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  • Articles: DFG German National Licenses  (2)
  • 2005-2009
  • 1995-1999  (2)
  • 1945-1949
  • Automation  (1)
  • Hydrothermal  (1)
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  • Articles: DFG German National Licenses  (2)
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  • 2005-2009
  • 1995-1999  (2)
  • 1945-1949
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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Materials research innovations 1 (1997), S. 44-52 
    ISSN: 1433-075X
    Keywords: Key words Microwave ; Synthesis ; Processing ; WC ; BaTiO3 ; Hydrothermal
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract  The innovations in microwave processing of ceramics have been dominated to date by serendipitous discovery, because the interaction between such radiation as delivered via available tools and the materials of widely varying properties, sizes, and shapes is so complex that it has defied quantitative analysis. For over 10 years a wide variety of inorganic ceramic and semiconducting materials have been synthesized, sintered, and reacted in our own labs, including microwave hydrothermal synthesis of metals, ferrites, and electroceramic phases. These local results are summarized and used as the reference point for reporting on two different new advances: sintering of WC-Co composite tool bits and other similar objects in under 15 min, while retaining extremely fine microstructures, without any grain growth inhibitors; using reduced TiO2 or Ta2O5 for the synthesis of phases such as BaTiO3, Ba3MgTa2O9, and Pb(Zr.Ti)O3 in a few minutes in a 2.45 GHz field at the astonishing temperatures of 300–700 ºC.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Neural computing & applications 4 (1996), S. 35-43 
    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
    Library Location Call Number Volume/Issue/Year Availability
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