Study of the surface qualities of laser shock-processing zones using an artificial neural network
-
摘要: 大量的实验表明,经激光冲击处理后,材料受冲击区的表面质量与材料的疲劳寿命有着明显的关系。因此,表面质量是判断激光冲击强化效果的重要手段。将人工神经网络技术用于激光冲击处理后试件的表面质量分析,建立了激光参数与激光冲击处理后试件的表面质量之间的联系,并用其实现了对冲击处理后的试件表面质量的预测。研究及实验表明,该方法不仅具有准确及稳定性好等特点,而且这种预测能力在实际应用中还具有不断提高的智能特性。Abstract: A lot of experiments have shown that there is an obvious relation between surface qualities of specimen after laser shock-processing(LSP) and its fatigue life.Consequently,the LSP effects can be evaluated by surface qualities in LSP areas.In this paper,an artificial neural network(ANN) is utilized to study the surface qualities of specimen after LSP.Based on the data obtained in the experiment,an ANN is established.The trained ANN could acquire the relations between surface qualities and laser parmeters.From the verification of aluminium alloy 2024-T62,it is proved that the neural network can successfully predict the surface quality grades of specimen after LSP,and easily determine the laser parameters under different production conditions.The research and experimental results show that the ANN has not only the accuracy and good stability,but also the intelligent improving control ability during process.
-
Keywords:
- laser shock-processing(LSP) /
- surface qualities /
- neural network
-
-
[1] Fairand B P,Wilcox B A,Gallagher W J et al.JAP,1972; 43(9)
[2] 张永康,张淑仪,吴鸿兴et al.中国科学(E辑),1997;27(1):28～34 [3] 张永康.激光冲击强化提高航空材料疲劳寿命的研究.南京航空航天大学博士学位论文,1995 [4] 张永康.中国激光,1997;A24:467～471 [5] 张宏.抗疲劳断裂激光冲击处理技术的研究.南京航空航天大学博士学位论文,1997 [6] Fairand B P,Clauer A H.SPIE,1976;86
[7] Zhang Y K,Zhang Sh Y,Yang J Ch et al.Surface and Coatings Technology,1997;92:104～109
[8] 张际先,宓霞.神经网络及其在工程中的应用.北京:机械工业出版社,1996 -
期刊类型引用(3)
1. 王铭，韩越，车东博，王挺峰. 连续激光与复合激光辐照钢靶的相变烧蚀分析. 激光与光电子学进展. 2022(11): 308-315 . 
百度学术
2. 孙华强，郇浩，齐萌，李庆坚. 基于全因子实验的激光打孔参量与吸阻关系分析. 激光技术. 2018(06): 790-795 . 本站查看
3. 王琪琪，任乃飞，任旭东. GH4037镍基高温合金激光打孔相变过程数值模拟. 激光技术. 2018(06): 764-768 . 本站查看
其他类型引用(4)
计量
- 文章访问数: 3
- HTML全文浏览量: 0
- PDF下载量: 16
- 被引次数: 7
下载: