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All issues Volume 65 (2024) ITM Web Conf., 65 (2024) 01001 References
Open Access
Issue
ITM Web Conf.
Volume 65, 2024
International Conference on Multidisciplinary Approach in Engineering, Technology and Management for Sustainable Development: A Roadmap for Viksit Bharat @ 2047 (ICMAETM-24)
Article Number 01001
Number of page(s) 13
Section Mechanical Engineering
DOI https://doi.org/10.1051/itmconf/20246501001
Published online 16 July 2024
  1. B.R.T. Vilane, Assessment of stabilisation of adobes by confined compression tests. Biosyst Eng. 106.4 (2010) 551–558. [CrossRef] [Google Scholar]
  2. T. Shanmugapriya, R.N. Uma, Optimization of partial replacement of m-sand by natural sand in high performance concrete with silica fume. Int. J. Eng. Sci. Emerg. Technol. 2.2 (2012) 73–80. [Google Scholar]
  3. Juran, J. M. Quality Control Handbook, 1979 (McGraw-Hill, New York). [Google Scholar]
  4. G.C.M. Patel, P. Krishna, M.B. Parappagoudar, Optimization of squeeze cast process parameters using Taguchi and grey relational analysis. Proc. Tech. 14 (2014) 157–164. [CrossRef] [Google Scholar]
  5. P. Senthil, K.S. Amirthagadeswaran, Experimental study and squeeze casting process optimization for high quality AC2A aluminium alloy castings. Arab. J. Sci. Eng., 39.3 (2014) 22152225. [CrossRef] [Google Scholar]
  6. S. Kumar, P.S. Satsangi, D.R. Prajapati, Optimization of green sand casting process parameters of a foundry by using Taguchi’s method. Int. J Adv. Manuf. Tech., 55.1-4 (2011) 23–34. [CrossRef] [Google Scholar]
  7. G.R. Chate, G.C.M. Patel, R.M. Kulkarni, P. Vernekar, A.S. Deshpande, M.B. Parappagoudar, Study of the effect of nano-silica particles on resinbonded moulding sand properties and quality of casting. Silicon (2018) 1–16. [Google Scholar]
  8. Kackar, R. N. Off-line quality control, parameter design and the Taguchi method. J. Qual. Technol., 1985, 17(4), 176–188. [CrossRef] [Google Scholar]
  9. Logothetis, N. The role of data transformation in the Taguchi analysis. Quality and Reliability Engng Int., 1988, 4, 49–61. [CrossRef] [Google Scholar]
  10. V.V. Reddy, P.M. Valli, A. Kumar, C.S. Reddy, Multi-objective optimization of electrical discharge machining of PH17-4 stainless steel with surfactant-mixed and graphite powder–mixed dielectric using Taguchi-data envelopment analysis–based ranking method. P I Mech. Eng. B-J Eng., 229.3 (2015). 487–494. [Google Scholar]
  11. T. Muthuramalingam, S. Vasanth, P. Vinothkumar, T. Geethapriyan, M.M. Rabik, Multi criteria decision making of abrasive flow oriented process parameters in abrasive water jet machining using Taguchi–DEAR methodology. Silicon (2018) 1–7. [Google Scholar]
  12. Phadke, S. M. Quality Engineering Using Robust Design, 1989 (Prentice-Hall, Englewood Cliffs, New Jersey). [Google Scholar]
  13. B. Surekha, L.K. Kaushik, A.K. Panduy, P.R. Vundavilli, M.B. Parappagoudar, Multi-objective optimization of green sand mould system using evolutionary algorithms. Int. J Adv. Manuf. Tech., 58.1-4. (2012) 9–17. [CrossRef] [Google Scholar]
  14. S.R. Pulivarti, A.K. Birru, Optimization of green sand mould system using Taguchi based grey relational analysis. China Foundry, 15.2 (2018) 152–159. [CrossRef] [Google Scholar]
  15. N.I.S. Hussein, M.N. Ayof, N.I. Mohamed Sokri, Mechanical properties and loss on ignition of phenolic and furan resin bonded sand casting. Int. J. Min. Met. Mech. Eng. 1 (2013) 223–227. [Google Scholar]
  16. Taguchi, G. and Konishi, S. Orthogonal Arrays and Linear Graphs, 1987 (American Supplier Institute, Dearborn, Michigan). [Google Scholar]
  17. J.A. Ghani, I.A. Choudhury, H.H. Hassan, Application of Taguchi method in the optimization of end milling parameters, J. of Mat. Proc. Technol. 145 (2002) 84–92. [Google Scholar]
  18. Enright, T. P. and Price, B. Use of the Taguchi method for off-line quality control, parameter estimation and experimental design in the Casting Division of Ford Motor Company. AFS Trans., 1987, 87, 144–151. [Google Scholar]
  19. R.R. Kundu, B.N. Lahiri, Study and statistical modelling of green sand mould properties using RSM, Int. J. of Mat. nd Product Technol. 31 (2008) 143–158. [CrossRef] [Google Scholar]
  20. C. Saikaew, S. Wiengwiset, Optimization of moulding sand composition for quality improvement of iron castings, Applied Clay Science 67-68 (2012) 26–31. [CrossRef] [Google Scholar]
  21. S.N. Ramrattan, A.M. Paudel, H. Makino, M. Hirata, Desirable green sand properties via aeration sand filling, Transactions of the American Foundry Men’s Society 116 (2008) 493–503. [Google Scholar]
  22. Dahle, A. K., Arnberg, L. and Apelian, D. Burst feeding and its role in porosity formation during solidification of Al foundry alloys. AFS Trans., 1997, 160, 963–969. [Google Scholar]
  23. Chiesa, F., Fuoco, R. and Gruzleski, J. E. Porosity distribution in directionally solidified test bars sand cast from a controlled A356 melt. Int. J. Cast Metals, 1995, 7(2), 113–122. [Google Scholar]
  24. B. H. Kim, J. S. Shin, S. M Lee, B. M. Moon: Improvement of tensile strength and corrosion resistance of highsilicon cast irons by optimizing casting process parameters, J Mater Sci (2007) 42:109117. [Google Scholar]
  25. Ballal Yuvraj P., Dr. Inamdar K. H., Patil P. V.: Application of Taguchi method for design of experiments in turning gray cast iron. International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 3, May-Jun 2012, pp. 1391–1397. [Google Scholar]
  26. V. D Tsoukalas, St A Mavrommatis, N G Orfanoudakis and A K Baldoukas, “A study of porosity formation in pressure die casting using the Taguchi approach”, Proc. Instn Mech. Engrs. Vol. 218 Part B: J. Engineering Manufacture 2004. [Google Scholar]
  27. A. Noorul Haq, S. Guharaja, K. M. Karuppannan: Parameter optimization of CO2 casting process by using Taguchi method. Int J Interact Des Manuf (2009) 3:41–50 [CrossRef] [Google Scholar]
  28. Sushil Kumar, P. S. Satsangi, D. R. Prajapati: Optimization of green sand casting process parameters of a foundry using Taguchi’s method, Int J Adv. Manu f Technology (2011) 55:23–34. [CrossRef] [Google Scholar]
  29. M. Perzyk and A. Kochanski, Detection of Causes of Casting Defects Assisted by Artificial Neural Networks, Institute of Materials Processing, Warsaw University of Technology, Warsaw, Poland, 2003, vol. 217, pp.1279–1284. [Google Scholar]
  30. Rahul Bhedasgaonkar, Uday A. Dabade, May 2012, Analysis of Casting Defects by Design of Experiments Method, Proceedings of 27th National Convention of Production Engineers and National Seminar on Advancements in Manufacturing – VISION 2020, organized by BIT, Mesra, Ranchi, India [Google Scholar]
  31. W. M. Abu Jadayi, “Studying the effects of varying the pouring rate on the casting defects using nondestructive testing techniques,” Jordan J. Mech. Ind. Eng., vol. 5, no. 6, pp. 521–526, 2011. [Google Scholar]
  32. A. Sharma and A. K. Sinha, “Ultrasonic Testing for Mechanical Engineering Domain: Present and Future Perspective,” vol. 7, no. 2, pp. 243–253, 2018, doi: 10.22105/riej.2018.100730.1018. [Google Scholar]
  33. J. Idris and A. Al-bakoosh, “Akademia Baru Application of Non-Destructive Testing Techniques for the Assessment of Casting of AA5083 Alloy Akademia Baru,” vol. 3, no. 1, pp. 25–34, 2014. [Google Scholar]
  34. J. Lin, Y. Yao, L. Ma, and Y. Wang, “Detection of a casting defect tracked by deep convolution neural network,” Int. J. Adv. Manuf. Technol., vol. 97, no. 1–4, pp. 573–581, 2018, doi: 10.1007/s00170-018-1894-0. [CrossRef] [Google Scholar]
  35. W. Du, H. Shen, J. Fu, G. Zhang, and Q. He, “Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning,” NDT E Int., vol. 107, no. May, p. 102144, 2019, doi: 10.1016/j.ndteint.2019.102144. [CrossRef] [Google Scholar]
  36. A. B. H. Bejaxhin, G. Paulraj, and M. Prabhakar, “Inspection of casting defects and grain boundary strengthening on stressed Al6061 specimen by NDT method and SEM micrographs,” J. Mater. Res. Technol., vol. 8, no. 3, pp. 2674–2684, 2019, doi: 10.1016/j.jmrt.2019.01.029. [CrossRef] [Google Scholar]
  37. S. Mozammil, J. Karloopia, and P. K. Jha, “Investigation of porosity in Al casting,” Mater. Today Proc., vol. 5, no. 9, pp. 17270–17276, 2018, doi: 10.1016/j.matpr.2018.04.138. [CrossRef] [Google Scholar]
  38. A. I. Cheprasov, S. V. Knyazev, A. A. Usoltsev, A. E. Dolgopolov, and R. O. Mamedov, “Detection of cold cracks in the cast-steels by the methods of ultrasonic and eddy-current infrared thermography,” IOP Conf. Ser. Mater. Sci. Eng., vol. 150, no. 1, 2016, doi: 10.1088/1757899X/150/1/012026. [CrossRef] [Google Scholar]
  39. C. Jin, X. Kong, J. Chang, H. Cheng, and X. Liu, “Internal crack detection of castings: a study based on relief algorithm and Adaboost-SVM,” Int. J. Adv. Manuf. Technol., vol. 108, no. 9–10, pp. 3313–3322, 2020, doi: 10.1007/s00170-020-05368-w. [CrossRef] [Google Scholar]
  40. M. Ferguson, R. Ak, Y.-T. T. Lee, and K. H. Law, “Automatic localization of casting defects with convolutional neural networks,” pp. 1726–1735, 2018, doi: 10.1109/bigdata.2017.8258115. [Google Scholar]
  41. W. Du, H. Shen, J. Fu, G. Zhang, X. Shi, and Q. He, “Automated detection of defects with low semantic information in X-ray images based on deep learning,” J. Intell. Manuf., 2020, doi: 10.1007/s10845-020-01566-1. [Google Scholar]
  42. E. Hanks, D. Liu, and A. Palazotto, “Surface roughness of electron beam melting Ti-6Al-4v effect on ultrasonic testing,” 57th AIAA/ASCE/AHS/ASC Struct. Struct. Dyn. Mater. Conf., no. January, pp. 1–13, 2016, doi: 10.2514/6.2016-1512. [Google Scholar]
  43. I. Narasimha Murthy and J. Babu Rao, “Non Destructive Evaluation of A356 alloy Castings made in Sand and Granulated Blast Furnace Slag Moulds,” Mater. Today Proc., vol. 5, no. 1, pp. 168–174, 2018, doi: 10.1016/j.matpr.2017.11.068. [CrossRef] [Google Scholar]
  44. S. A. Arhamnamazi, N. B. M. Arab, A. R. Oskouei, and F. Aymerich, “Accuracy assessment of ultrasonic C-scan and X-ray radiography methods for impact damage detection in glass fiber reinforced polyester composites,” J. Appl. Comput. Mech., vol. 5, no. 2, pp. 258–268, 2019, doi: 10.22055/JACM.2018.26297.1318. [Google Scholar]
  45. S. A. Patil and P. D. Darade, “Application and Performance Frequency Response Method as NDT Tool to Detect Defects in Castings,” Mater. Today Proc., vol. 4, no. 8, pp. 8463–8468, 2017, doi: 10.1016/j.matpr.2017.07.192. [CrossRef] [Google Scholar]
  46. B. R. Goodlet, L. H. Rettberg, and T. M. Pollock, “Resonant ultrasound spectroscopy for defect detection in single crystal superalloy castings,” Proc. Int. Symp. Superalloys, vol. 2016-Janua, no. October, pp. 303–312, 2016, doi: 10.1002/9781119075646.ch33. [CrossRef] [Google Scholar]
  47. W. Orłowicz, M. Tupaj, M. Mróz, and E. Guzik, “Evaluation of ductile iron casting material quality using ultrasonic testing,” J. Mater. Process. Technol., vol. 210, no. 11, pp. 1493–1500, 2010, doi: 10.1016/j.jmatprotec.2010.04.007. [CrossRef] [Google Scholar]
  48. A. Messager et al., “In situ synchrotron ultrasonic fatigue testing device for 3D characterisation of internal crack initiation and growth,” Fatigue Fract. Eng. Mater. Struct., vol. 43, no. 3, pp. 558–567, 2020, doi: 10.1111/ffe.13140. [CrossRef] [Google Scholar]
  49. C. Brugger, T. Palin-Luc, P. Osmond, and M. Blanc, “A new ultrasonic fatigue testing device for biaxial bending in the gigacycle regime,” Int. J. Fatigue, vol. 100, pp. 619–626, 2017, doi: 10.1016/j.ijfatigue.2016.12.039. [CrossRef] [Google Scholar]
  50. A. Chabot, N. Laroche, E. Carcreff, M. Rauch, and J. Y. Hascoët, “Towards defect monitoring for metallic additive manufacturing components using phased array ultrasonic testing,” J. Intell. Manuf., vol. 31, no. 5, pp. 1191–1201, 2020, doi: 10.1007/s10845-019-01505-9. [CrossRef] [Google Scholar]
  51. A. Moghanizadeh, “Evaluation of the physical properties of spot welding using ultrasonic testing,” Int. J. Adv. Manuf. Technol., vol. 85, no. 1–4, pp. 535–545, 2016, doi: 10.1007/s00170-015-7952-y. [CrossRef] [Google Scholar]
  52. R. Wang, Z. Liu, J. Wu, B. Jiang, and B. Li, “Research on phased array ultrasonic testing on CFETR vacuum vessel welding,” Fusion Eng. Des., vol. 139, no. January, pp. 124–127, 2019, doi: 10.1016/j.fusengdes.2019.01.050. [CrossRef] [Google Scholar]
  53. G. Raju and M. Ashok, “The Phased Array Advantage of Ultrasonic Scanning of Rocket Motor Cases of Indian Satellite Launch Vehicles,” no. December, pp. 14–16, 2017. [Google Scholar]
  54. J. Parra-Raad, P. Khalili, and F. Cegla, “Shear waves with orthogonal polarisations for thickness measurement and crack detection using EMATs,” NDT E Int., vol. 111, p. 102212, 2020, doi: 10.1016/j.ndteint.2019.102212. [CrossRef] [Google Scholar]
  55. A. Maurer, W. Deodorico, R. Huber, and T. Laffont, “Aerospace Composite Testing Solutions using Industrial Robots,” 18th World Conf. Nondestruct. Test., no. April, p. 7, 2012, [Online] . Available: https://www.ndt.net/article/wcndt2012/papers/166_wcndtfinal00166.pdf. [Google Scholar]
  56. P. Khalili and P. Cawley, “The choice of ultrasonic inspection method for the detection of corrosion at inaccessible locations,” NDT E Int., vol. 99, no. June, pp. 80–92, 2018, doi: 10.1016/j.ndteint.2018.06.003. [CrossRef] [Google Scholar]
  57. S. Palanisamy, C. R. Nagarajah, and P. Iovenitti, “Ultrasonic inspection of rough surface aluminium die castings,” Insight Non-Destructive Test. Cond. Monit., vol. 49, no. 3, pp. 160–164, 2007, doi: 10.1784/insi.2007.49.3.160. [CrossRef] [Google Scholar]
  58. S. nan Xue, Q. chi Le, Y. hui Jia, L. ping Jiang, Z. qiang Zhang, and L. Bao, “Ultrasonic flaw detection of discontinuous defects in magnesium alloy materials,” China Foundry, vol. 16, no. 4, pp. 256–261, 2019, doi: 10.1007/s41230-019-9041-6. [CrossRef] [Google Scholar]
  59. I. Baillie, P. Griffith, X. Jian, and S. Dixon, “Implementing an ultrasonic inspection system to find surface and internal defects in hot, moving steel using E18MATs,” AIP Conf. Proc., vol. 1096, no. 2, pp. 1711–1718, 2009, doi: 10.1063/1.3114165. [CrossRef] [Google Scholar]
  60. D. M. Schwabe, A. Maurer, and R. Koch, “Ultrasonic Testing Machines with Robot Mechanics – A New Approach to CFRP Component Testing,” Aerospace, pp. 1–5, 2010. [Google Scholar]
  61. C. Garnier, M. L. Pastor, F. Eyma, and B. Lorrain, “The detection of aeronautical defects in situ on composite structures using non destructive testing,” Compos. Struct., vol. 93, no. 5, pp. 1328–1336, 2011, doi: 10.1016/j.compstruct.2010.10.017. [CrossRef] [Google Scholar]
  62. R. A. Smith, L. J. Nelson, M. J. Mienczakowski, and R. E. Challis, “Automated analysis and advanced defect characterisation from ultrasonic scans of composites,” Insight Non-Destructive Test. Cond. Monit., vol. 51, no. 2, pp. 82–87, 2009, doi: 10.1784/insi.2009.51.2.82. [CrossRef] [Google Scholar]
  63. A. Habibalahi, M. D. Moghari, K. Samadian, S. S. Mousavi, and M. S. Safizadeh, “Improving pulse eddy current and ultrasonic testing stress measurement accuracy using neural network data fusion,” IET Sci. Meas. Technol., vol. 9, no. 4, pp. 514–521, 2015, doi: 10.1049/ietsmt.2014.0211. [CrossRef] [Google Scholar]
  64. S. Roccella et al., “Development of an ultrasonic test method for the non-destructive examination of ITER divertor components,” Fusion Eng. Des., vol. 84, no. 7–11, pp. 1639–1644, 2009, doi: 10.1016/j.fusengdes.2008.12.096. [CrossRef] [Google Scholar]
  65. A. Kumar, C. J. Torbet, T. M. Pollock, and J. Wayne Jones, “In situ characterization of fatigue damage evolution in a cast Al alloy via nonlinear ultrasonic measurements,” Acta Mater., vol. 58, no. 6, pp. 2143–2154, 2010, doi: 10.1016/j.actamat.2009.11.055. [CrossRef] [MathSciNet] [Google Scholar]
  66. H. Kato, T. Suzuki, Y. Annou, and K. Kageyama, “Nondestructive detection of cold flakes in aluminum alloy die-cast plate with ultrasonic measurement,” Mater. Trans., vol. 45, no. 7, pp. 2403–2409, 2004, doi: 10.2320/matertrans.45.2403. [CrossRef] [Google Scholar]
  67. Q. Y. Lu and C. H. Wong, “Applications of non-destructive testing techniques for post-process control of additively manufactured parts,” Virtual Phys. Prototyp., vol. 12, no. 4, pp. 301–321, 2017, doi: 10.1080/17452759.2017.1357319. [CrossRef] [Google Scholar]
  68. E. Ayorinde et al., “Reliable low-cost NDE of composite marine sandwich structures,” Compos. Part B Eng., vol. 39, no. 1, pp. 226–241, 2008, doi: 10.1016/j.compositesb.2007.02.028. [CrossRef] [Google Scholar]
  69. E. Jasiūnienė et al., “Ultrasonic NDT of wind turbine blades using contact pulse-echo immersion testing with moving water container,” Ultragarsas (Ultrasound), vol. 63, no. 3, pp. 28–32, 2008, doi: 10.5755/J01.U.63.3.17075. [Google Scholar]
  70. A. Wilczek, P. Długosz, and M. Hebda, “Porosity Characterization of Aluminium Castings by Using Particular Non-destructive Techniques,” J. Nondestruct. Eval., vol. 34, no. 3, pp. 1–7, 2015, doi: 10.1007/s10921-015-0302-z. [CrossRef] [Google Scholar]
  71. I. Amenabar, A. Mendikute, A. López-Arraiza, M. Lizaranzu, and J. Aurrekoetxea, “Comparison and analysis of non-destructive testing techniques suitable for delamination inspection in wind turbine blades,” Compos. Part B Eng., vol. 42, no. 5, pp. 1298–1305, 2011, doi: 10.1016/j.compositesb.2011.01.025. [CrossRef] [Google Scholar]
  72. X. E. Gros, J. Bousigue, and K. Takahashi, “NDT data fusion at pixel level,” NDT E Int., vol. 32, no. 5, pp. 283–292, 1999, doi: 10.1016/S09638695(98)00056-5. [CrossRef] [Google Scholar]
  73. T. Hasiotis, E. Badogiannis, and N. G. Tsouvalis, “Application of ultrasonic C-scan techniques for tracing defects in laminated composite materials,” Stroj. Vestnik/Journal Mech. Eng., vol. 57, no. 3, pp. 192–203, 2011, doi: 10.5545/sv-jme.2010.170. [CrossRef] [Google Scholar]
  74. L. Nastac, M. N. Gungor, I. Ucok, K. L. Klug, and W. T. Tack, “Advances in investment casting of Ti – 6Al – 4V alloy : a review,” vol. 19, no. 2, pp. 73–93, 2006, doi: 10.1179/136404605225023225. [Google Scholar]
  75. S. Liu and Y. C. Shin, “Additive manufacturing of Ti6Al4V alloy : A review,” Mater. Des., vol. 164, p. 107552, 2019, doi: 10.1016/j.matdes.2018.107552. [CrossRef] [Google Scholar]
  76. S. Gholizadeh, “ScienceDirect ScienceDirect ScienceDirect A review of non-destructive testing methods of composite materials Thermomechanical modeling of a high pressure turbine blade of an airplane gas turbine engine,” Procedia Struct. Integr., vol. 1, pp. 50–57, 2016, doi: 10.1016/j.prostr.2016.02.008. [CrossRef] [Google Scholar]
  77. S. Kumar, M. Vishwakarma, and P. Akhilesh, “ScienceDirect Advances and Researches on Non Destructive Testing : A Review,” vol. 5, pp. 3690–3698, 2018. [Google Scholar]
  78. P. Tao, H. Shao, Z. Ji, H. Nan, and Q. Xu, “Numerical simulation for the investment casting process of a large-size titanium alloy thin-wall casing,” Prog. Nat. Sci. Mater. Int., vol. 28, no. 4, pp. 520–528, 2018, doi: 10.1016/j.pnsc.2018.06.005. [CrossRef] [Google Scholar]
  79. H. Huaishu, Y. Xiao Dong, L. Ding, F. Xinchong, and S. Jicai, “Detection and Analysis of Metal Induction Hardened Layer Based on Ultrasonic Technology,” J. Phys. Conf. Ser., vol. 1965, no. 1, pp. 5–9, 2021, doi: 10.1088/1742-6596/1965/1/012146. [CrossRef] [Google Scholar]
  80. F. Honarvar and A. Varvani-Farahani, “A review of ultrasonic testing applications in additive manufacturing: Defect evaluation, material characterization, and process control,” Ultrasonics, vol. 108, no. February, p. 106227, 2020, doi: 10.1016/j.ultras.2020.106227. [CrossRef] [Google Scholar]
  81. H. Chen, M. Liu, Z. Hu, M. Li, and S. Li, “Mechanical structural health prognosis with nonlinear mixed frequency ultrasonic signal analysis,” vol. 01075, pp. 1–10, 2021. [Google Scholar]
  82. R. Ahmad, N. A. Talib, and M. B. A. Asmael, “Effect of pouring temperature on microstructure properties of Al-Si LM6 alloy sand casting,” Appl. Mech. Mater., vol. 315, no. June, pp. 856–860, 2013, doi: 10.4028/www.scientific.net/AMM.315.856. [CrossRef] [Google Scholar]

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