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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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