Process Research of Aviation Aluminum Alloy Laser Cutting Based on Artificial Neural Network

LIU Yuanyang; YANG Hongyan; ZHANG Zenghuan

doi:10.16080/j.issn1671-833x.2023.07.113

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Process Research of Aviation Aluminum Alloy Laser Cutting Based on Artificial Neural Network

更新时间：2025-10-30

- Process Research of Aviation Aluminum Alloy Laser Cutting Based on Artificial Neural Network
- Aeronautical Manufacturing Technology Vol. 66, Issue 7, Pages: 113-117(2023)
- 作者机构：
  
  中国商飞上海飞机制造有限公司,上海,201324
- 作者简介：
- 基金信息：
- DOI：10.16080/j.issn1671-833x.2023.07.113
  CLC：
- Published：2023
- 稿件说明：
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刘远洋，杨红艳，张增焕. 基于人工神经网络的航空铝合金激光切割工艺研究[J]. 航空制造技术, 2023, 66(7): 113-117.

LIU Yuanyang, YANG Hongyan, ZHANG Zenghuan. Process Research of Aviation Aluminum Alloy Laser Cutting Based on Artificial Neural Network[J]. Aeronautical Manufacturing Technology, 2023, 66(7): 113-117.
刘远洋，杨红艳，张增焕. 基于人工神经网络的航空铝合金激光切割工艺研究[J]. 航空制造技术, 2023, 66(7): 113-117. DOI： 10.16080/j.issn1671-833x.2023.07.113.

LIU Yuanyang, YANG Hongyan, ZHANG Zenghuan. Process Research of Aviation Aluminum Alloy Laser Cutting Based on Artificial Neural Network[J]. Aeronautical Manufacturing Technology, 2023, 66(7): 113-117. DOI： 10.16080/j.issn1671-833x.2023.07.113.

摘要

为了推动铝合金激光切割工艺在航空领域的应用，进而对航空铝合金激光切割工艺进行研究。采用正交试验获取了工艺参数与质量特性的数据样本；再利用Python 语言建立一个包含双隐藏层的人工神经网络模型对样本进行学习，通过该模型探究了工艺参数同质量特性之间的影响关联并进行预测分析。结果表明，得到的各质量特性预测的平均误差都能控制在10% 以内。通过该模型优化获得试验件的尺寸偏差、粗糙度和热影响区宽度的实际测量值和预测值误差分别为8.5%、6.4% 和8.2%。

Abstract

In order to promote the application of laser cutting in the field of aviation

process research of the aviation aluminum alloy laser cutting has been carried out. Data samples of the process parameters and the quality characteristics were obtained by orthogonal test. An artificial neural network model was built by Python language to study the data samples

which has double hidden layer. Based on this model

the correlation between the process parameters and the quality characteristics were investigated and predicted. The result reveals that the predicted average error of the quality characteristics can be controlled in 10%. The size deviation

roughness and heat affected zone width of the test specimens optimized by the model were measured

and the errors between the actual measured value and the predicted value are 8.5%

6.4% and 8.2% respectively.

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