Abstract
Seamless tubing is the most commonly used process for high quality pipe products due to its ability to provide material consistency. However, the yield is hindered by complex workmanship and manufacturing operations, with the resulting risk of flaws, low productivity, and high manufacturing costs. This paper describes a systematic procedure to implement a data-driven process modeling technology in a seamless tubing factory, using neural networks to model relationships between controlled and uncontrolled process parameters and the yield. The issues of data collection/analysis and dimensionality reduction are discussed. It was found that model predictive ability improves when only important variables are included in the model. A software tool is developed, which offers such functions as response surface visualization and yield prediction.
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