FREE ABSTRACT (to read the full article, please log in below).
Selective Laser Sintering (SLS) is a new and one of the most important
rapid prototyping techniques used in precision engineering industry. How
to select optimal SLS parameters for building a desired SLS part is still
a research and practical problem. The paper describes a development of
a relational database (SLS-Database) and a neural network learning
system (SLS-Learning System) for SLS parameter optimization. SLS-Database
is used to store production data. The data stored not only provide production
experience that will help the operator to set up proper parameters for
prototyping applications, but also provide training data for the SLS-Learning
System to learn the process. The trained neural networks can predict
the output of the SLS prototyping application and based on the prediction,
optimal selection of input parameters can be determined for building the
desired SLS part. The paper also discusses effectiveness and further improvement
of the SLS parameter optimization system.
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© Copyright 2001 Professor F.R. Hall - University of Wolverhampton.