Abstract
Dimensional error is one of the most important product quality characteristics during slender bar turning operations. In this study, artificial neural network was employed to investigate the dimensional errors during slender bar turning process. A systematic method based on neural network modeling technique and statistical tool was designed to select the input parameters of the monitoring model. The average effect of each candidate machining factor and sensed information on the modeling performance was determined. Then, the monitoring system was developed to perform the in-process prediction of dimensional errors. Experimental results showed that the proposed system had the ability to monitor efficiently dimensional errors within the range that it had been trained.
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Han, R., Cui, B., Guo, J. (2007). In-Process Monitoring of Dimensional Errors in Turning Slender Bar Using Artificial Neural Networks. In: Shen, W., Luo, J., Lin, Z., Barthès, JP.A., Hao, Q. (eds) Computer Supported Cooperative Work in Design III. CSCWD 2006. Lecture Notes in Computer Science, vol 4402. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72863-4_29
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DOI: https://doi.org/10.1007/978-3-540-72863-4_29
Publisher Name: Springer, Berlin, Heidelberg
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