航空用复合材料制孔缺陷检测技术研究进展

Research progress of hole-making defect detection technology for aerospace composite materials

摘要

碳纤维复合材料（carbon fiber reinforced polymer，CFRP）因其轻质高强特性在航空航天领域具有重要应用价值，但其制孔过程中因材料各向异性易引发分层、毛刺等缺陷，影响构件的服役性能与装配质量。因此，需要对其进行高精高效检测。本文基于制孔缺陷机理分析，深入探讨无损检测技术在 CFRP 制孔缺陷检测领域的适用性，分析传统机器视觉检测与基于深度学习检测的方法特性，并指出多模态数据融合的智能检测技术在提升检测精度与效率方面具有显著优势。此外针对当前 CFRP 制孔缺陷检测技术面临的挑战，提出开发各向异性自适应检测算法、构建标准化的缺陷分级体系及实现制孔过程的在线监测等发展路径，旨在为航空复合材料制造领域的智能化、高精度化检测提供革新的理论支撑与技术方向。

Abstract

Carbon fiber reinforced polymer (CFRP) has significant application value in the aerospace field due to its lightweight and high-strength characteristics. However，during the hole-making process，the material’s anisotropic nature makes it susceptible to defects such as delamination and burrs，which can adversely affect the service performance and assembly quality of components. Therefore，it is necessary to conduct high-precision and high-efficiency detection on it. Based on the analysis of the defect mechanisms associated with hole-making in CFRP，this paper thoroughly examines the applicability of non-destructive testing technologies for CFRP hole-making defect. It analyzes the characteristics of traditional machine vision detection methods with those based on deep learning techniques. Furthermore，it emphasizes that intelligent detection technology utilizing multimodal data fusion provides significant advantages in enhancing both detection accuracy and efficiency. In addition，in response to the challenges currently faced by CFRP hole-making defect detection technologies，it proposes several development paths. These include the creation of an anisotropic adaptive detection algorithm，the establishment of a standardized defect classification system，and the implementation of online monitoring for the hole-making process. The aim is to provide innovative theoretical support and technical direction for intelligent and high-precision detection in the field of aerospace composite material manufacturing.

关键词

碳纤维复合材料 / 制孔缺陷 / 无损检测 / 机器视觉检测 / 智能检测

Key words

CFRP / hole-making defect / non-destructive testing / machine vision detection / intelligent detection

参考文献

[1] 宋晨曦，林海涛，赖恩平，等. 碳纤维增强热塑性复合材料成型工艺的研究进展[J].纺织科学与工程学报，2023，40（4）：86-93.

[2] SONG C X， LIN H T， LAI E P，et al. Research progress of the forming process of carbon fiber reinforced thermoplastic composites[J].Journal of Textile Science and Engineering，2023，40（4）：86-93.

[3] RAJAK D K， PAGAR D D， MENEZES P L，et al. Fiber-reinforced polymer composites：manufacturing，properties，and applications[J].Polymers，2019，11（10）：1667.

[4] ZHANG J， LIN G， VAIDYA U . Past，present and future prospective of global carbon fibre composite developments and applications[J].Composites Part B，2023，250：110463.

[5] AAMIR M， TOLOUEI-RAD M， GIASIN K，et al. Recent advances in drilling of carbon fiber-reinforced polymers for aerospace applications：a review[J].The International Journal of Advanced Manufacturing Technology，2019，105（5/6）：2289-2308.

[6] CHEN Z G， DING F， ZHANG Z C，et al. A review on machining SiC_p/Al composite materials[J].Micromachines，2024，15（1）：107.

[7] LIU W T， QIN X D， LI S P，et al. Experimental research on the matrix cracks and delamination distribution and their monitoring methods during drilling UD-CFRP[J].The International Journal of Advanced Manufacturing Technology，2024，130（3/4）：1595-1615.

[8] 魏莹莹. 碳纤维增强复合材料孔加工缺陷产生机理、评价与工艺优化研究[D]. 上海：上海交通大学，2016.

[9] WEI Y Y . The drilling defects machanism，evaluation method and process optimization on carbon fiber reinforced polymer[D]. Shanghai：Shanghai Jiao Tong University，2016.

[10] PERUŃ G . Advances in non-destructive testing methods[J].Materials（Basel，Switzerland），2024，17（3）：554.

[11] 张薇，苑改红，周天一，等. 基于机器学习的航天装备金属表面缺陷检测方法综述[J].航天电子对抗，2025，41（3）：43-50.

[12] ZHANG W， YUAN G H， ZHOU T Y，et al. Review of machine learning-based methods for detecting surface defects in aerospace equipment metals[J].Aerospace Electronic Warfare，2025，41（3）：43-50.

[13] TOWSYFYAN H， BIGURI A， BOARDMAN R，et al. Successes and challenges in non-destructive testing of aircraft composite structures[J].Chinese Journal of Aeronautics，2020，33（3）：771-791.

[14] REN Z H， FANG F Z， YAN N，et al. State of the art in defect detection based on machine vision[J].International Journal of Precision Engineering and Manufacturing-Green Technology，2022，9（2）：661-691.

[15] MA Y X， YIN J X， HUANG F，et al. Surface defect inspection of industrial products with object detection deep networks：a systematic review[J].Artificial Intelligence Review，2024，57（12）：333.

[16] DONG G P， SUN S W， WANG Z X，et al. Application of machine vision-based NDT technology in ceramic surface defect detection-a review[J].Materials Testing，2022，64（2）：202-219.

[17] ZHENG H， ZHANG W J， LI B W，et al. Recent advances of interphases in carbon fiber-reinforced polymer composites：a review[J].Composites Part B，2022，233：109639.

[18] HOU G Y， LUO B， ZHANG K F，et al. Effects of heat accumulation on the characteristics of hole wall temperature and damages in drilling of UD CFRP[J].The International Journal of Advanced Manufacturing Technology，2021，115（5/6）：1529-1546.

[19] 徐锦泱. 碳纤维增强树脂基复合材料钻削缺陷研究进展[J].航空制造技术，2022，62（22）：24-33.

[20] XU J Y . Research advances in drilling-induced defects of carbon fiber reinforced polymers[J].Aeronautical Manufacturing Technology，2022，62（22）：24-33.

[21] ZHANG D Z， WANG H， BURKS A R，et al. Delamination in rotary ultrasonic machining of CFRP composites：finite element analysis and experimental implementation[J].The International Journal of Advanced Manufacturing Technology，2020，107（9/10）：3847-3858.

[22] RAHME P， LANDON Y， LACHAUD F，et al. Delamination-free drilling of thick composite materials[J].Composites Part A，2015，72：148-159.

[23] 朱萍玉，李永敬，孙孝鹏，等. 纤维增强复合材料制孔分层缺陷检测与评价技术研究进展[J].中国测试，2018，44（1）：1-8.

[24] ZHU P Y， LI Y J， SUN X P，et al. Research progress on detection and evaluation technology of drilling delamination defect for fiber reinforced plastic[J].China Measurement & Test，2018，44（1）：1-8.

[25] SHAHABAZ S M， SHETTY N， SHARMA S，et al. Optimization of drilling parameters on delamination and burr formation in drilling of neat CFRP and hybrid CFRP nano-composites[J].Materials Research Express，2024，11（3）：035006.

[26] HAN S L， LI X D， CUI Z，et al. Detection and characterization of hole processing defects in carbon fiber-reinforced polymer[J].Journal of Physics：Conference Series，2024，2879（1）：012003.

[27] ZHU W Y， FU H G， LI F，et al. Optimization of CFRP drilling process：a review[J].The International Journal of Advanced Manufacturing Technology，2022，123（5/6）：1403-1432.

[28] DONG Z G， WANG Z J， TIAN J C，et al. Investigation on tearing damage of CFRP circular cell honeycomb in end-face grinding[J].Composite Structures，2023，325：117616.

[29] 康仁科，陆冰伟，陈凯良，等. 超声振动辅助磨削 CFRP 复合材料薄管撕裂损伤研究[J].中国机械工程，2024，35（3）：524-533.

[30] KANG R K， LU B W， CHEN K L，et al. Study on tearing of CFRP thin circular tubes machined by ultrasonic vibration assisted grinding[J].China Mechanical Engineering，2024，35（3）：524-533.

[31] LI P N， QIU X Y， LI C P，et al. Hole exit damage and tool wear during the drilling of CFRP with a double-point angle drill[J].Journal of Mechanical Science and Technology，2019，33（5）：2363-2370.

[32] XIAO J Z， WANG G F， SU H，et al. Study on cutting force and induced thermal damage of carbon fiber reinforced polymer composites using microscopic simulation modeling[J].Polymer Composites，2022，43（3）：1626-1636.

[33] GU G W， PARK M S， SUH J H，et al. A technique for integrated compensation of geometric errors and thermal errors to improve positional accuracy of hole machining in large-size parts[J].International Journal of Precision Engineering and Manufacturing，2024，25（8）：1541-1555.

[34] 张辉，张邹铨，陈煜嵘，等. 工业铸件缺陷无损检测技术的应用进展与展望[J].自动化学报，2022，48（4）：935-956.

[35] ZHANG H， ZHANG Z Q， CHEN Y R，et al. Application advance and prospect of nondestructive testing technology for industrial casting defects[J].Acta Automatica Sinica，2022，48（4）：935-956.

[36] 刘文婷，杨建青，陆燕燕，等. 应用于复合材料诊断的声发射检测技术综述[J].绝缘材料，2024，57（6）：9-16.

[37] LIU W T， YANG J Q， LU Y Y，et al. Review of acoustic emission detection techniques applied to composite material diagnosis[J].Insulating Materials，2024，57（6）：9-16.

[38] LIU Y . Research progress of acoustic emission detection technology based on modal theory[J].Journal of Computational Methods in Sciences and Engineering，2021，21（4）：853-864.

[39] LIU P F， CHU J K， LIU Y L，et al. A study on the failure mechanisms of carbon fiber/epoxy composite laminates using acoustic emission[J].Materials & Design，2012，37：228-235.

[40] BARILE C， CASAVOLA C， PAPPALETTERA G，et al. Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique[J].Applied Acoustics，2022，185：108425.

[41] 白生宝，肖迎春，杨宇，等. 飞机结构强度试验中的声发射应用进展[J].环境技术，2021，39（5）：121-124.

[42] BAI S B， XIAO Y C， YANG Y，et al. Applicate progress of acoustic emission technology in the aircraft structure test[J].Environmental Technology，2021，39（5）：121-124.

[43] VANDENDRIESSCHE J， ORTA A H， VERBOVEN E，et al. Probabilistic ultrasound C-scan imaging of barely visible impact damage in CFRP laminates[J].Composite Structures，2022，284：115209.

[44] LI Y M， YAO K， LI X L . An ultrasonic signal reconstruction algorithm of multilayer composites in non-destructive testing[J].Applied Acoustics，2022，186：108461.

[45] ZHANG C C， ZHANG Y H， LIU D Z，et al. A novel two-level approach to defect detection in braided CFRP using air-coupled ultrasonic testing[J].Ultrasonics，2023，128：106884.

[46] MILLS B， JAVADI Y， ABAD F，et al. Inspection of wind turbine bolted connections using the ultrasonic phased array system[J].Heliyon，2024，10（14）：34579.

[47] LIAN Y D， XIE L Y， HAN S W，et al. Advances in post-processing technology for laser ultrasound detection signals：a review[J].IEEE Sensors Journal，2023，23：28564-28578.

[48] LONG B N， CHENG Z W， LIAO W S，et al. Large-field detection of metal/CFRP hybrid composites based on air-coupled laser ultrasound[J].Composites Communications，2025，53：102174.

[49] 方周倩，苗沛源，金肖克，等. 碳纤维复合材料孔洞损伤超声波 C 扫描无损检测[J].纺织学报，2022，43（10）：71-76.

[50] FANG Z Q， MIAO P Y， JIN X K，et al. Nondestructive testing on damage of carbon fiber composites using ultrasonic C-scanning[J].Journal of Textile Research，2022，43（10）：71-76.

[51] 詹湘琳，韩红斌，周德新. 民用飞机复合材料结构件超声相控阵无损检测技术进展[J].航空制造技术，2014，54（15）：124-127.

[52] ZHAN X L， HAN H B， ZHOU D X . Progress of ultrasonic phased array nondestructive testing technique for civil aviation composite structures[J].Aeronautical Manufacturing Technology，2014，54（15）：124-127.

[53] CHRYSAFI A P， ATHANASOPOULOS N， SIAKAVELLAS N J . Damage detection on composite materials with active thermography and digital image processing[J].International Journal of Thermal Sciences，2017，116：242-253.

[54] QU Z， JIANG P， ZHANG W X . Development and application of infrared thermography non-destructive testing techniques[J].Sensors，2020，20（14）：3851.

[55] CIAMPA F， MAHMOODI P， PINTO F，et al. Recent advances in active infrared thermography for non-destructive testing of aerospace components[J].Sensors，2018，18（2）：609-609.

[56] LUO Z T， WANG H， HUANG Y D，et al. Nondestructive detection of CFRP subsurface defects using transient lock-in thermography[J].Journal of Thermal Analysis and Calorimetry，2025，150（11）：8229-8239.

[57] ZHOU G Y， ZHANG Z J， YIN W L，et al. Characterization and depth detection of internal delamination defects in CFRP based on line laser scanning infrared thermography[J].Structural Health Monitoring，2024，23（5）：3195-3210.

[58] 林伟谦. 浅谈红外热成像技术在航空无损检测中的应用[J].科技信息，2012，24：258-259.

[59] LIN W Q . Application of infrared thermal imaging technology in aviation nondestructive testing[J].Science & Technology Information，2012，24：258-259.

[60] LIU S N， SONG K C， YANG X M，et al. EHSGNet：a novel edge and high-level semantic guided network for CFRP subsurface defects detection[J].Measurement，2024，237：115210.

[61] 董方旭，王从科，凡丽梅，等. X 射线检测技术在复合材料检测中的应用与发展[J].无损检测，2016，38（2）：67-72.

[62] DONG F X， WANG C K， FAN L M，et al. The application and development of detection of composite materials by X-ray nondestructive testing techniques[J].Nondestructive Testing，2016，38（2）：67-72.

[63] KATUNIN A， DAŃCZAK M， KOSTKA P . Automated identification and classification of internal defects in composite structures using computed tomography and 3D wavelet analysis[J].Archives of Civil and Mechanical Engineering，2015，15（2）：436-448.

[64] PEJRYD L， BENO T， CARMIGNATO S . Computed tomography as a tool for examining surface integrity in drilled holes in CFRP composites[J].Procedia CIRP，2014，13：43-48.

[65] PENUMADU D， KIM F， BUNN J . Damage of composite materials subjected to projectile penetration using high resolution X-ray micro computed tomography[J].Experimental Mechanics，2016，56（4）：607-616.

[66] 罗美侬. Eshihou 美国 X 射线工业 CT 的发展及应用[C]//2004 年全国射线检测新技术研讨会论文集. 北京：中国机械工程学会，2005：112-117.

[67] LUO M N . Eshihou development and application of X-ray industrial CT in the United States[C]//Proceedings of the National Conference on New Technologies for Radiographic Testing. Beijing：China Mechanical Engineering Society，2005：112-117.

[68] 林心园. 基于 X 射线图像的树脂基复材工件缺陷检测技术研究[D]. 济南：济南大学，2022.

[69] LIN X Y . Research on defect detection technology based on X-ray images for resin-based composite workpiece[D]. Jinan：University of Jinan，2022.

[70] HE Z Q， LIU Q F . Deep regression neural network for industrial surface defect detection[J].IEEE Access，2020，8：35583-35591.

[71] 张涛，刘玉婷，杨亚宁，等. 基于机器视觉的表面缺陷检测研究综述[J].科学技术与工程，2020，20（35）：14366-14376.

[72] ZHANG T， LIU Y T， YANG Y N，et al. Review of surface defect detection based on machine vision[J].Science Technology and Engineering，2020，20（35）：14366-14376.

[73] THAKUR R S， CHATTERJEE S， YADAV R N，et al. Image de-noising with machine learning：a review[J].IEEE Access，2021，9：93338-93363.

[74] 王浩，张叶，沈宏海，等. 图像增强算法综述[J].中国光学，2017，10（4）：438-448.

[75] WANG H， ZHANG Y， SHEN H H，et al. Review of image enhancement algorithms[J].Chinese Optics，2017，10（4）：438-448.

[76] QI J， YANG H T . Research on image segmentation and edge detection technology based on computer vision[J].Journal of Physics，2021，1994（1）：012035.

[77] GIACOMINI M， PEROTTO S . Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information[J].Computers & Mathematics with Applications，2022，11：1-17.

[78] MUTLAG W K， ALI S K， AYDAM Z M，et al. Feature extraction methods：a review[J].Journal of Physics Conference Series，2020，1591（1）：012028.

[79] DHONGADE M . Survey on texture classification methods[J].International Journal of Computer Science Trends and Technology，2015，3（5）：185-188.

[80] AHMED J，GAO B， WOO W L . Wavelet-integrated alternating sparse dictionary matrix decomposition in thermal imaging CFRP defect detection[J].IEEE Transactions on Industrial Informatics，2019，15（7）：4033-4043.

[81] 梁思羽，刘广军，陈涛，等. 基于极坐标映射的 CFRP 制孔毛刺形态学特征与评价指标[J].机械工程学报，2025，61（7）：167-180.

[82] LIANG S Y， LIU G J， CHEN T，et al. Morphological characteristics and evaluation indicators of CFRP drilling burrs based on polar coordinate mapping[J].Journal of Mechanical Engineering，2025，61（7）：167-180.

[83] ZHANG S G， WANG W H， ZHANG T R，et al. Evaluation of hole quality in drilling CF/BMI composite via machine learning：multi-defects analysis and fatigue life prediction[J].Thin-Walled Structures，2025，212：113189.

[84] 张伟，王梦迪，樊俊铃，等. 机器视觉在飞机结构损伤检测中的应用进展与展望[J].无损检测，2021，43（10）：75-80.

[85] ZHANG W， WANG M D， FAN J L，et al. Progress and prospect of the application of machine vision in aircraft structural damage detection[J].Nondestructive Testing，2021，43（10）：75-80.

[86] SABERIRONAGHI A， REN J， EL-GINDY M，et al. Defect detection methods for industrial products using deep learning techniques：a review[J].Algorithms，2023，16（2）：95.

[87] CHUM L， SUBRAMANIAN A， BALASUBRAMANIAN V N，et al. Beyond supervised learning：a computer vision perspective[J].Journal of the Indian Institute of Science，2019，99（2）：177-199.

[88] LU Y， ZHANG C S ，DONG X J. A survey of real-time surface defect inspection methods based on deep learning[J].Artificial Intelligence Review，2023，56（10）：12131-12170.

[89] SINGH V， BARAL A， KUMAR R，et al. A hybrid deep learning model for enhanced structural damage detection：integrating ResNet50，GoogLeNet，and attention mechanisms[J].Sensors，2024，24（22）：7249-7249.

[90] 张阳婷，黄德启，王东伟，等. 基于深度学习的目标检测算法研究与应用综述[J].计算机工程与应用，2023，59（18）：1-13.

[91] ZHANG Y T， HUANG D Q， WANG D W，et al. Review on research and application of deep learning-based target detection algorithms[J].Computer Engineering and Applications，2023，59（18）：1-13.

[92] 李炳臻，姜文志，顾佼佼，等. 基于卷积神经网络的目标检测算法综述[J].计算机与数字工程，2022，50（5）：1010-1017.

[93] LI B Z， JIANG W Z， GU J J，et al. Review of target detection algorithms based on deep learning[J].Computer & Digital Engineering，2022，50（5）：1010-1017.

[94] 赵朗月，吴一全. 基于机器视觉的表面缺陷检测方法研究进展[J].仪器仪表学报，2022，43（1）：198-219.

[95] ZHAO L Y， WU Y Q . Research progress of surface defect detection methods based on machine vision[J].Chinese Journal of Scientific Instrument，2022，43（1）：198-219.

[96] WANG X L， ZHANG R F， SHEN C H，et al. SOLO：a simple framework for instance segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2022，44（11）：8587-8601.

[97] FOTOUHI S， PASHMFOROUSH F， BODAGHI M，et al. Autonomous damage recognition in visual inspection of laminated composite structures using deep learning[J].Composite Structures，2021，268：113960.

[98] WEN R Y， TAO C C， JI H L，et al. Classification，localization and quantization of eddy current detection defects in CFRP based on EDC-YOLO[J].Sensors，2024，24（20）：6753.

[99] CHENG X Y， MA G S， WU Z Y，et al. Automatic defect depth estimation for ultrasonic testing in carbon fiber reinforced composites using deep learning[J].NDT & E International，2023，135：102804.

[100] LI G H， YANG S， CAO S M，et al. A semi-supervised deep learning approach for circular hole detection on composite parts[J].The Visual Computer，2021，37（3）：433-445.

[101] SAEED N， KING N， SAID Z，et al. Automatic defects detection in CFRP thermograms，using convolutional neural networks and transfer learning[J].Infrared Physics & Technology，2019，102：103048.

[102] 何友，刘瑜，李耀文，等. 多源信息融合发展及展望[J].航空学报，2025，46（6）：29-54.

[103] HE Y， LIU Y， LI Y W，et al. Development and prospects of multisource information fusion[J].Acta Aeronautica et Astronautica Sinica，2025，46（6）：29-54.

[104] 吴昊，刘志平，杜勇，等. 超声红外热波成像在 CFRP 板螺栓孔损伤检测的研究[J].红外技术，2019，41（8）：786-794.

[105] WU H， LIU Z P， DU Y，et al. Study on damage detection of bolt holes in CFRP plate by ultrasonic infrared thermal wave imaging[J].Infrared Technology，2019，41（8）：786-794.

[106] MAGHAMI A， SALEHI M， KHOSHDARREGI M . Automated vision-based inspection of drilled CFRP composites using multi-light imaging and deep learning[J].CIRP Journal of Manufacturing Science and Technology，2021，35：441-453.

[107] HU Q C， WEI X L， GUO H Y，et al. Study on intelligent and visualization method of ultrasonic testing of composite materials based on deep learning[J].Applied Acoustics，2023，207：109363.

[108] LI C Z， WEI X L， HE W F，et al. Intelligent recognition of composite material damage based on deep learning and infrared testing[J].Optics Express，2021，29（20）：31739-31753.

[109] GONG Y F， SHAO H L， LUO J，et al. A deep transfer learning model for inclusion defect detection of aeronautics composite materials[J].Composite Structures，2020，252：112681.

基金资助

国家自然科学基金面上项目(52175400)