Evaluation of hole quality in drilling CF/BMI composite via machine learning: Multi-defects analysis and fatigue life prediction

IF 6.6 1区 工程技术 Q1 ENGINEERING, CIVIL Thin-Walled Structures Pub Date : 2025-07-01 Epub Date: 2025-03-17 DOI:10.1016/j.tws.2025.113189
Shengguo Zhang , Wenhu Wang , Tianren Zhang , Yifeng Xiong , Bo Huang , Ruisong Jiang
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Abstract

CF/BMI (carbon fiber/bismaleimide) composite has emerged as one of the most promising of the high-performance carbon fiber-reinforced polymer (CFRP) composites owing to its excellent mechanical strength and high temperature resistance. Although drilling in CF/BMI composite is a common machining procedure, the BMI resin tends to become brittle after curing, which means that drilling holes can lead to serious defects such as delamination, tearing, and scratches on the hole-wall. In this paper, to predict fatigue life under multi-defects and calculate the sensitivity of defects to fatigue life, a machine learning model for comprehensively evaluating the quality of drilled holes was proposed. Firstly, the quantitative characterization methods of tearing, burr, and delamination in uniform dimensions were presented, and the hole-wall surfaces were sampled and characterized with three-dimensional surface roughness. The comparative effect of tool type, ultrasonic-vibration assisted drilling (UVAD), and drilling parameters on defects was analyzed. Through quasi-static tensile and fatigue tests, the effects of multi-defects on the mechanical properties of open-hole laminates were investigated. An ANN model was developed to predict the correlation between drilling-induced defects and fatigue life. The model was optimized by tuning parameters and hyperparameters, the accuracy error of the model was a MAPE value of 1.263 % and an R2 value of 0.913.

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基于机器学习的CF/BMI复合材料钻孔质量评价:多缺陷分析和疲劳寿命预测
CF/BMI(碳纤维/双马来酰亚胺)复合材料因其优异的机械强度和耐高温性能而成为高性能碳纤维增强聚合物(CFRP)复合材料中最有前途的一种。虽然在CF/BMI复合材料中钻孔是一种常见的加工工艺,但BMI树脂在固化后容易变脆,这意味着钻孔会导致孔壁出现分层、撕裂和划伤等严重缺陷。为了预测多缺陷下的疲劳寿命,计算缺陷对疲劳寿命的敏感性,提出了一种综合评价钻孔质量的机器学习模型。首先,提出了均匀尺寸的撕裂、毛刺和分层的定量表征方法,并对孔壁表面进行了三维表面粗糙度表征。对比分析了刀具类型、超声振动辅助钻孔(UVAD)和钻孔参数对缺陷的影响。通过准静态拉伸和疲劳试验,研究了多缺陷对开孔层压板力学性能的影响。建立了一种人工神经网络模型来预测钻井缺陷与疲劳寿命之间的关系。通过参数和超参数对模型进行优化,模型的精度误差MAPE值为1.263%,R2值为0.913。
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来源期刊
Thin-Walled Structures
Thin-Walled Structures 工程技术-工程:土木
CiteScore
9.60
自引率
20.30%
发文量
801
审稿时长
66 days
期刊介绍: Thin-walled structures comprises an important and growing proportion of engineering construction with areas of application becoming increasingly diverse, ranging from aircraft, bridges, ships and oil rigs to storage vessels, industrial buildings and warehouses. Many factors, including cost and weight economy, new materials and processes and the growth of powerful methods of analysis have contributed to this growth, and led to the need for a journal which concentrates specifically on structures in which problems arise due to the thinness of the walls. This field includes cold– formed sections, plate and shell structures, reinforced plastics structures and aluminium structures, and is of importance in many branches of engineering. The primary criterion for consideration of papers in Thin–Walled Structures is that they must be concerned with thin–walled structures or the basic problems inherent in thin–walled structures. Provided this criterion is satisfied no restriction is placed on the type of construction, material or field of application. Papers on theory, experiment, design, etc., are published and it is expected that many papers will contain aspects of all three.
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