboratory-scale process of 1.6 kg has been successfully scaled to a production batch quantity of 10 kg. This report documents efforts to characterize and optimize the production-scale process utilizing Design of Experiments methodology. Of the 34 factors identified in the powder preparation sub-process, 11 were initially selected for the screening design. Additional experiments and safety analysis subsequently reduced the screening design to six factors. Three of the six factors (Milling Time, Media Size, and Pyrolysis Air Flow) were identified as statistically significant for one or more responses and were further investigated through a full factorial interaction design. Analysis of the interaction design resulted in developing models for Powder Bulk Density, Powder Tap Density, and +20 Mesh Fraction. Subsequent batches validated the models. The initial baseline powder preparation conditions were modified, resulting in improved powder yield by significantly reducing the +20 mesh waste fraction. Response variation analysis indicated additional investigation of the powder preparation sub-process steps was necessary to identify and reduce the sources of variation to further optimize the process

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