中国激光, 2016, 43 (11): 1102008, 网络出版: 2016-11-10
不锈钢激光着色机理及基于神经网络的颜色预测
Mechanism of Laser Coloration of Stainless Steel and Color Prediction Based on Neural Network
摘要
研究了离焦量、脉冲能量、扫描间距、扫描速度和重复频率等激光加工参数对金属表面着色及微纳结构制备的影响机理,诱导制备了氧化膜、类光栅、凹坑和柱状突起4种结构,这些结构会使不锈钢表面产生薄膜干涉、光栅衍射和陷光等现象。通过Matlab软件在工艺参数与颜色HSB值之间建立了一个单隐含层的反向传播(BP)神经网络,该神经网络的训练均方根误差为0.0078,色相H、饱和度S和亮度B的测试相对误差分别为23%,10.4%和5.6%。该神经网络在一定程度上揭示了工艺参数与颜色之间的映射关系,使用该神经网络模型可以对激光着色效果作出有效的预测。
Abstract
To investigate the mechanism of laser coloring and fabrication of micro- and nano-structures fabrication on stainless steel, the influence of such laser parameters as defocusing distance, pulse energy, scanning interval, scanning speed, and repetition rate is studied. The oxide film, grating-like structure, concave and columnar protrusion are produced. The four structures lead to thin-film interference, grating diffraction effect and light trapping effect. A BP (back propagation) neural network with one hidden layer between process parameters and color parameters is established via Matlab. The training root-mean-square error of this BP neural network is 0.0078. The relative errors of hue, saturation and brightness are 23%, 10.4%, 5.6%, respectively. To a certain extent, this neural network reveals the mapping relationship between process parameters and color. The laser coloring effect can be predicted effectively with the neural network model.
郭亮, 林远添, 张震华, 张庆茂. 不锈钢激光着色机理及基于神经网络的颜色预测[J]. 中国激光, 2016, 43(11): 1102008. Guo Liang, Lin Yuantian, Zhang Zhenhua, Zhang Qingmao. Mechanism of Laser Coloration of Stainless Steel and Color Prediction Based on Neural Network[J]. Chinese Journal of Lasers, 2016, 43(11): 1102008.