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  • 2000-2004  (1)
  • 1995-1999  (1)
  • Forming  (1)
  • Framework zirconium phosphates  (1)
  • 1
    Electronic Resource
    Electronic Resource
    The microstructure and properties of framework zirconium phosphates based nanocomposites – catalysts of alkane isomerization (2000)
    Springer
    Materials research innovations 3 (2000), S. 276-285 
    Details
    ISSN: 1433-075X
    Keywords: Key words Nanocomposites ; Framework zirconium phosphates ; Supported nanoparticles of WO3 ; MoO3 and Pt ; Catalysis of pentane and hexane isomerization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract  Nanocomposites based upon framework zirconium phosphates with supported WO3, MoO3 and Pt nanoparticles were synthesized via the incipient wetness impregnation of high-surface-area mesoporous phosphate samples with water solutions of corresponding salts followed by drying and calcination. The structure and surface properties of nanocomposites were studied by using combination of structural and spectral methods. Due to a strong interaction between supports and supported species, the structure of the latter differs considerably from that of the bulk phases. Surface acid centers typical for zirconium phosphates disappear suggesting their participation in bonding nanoparticles of promoters. Instead, new types of strong acid sites associated with tungsten oxide clusters emerge. The effect of these promoters on performance of zirconium phosphates in the reaction of pentane and hexane isomerization is considered.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s100190000051
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Assessment of sheet-metal bending requirements using neural networks (1996)
    Springer
    Neural computing & applications 4 (1996), S. 35-43 
    Details
    ISSN: 1433-3058
    Keywords: Automation ; Bending ; Forming ; Modelling ; Neural networks ; Sheet-metal ; Springback system
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The springback behaviour of a sheet-metal is dependent on the properties of the metal and the bending conditions, namely the thickness of the sheet-metal, geometry of the tooling and the amount of force used for bending. Sheet-metal component manufacturing often requires near zero springback angle to obtain the correct shape of the product. An attempt has been made to model the non-linear relation between properties of the metal, the springback angle, geometry of the tooling and the bending force applied. Multilayer perceptron neural networks with a backpropagation learning algorithm were used to model the bending process. One set of data from bending experiments in a laboratory environment was used to train the networks. The networks were tested with the remaining set of experimental results. Then, the neural networks were used to predict the forces required for a number of bending experiments to achieve a zero springback angle. Validation of the neural network predictions was performed by trying to apply the predicted amounts of bending force in the physical experiments. The springback angles achieved were within ±1 degree, which is an acceptable range for the work. The research clearly demonstrates the applicability of neural networks to modelling the sheet-metal bending process.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01413868
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    Paper (German National Licenses)
    Fulltext
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