Inspection of Additively Manufactured Aero-engine Parts Using Computed Radiography Technique

被引:0
|
作者
B. K. Nagesha
S. Anand Kumar
S. Rajeswari
Sanjay Barad
Akshay Pathania
机构
[1] DRDO,Gas Turbine Research Establishment
[2] Indian Institute of Technology Jammu,Additive Manufacturing Research Laboratory, Department of Mechanical Engineering
[3] Indian Institute of Technology Jammu,Department of Mechanical Engineering
来源
Journal of Materials Engineering and Performance | 2022年 / 31卷
关键词
additive manufacturing; aero-engine end-use parts; computed radiography; inspection; laser powder bed fusion; metrological analysis;
D O I
暂无
中图分类号
学科分类号
摘要
The recent advancement and development of fabricating metallic end-use parts by the additive manufacturing (AM) process are targeted for the complex shapes and part consolidation applications of aero-engine systems. Among various AM technologies, laser powder bed fusion (LPBF) is promising for complex geometries with better dimensional accuracy and surface finish. Application of non-destructive testing (NDT) methods to inspect the quality and integrity of LPBF processed parts is inevitable for quantitative assessment of process-related defects like voids and porosities. The LPBF process also tosses inspection and metrological challenges for end-use parts involving the inherent geometric complexity of topology-optimized AM parts and inaccessible internal geometries, primarily for conventional NDT methods. The computed radiography (CR) is a simpler and faster NDT technique employed in the present study to inspect the inaccessible internal geometries in the LPBF parts. CR inspection methodology is established on the different LPBF samples with different internal through-hole of 0.5 and 1.0 mm diameters. The established methodology was employed on the aero-engine parts such as instrument probe, oil injector, and diffusion cooling hole plate. The dimensional analysis demonstrated that the CR results were comparable with the CAD model. In the present study, a reasonable accuracies of 0.6, 0.6, 0.8, 0.85, and 1.24% for thin-walled plate (0.5 mm hole), thin-walled plate (1.0 mm hole), diffusion cooling hole plate, oil injector, and instrument probe, respectively, were arrived. The present study envisages the CR technique as a quicker and cost-effective inspection tool for early-stage detection of unacceptable dimensional deviations and process-induced defects for shortening the lead time of the complex-shaped aero-engine end-use parts.
引用
收藏
页码:6322 / 6331
页数:9
相关论文
共 50 条
  • [41] Aero-Engine Surge Fault Diagnosis Using Deep Neural Network
    Zhang, Kexin
    Lin, Bin
    Chen, Jixin
    Wu, Xinlong
    Lu, Chao
    Zheng, Desheng
    Tian, Lulu
    COMPUTER SYSTEMS SCIENCE AND ENGINEERING, 2022, 42 (01): : 351 - 360
  • [42] Correcting Hardening Artifacts of Aero-Engine Blades with an Iterative Linear Fitting Technique Framework
    Gao, Yenan
    Fu, Jian
    Chen, Xiaolong
    SENSORS, 2024, 24 (06)
  • [43] Design of Aero-Engine Performance Simulation System Based on Object-Oriented Technique
    Zhang, Xiaobo
    Wang, Zhanxue
    Cai, Yuanhu
    2010 INTERNATIONAL CONFERENCE ON INFORMATION, ELECTRONIC AND COMPUTER SCIENCE, VOLS 1-3, 2010, : 1859 - 1863
  • [44] An aero-engine inspection continuum robot with tactile sensor based on EIT for exploration and navigation in unknown environment
    Wang, Yaming
    Ju, Feng
    Cao, Yanfei
    Yun, Yahui
    Wang, Yaoyao
    Bai, Dongming
    Chen, Bai
    2019 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2019, : 1157 - 1162
  • [45] Post processing of additively manufactured parts using electrochemical jet machining
    Speidel, Alistair
    Selo, Richard
    Bisterov, Ivan
    Mitchell-Smith, Jonathon
    Clare, Adam T.
    MATERIALS LETTERS, 2021, 292
  • [46] Enhanced sensitivity detection of defects in gas turbine blades of aero-engine and hairpin tubes of heavy water plant using microfocal radiography
    Saravanan, T.
    Bagavathiappan, S.
    Philip, John
    Jayakumar, T.
    Raj, Baldev
    INSIGHT, 2008, 50 (10) : 560 - 563
  • [47] Process Parameter Optimization of Additively Manufactured Parts Using Intelligent Manufacturing
    Rehman, Rizwan Ur
    Zaman, Uzair Khaleeq Uz
    Aziz, Shahid
    Jabbar, Hamid
    Shujah, Adnan
    Khaleequzzaman, Shaheer
    Hamza, Amir
    Qamar, Usman
    Jung, Dong-Won
    SUSTAINABILITY, 2022, 14 (22)
  • [48] Detecting and classifying hidden defects in additively manufactured parts using deep learning and X-ray computed tomography
    Bimrose, Miles V.
    Hu, Tianxiang
    Mcgregor, Davis J.
    Wang, Jiongxin
    Tawfick, Sameh
    Shao, Chenhui
    Liu, Zuozhu
    King, William P.
    JOURNAL OF INTELLIGENT MANUFACTURING, 2024,
  • [49] Correction to: Flaw Identification in Additively Manufactured Parts Using X-ray Computed Tomography and Destructive Serial Sectioning
    Zackary Snow
    Jayme Keist
    Griffin Jones
    Rachel Reed
    Edward Reutzel
    Veeraraghavan Sundar
    Journal of Materials Engineering and Performance, 2021, 30 : 4965 - 4965
  • [50] Failure classification of porous additively manufactured parts using Deep Learning
    Johnson, Kyle L.
    Maestas, Demitri
    Emery, John M.
    Grigoriu, Mircea D.
    Smith, Matthew D.
    Martinez, Carianne
    COMPUTATIONAL MATERIALS SCIENCE, 2022, 204