Comparative Dimensional Accuracy of Dovetail and Airfoil EDM Defects on Gas Turbine Blades
Abstract
Keywords
Full Text:
PDFReferences
References
Barella, S., Boniardi, M., Cincera, S., Pellin, P., Degive, X., and Gijbels, S., Failure analysis of a third stage gas turbine blade, Engineering Failure Analysis, 18(1), pp. 386–393, Jan. 2011. https://doi.org/10.1016/j.engfailanal.2010.09.017.
Katinić, M., Kozak, D., Gelo, I., and Damjanović, D., Corrosion fatigue failure of steam turbine moving blades: A case study, Engineering Failure Analysis, 106, Dec. 2019. https://doi.org/10.1016/j.engfailanal.2019.08.002.
Arabkoohsar, A. and Sadi, M., Thermodynamics, economic and environmental analyses of a hybrid waste–solar thermal power plant, Journal of Thermal Analysis and Calorimetry, 144(3), pp. 917–940, May 2021. https://doi.org/10.1007/S10973-020-09573-3.
Zhang, S., et al., UAV based defect detection and fault diagnosis for static and rotating wind turbine blade: a review, Nondestructive Testing and Evaluation, 40(4), pp. 1691–1729, Apr. 2025. https://doi.org/10.1080/10589759.2024.2395363.
Izzuddin, M. Y., Syaifudin, A., Rahmat, M. F., and Suwarmin, Finite Element-Based Fatigue Analysis of Medium-Speed Train Car Structure, International Review of Mechanical Engineering, 19(9), pp. 472–483, 2025. https://doi.org/10.15866/ireme.v19i9.26790.
Syaifudin, A., et al., How Mechanical Properties of Ureteral Stent Material Affect Its Service Life, Journal of Biomedical Materials Research Part B: Applied Biomaterials, 113(11), Nov. 2025. https://doi.org/10.1002/jbm.b.35692.
Lario, J., Mateos, J., Psarommatis, F., and Ortiz, Á., Towards Zero Defect and Zero Waste Manufacturing by Implementing Non-Destructive Inspection Technologies, Journal of Manufacturing and Materials Processing, 9(2), article 29, Jan. 2025. https://doi.org/10.3390/JMMP9020029.
Lysenko, I., Kuts, Y., Uchanin, V., Mirchev, Y., and Levchenko, O., Evaluation of Eddy Current Array Performance in Detecting Aircraft Component Defects, Transactions on Aerospace Research, 2024(2), pp. 1–9, Jun. 2024. https://doi.org/10.2478/TAR-2024-0007.
Wahjudi, A., et al., The Application of Hybrid BPNN and GA in the Dry Machining End-Milling Process of Aluminum Alloy, International Review of Mechanical Engineering, 19(1), pp. 1–15, 2025. https://doi.org/10.15866/ireme.v19i1.25988.
Sarala Rubi, C., et al., Comprehensive review on wire electrical discharge machining: a non-traditional material removal process, Frontiers in Mechanical Engineering, 10, article 1322605, Jan. 2024. https://doi.org/10.3389/fmech.2024.1322605.
Markopoulos, A. P., Papazoglou, E. L., and Karmiris-Obratański, P., Experimental Study on the Influence of Machining Conditions on the Quality of Electrical Discharge Machined Surfaces of aluminum alloy Al5052, Machines, 8(1), article 12, Mar. 2020. https://doi.org/10.3390/machines8010012.
Zuo, P., Dou, Z., Yang, H., Hou, H., and Zheng, Y., Enhancement of resistance to cracking under thermal cycling of EDM-treated H13 steel by shot peening with optimized intensity, Engineering Failure Analysis, 187, article 110622, Apr. 2026. https://doi.org/10.1016/j.engfailanal.2026.110622.
Kim, D., Gerstberger, U., Asli, M., and Höschler, K., U-Net driven semantic segmentation for detection and quantification of cracks on gas turbine blade tips, Results in Engineering, 29, article 108864, Mar. 2026. https://doi.org/10.1016/j.rineng.2025.108864.
Ahuja, N., Batra, U., and Kumar, K., Experimental Investigation and Optimization of Wire Electrical Discharge Machining for Surface Characteristics and Corrosion Rate of Biodegradable Mg Alloy, Journal of Materials Engineering and Performance, 29(6), pp. 4117–4129, Jun. 2020. https://doi.org/10.1007/S11665-020-04905-8.
Halkaci, H. S., Mavi, Ö., and Yigit, O., Evaluation of form error at semi-spherical tools by use of image processing, Measurement, 40(9–10), pp. 860–867, Nov. 2007. https://doi.org/10.1016/j.measurement.2007.06.006.
Gehri, N., Mata-Falcón, J., and Kaufmann, W., Automated crack detection and measurement based on digital image correlation, Construction and Building Materials, 256, article 119383, Sep. 2020. https://doi.org/10.1016/j.conbuildmat.2020.119383.
Bergs, T., Holst, C., Gupta, P., and Augspurger, T., Digital image processing with deep learning for automated cutting tool wear detection, Procedia Manufacturing, 48, pp. 947–958, Jan. 2020. https://doi.org/10.1016/j.promfg.2020.05.134.
Qudeiri, J. E. A., Zaiout, A., Mourad, A. H. I., Abidi, M. H., and Elkaseer, A., Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels, Applied Sciences, 10(6), article 2082, Mar. 2020. https://doi.org/10.3390/app10062082.
DOI: http://dx.doi.org/10.12962%2Fj25807471.v10i1.23503

JMES The International Journal of Mechanical Engineering and Sciences by Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/jmes.





