Chapter 60. Modelling of the Sintering Behaviour of Al2O3 with a Neural Network

  1. J. P. Singh
  1. H. Hofmann

Published Online: 26 MAR 2008

DOI: 10.1002/9780470294444.ch60

Proceedings of the 21st Annual Conference on Composites, Advanced Ceramics, Materials, and Structures - B: Ceramic Engineering and Science Proceedings, Volume 18, Issue 4

Proceedings of the 21st Annual Conference on Composites, Advanced Ceramics, Materials, and Structures - B: Ceramic Engineering and Science Proceedings, Volume 18, Issue 4

How to Cite

Hofmann, H. (1997) Modelling of the Sintering Behaviour of Al2O3 with a Neural Network, in Proceedings of the 21st Annual Conference on Composites, Advanced Ceramics, Materials, and Structures - B: Ceramic Engineering and Science Proceedings, Volume 18, Issue 4 (ed J. P. Singh), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470294444.ch60

Author Information

  1. Powder Technology Laboratory, DMX-LTP, Swiss Federal Institute of Technology, CH-1015 Lausanne, Switzerland

Publication History

  1. Published Online: 26 MAR 2008
  2. Published Print: 1 JAN 1997

ISBN Information

Print ISBN: 9780470375532

Online ISBN: 9780470294444

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Keywords:

  • microstructure;
  • densification;
  • sintering theories;
  • characteristics;
  • sintering equations

Summary

The influence of the properties of commercial alumina powder on the sintering behaviour as well as on the final microstructure (e.g. grain size, porosity) is still not understood in detail. Neither the well-known sintering equation for the first sintering step nor the relation between the density and grain size at the final sintering step can describe the densification behaviour of commercial alumina powder. Therefore the aim of this work is to develop a better understanding of the relation between the powder properties and the densification behaviour of commercial alumina powder. Typical properties of the powders, such as particle size distribution, specific surface area, agglomerate factor and chemical composition were determined and their influence on the sintered density and the microstructure was investigated. Since none of the known sintering theories could describe the whole range of properties of the commercial powders, the ‘neural network’ method was used for the description of the relationship between the powder characteristics and the microstructure of the sintered samples. Based on the result from the neural network approach, experimental studies as well as theoretical sintering studies we have developed a model which describe the sintering behaviour of commercial alumina powders.