针对真空感应炉生产过程中温度测量成本较高及精度较差等不足，建立了基于RBF神经网络的真空感应炉终点钢水温度预报模型。
A prediction model of molten steel temperature based on RBF neural network was developed to reduce cost and improve temperature control accuracy for vacuum induction melting.
该过程模型还考虑了应用系统开发时效和软件开发团队运营成本问题，此阶段软件复用不强调领域工程，应用系统开发是面向提交的，但需要使用基于构件的软件开发。
And the model considered the time efficient and operation cost of the applications, it didn't emphasize the domain engineering, the application development is for submission, but it need use CBSD.
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