Design of Experiments (ODE) and Artificial Neural Networks (ANN) are suitable for studies of complex systems. In DOE, experiments are properly distributed within the factor space in order to minimize the number of experiments required to obtain a statistically valid functional relationship between a response and factors. ANN is a computer model inspired by the neural network structure of the brain. Although these methods are based on mathematical theories, their use do not require advanced mathematical skills as efficient PCs and software tools are available. Major advantages of these experimental approaches over traditional ones, such as the onefactor-at-a-time method (OFATl, include the possibility to reveal interactions between factors and determine their setting for a desired response. Despite these advantages, their use is still limited, probably due to lack of familiarity. This report will give a short introduction to these methods and their use in traditional ceramics.
|Anno di pubblicazione:||2009|
|Titolo:||Advantages in using Design of Experiment and Artificial Neural Networks in the study and optimisation of ceramic systems|
|Appare nelle tipologie:||Articolo su rivista|
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