Laser & Optoelectronics Progress, Volume. 54, Issue 1, 11407(2017)
Roughness Prediction of Laser Drilling Pore Surface Based on Back-Propagation Artificial Neural Networks
The tests of laser drilling of 304 stainless steel specimens is conducted, the surface roughness parameter is obtained by the profilometer, and based on the back-propagation artificial neural network, the neural network prediction model based on the relationship between the three process parameters of laser power, pulse frequency, and defocusing amount, and the microporous surface roughness is established. After lots of network trainings with enough test data, it is confirmed that this artificial neural network model possesses a high prediction precision, the predication error is controlled around 6%, and the maximum error is less than 8.08%. This model can precisely predict the surface roughness of laser drilling pore surface, and effectively shorten the preparation period for laser drilling operations.
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Ding Hua, Li Yanwei, Yuan Dongqing. Roughness Prediction of Laser Drilling Pore Surface Based on Back-Propagation Artificial Neural Networks[J]. Laser & Optoelectronics Progress, 2017, 54(1): 11407
Category: Lasers and Laser Optics
Received: Sep. 21, 2016
Accepted: --
Published Online: Jan. 17, 2017
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