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

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Thin-Walled Structures Pub Date : 2025-03-17 DOI:10.1016/j.tws.2025.113189

Shengguo Zhang , Wenhu Wang , Tianren Zhang , Yifeng Xiong , Bo Huang , Ruisong Jiang

{"title":"Evaluation of hole quality in drilling CF/BMI composite via machine learning: Multi-defects analysis and fatigue life prediction","authors":"Shengguo Zhang , Wenhu Wang , Tianren Zhang , Yifeng Xiong , Bo Huang , Ruisong Jiang","doi":"10.1016/j.tws.2025.113189","DOIUrl":null,"url":null,"abstract":"<div><div>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 <em>MAPE</em> value of 1.263 % and an <em>R</em><sup>2</sup> value of 0.913.</div></div>","PeriodicalId":49435,"journal":{"name":"Thin-Walled Structures","volume":"212 ","pages":"Article 113189"},"PeriodicalIF":5.7000,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Thin-Walled Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263823125002836","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}

引用次数: 0

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.

Abstract Image

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

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.