基于机器学习的复材装配制孔微损伤监测方法研究
Research on Monitoring Method of Micro-damage in Composite Assembly Drilling Holes based on Machine Learning
乐洪博 1王宇宁 2谢大叶2
作者信息
- 1. 空装驻沈阳局驻沈阳地区第一军事代表室,辽宁 沈阳 110850
- 2. 航空工业沈阳飞机工业(集团)有限公司 工程技术中心,辽宁 沈阳 110850
- 折叠
摘要
研究旨在开发一种基于机器学习的复合材料装配制孔微损伤监测方法,针对碳纤维增强复合材料(CFRP)在制孔加工过程中容易产生的分层损伤等问题,通过采集力学信号、声发射信号和温度信号方式,利用机器学习模型实现对碳纤维增强复合材料(CFRP)制孔微损伤的实时监控.研究内容包括理论研究、实验研究、信号处理、模型构建及监测系统设计等,从而形成一套基于工艺参数及刀具角度耦合约束下的复材制孔微损伤控制方法,并达到一定的技术指标,为后续研究指明了方向.
Abstract
Aimed to develop a composite material assembly hole micro-damage monitoring method based on machine learning.In view of the problems of delamination damages which were easily caused during the process of drilling holes in carbon fiber reinforced composite materials(CFRP),it was collected that mechanical signals,acoustic emission signals,and temperature signals,and machine learning models were used to achieve real-time monitoring of micro-damage in the drilling process of carbon fiber reinforced composite materials(CFRP).The research contents included theoretical research,experi-mental research,signal processing,model construction,monitoring system design and so on,so as to form a set of compos-ite material micro-damage control methods based on process parameters and tool angle coupling constraints.It achieved cer-tain technical indicators,and pointed out the direction for future research.
关键词
碳纤维增强复合材料(CFRP)/制孔微损伤监测/机器学习/损伤抑制方法/神经网络/遗传算法Key words
carbon fiber reinforced composite materials(CFRP)/micro-damage monitoring during drilling holes/ma-chine learning/damage suppression methods/neural network/genetic algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据