Abstract
In some cases, in order to avoid interference during 3D laser cutting of thin metal a laser head could not be kept vertical to the surface of a work piece. In such situations, the cutting quality depends not only on “typical” cutting parameters but also on the slant angle of the laser head. Traditionally, many tests had to be done in order to obtain the best cutting results. In this paper, an experimental design is employed to reduce the number of tests and an artificial neural network (ANN) is set up to describe quantitatively the relationship between cutting quality and cutting parameters in the non-vertical laser cutting situation. A quality point system is used to evaluate the cutting result of the thin sheet quantitatively. Testing of this novel method shows that the calculated “quality point” using ANN is quite closely in accord with the actual cutting result. The ANN is very successful for optimizing parameters, predicting cutting results and deducing new cutting information.
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Acknowledgements
This work was supported by the Beijing Education Ministry under Grants KM200510005013 and the Chinese Nature and Science Foundation (No. 50575005). Editing assistance was provided by American professors Edmund F. Perozzi, and Rhoda E. Perozzi, who are currently teaching at Beijing University of Technology.
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Jimin, C., Jianhua, Y., Shuai, Z. et al. Parameter optimization of non-vertical laser cutting. Int J Adv Manuf Technol 33, 469–473 (2007). https://doi.org/10.1007/s00170-006-0489-3
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DOI: https://doi.org/10.1007/s00170-006-0489-3