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13 January 2005 Synthetic evaluation and neural-network prediction of laser cutting quality
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Abstract
Evaluation of the cut quality is extremely significant to industrial applications of laser cutting. The relationship between cut quality and processing conditions has been investigated by using one of the measurable cut qualities, such as kerf width, striations, dross, roughness and so on. However, each of these qualities can only partially represent the cut quality. In this paper, a synthetic evaluation method for laser cutting quality has been proposed. A 3KW CO2laser was used to perform cutting experiments with 1.0mm thick mild steel sheets. The cut quality indicators, including kerf width, striations, dross, roughness, under different cutting conditions have been studied. A Synthetic Quality Number (SQN) has been presented as the evaluation indicator by quantitatively analyzing the conventional indicators. A neural network based method to anticipate laser cutting quality has been presented with SQN as the evaluation indicator.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yongqiang Zhang, Wuzhu Chen, Xudong Zhang, Yanhua Wu, and Qi Yan "Synthetic evaluation and neural-network prediction of laser cutting quality", Proc. SPIE 5629, Lasers in Material Processing and Manufacturing II, (13 January 2005); https://doi.org/10.1117/12.575008
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