Details

Title

Review of Data-driven Decision Support Systems and Methodologies for the Diagnosis of Casting Defects

Journal title

Archives of Foundry Engineering

Yearbook

2024

Volume

Accepted articles

Authors

Affiliation

Burzyńska, A. : University of Warmia and Mazury in Olsztyn, Poland

Keywords

Casting defects ; Quality 4.0. ; Digital transformation ; Zero defects manufacturing ; Smart manufacturing systems

Divisions of PAS

Nauki Techniczne

Publisher

The Katowice Branch of the Polish Academy of Sciences

Bibliography

  1. Masri, N., Sultan, Y., Akkila, A. N., Almasri, A., Ahmed, A., Mahomud, A. Y., Zaqout, I. & Abu-Naser, S.S. (2019). Survey of rule_based systems. International Journal of Academic Information Systems Research (IJAISR). 3(7), 1-22.
  2. Tsujioka, Y., Akmal, S., Takada, Y., Kawai, H., & Batres, R. (2012) Semantic similarity for case-based reasoning in the context of GMP. Computer Aided Chemical Engineering. 31, 830-834, https://doi.org/10.1016/B978-0-444-59507-2.50158-X.
  3. Maynard, A.D. (2015). Avigating the fourth industrial revolution. Nature Nanotechnology. 10(12), 1005-1006. https://doi.org/10.1038/nnano.2015.286.
  4. Ślusarczyk, B. (2018). Industry 4.0: Are we ready? Polish Journal Manageement Studies. 17(1), 232-248. DOI:10.17512/pjms.2018.17.1.19.
  5. Sarker, I.H. (2022). Al-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science. 3, 158, 157-159. DOI:10.1007/s42979-022-01043-x.
  6. Grzegorzewski, P. & Kochański, A. (2018). From data to reasoning. In P. Grzegorzewski, A. Kochanski, J. Kacprzyk (Eds.), Soft modeling in industrial manufacturing (pp. 15-25). Springer Cham.
  7. Maheshwera, U., Paturi R. & Cheruku S. (2021). Application and performance of machine learning techniques in manufacturing sector from the past two decades. A review, Materials Today: Proceedings. 38(5), 2392-2401. https://doi.org/10.1016/j.matpr.2020.07.209.
  8. Too, F., Qi, A., Liu, A. & Kusiak, A. (2018). Data-driven smart manufacturing. Journal Manufacturing Systems. 48, 157-169. DOI: 10.1016/j.jmsy.2018.01.006.
  9. Kuo Y. & Kusiak A. (2017) From data to big data in production research: the past and furure trends. International Journal of Production Research. 57(15-16), 4828-4853. DOI: 10.1080/00207543.2018.1443230.
  10. Koksal, G., Batmaz, I., Testik, M.C. (2011). A review of data mining applications for quality improvement in manufacturing industry (review). Expert Systems with Applications. 38(10), 13448-13467. DOI: 10.1016/j.eswa.2011.04.063
  11. (2016). Industry 4.0. – is it all about industrial data and analytics. Retrieved June 15, 2024, from https://www.i-scoop.eu/industry-4-0/industrial-data-analytics/
  12. Peres, R., Jia, X., Lee, J., Sun, K., Colombo, A.W. & Barata, J. (2020). Industrial artificial intelligence in Industry 4.0. – systematic review, challenges and outlook. Smart Manufacturing IEEE Access. 8, 220121-220139. DOI: 10.1109/access.2020.3042874.
  13. Zavalishina, J., (2016) Manufacturing and Fourth Revolution, Control Engineering, Cybersecurity. Retrieved June 15, 2024, from https://www.controleng.com/articles/manufacturing-and-the-fourth-revolution/
  14. Perzyk, M., Dybowski, B. & Kozłowski, J. (2019). Introducing advanced data analytics in perspective of industry 4.0. in die casting foundry. Archives of Foundry Engineering. 19(1), 53-57. DOI: 10.24425/afe.2018.125191.
  15. Perzyk, M., Kozłowski, J. & Wisłocki, M., (2013). Advanced methods of foundry processes control. Archives of Metallurgy and Materials. 58(3), 899-902. DOI: 10.2478/amm-2013-0096
  16. Tariq, S., Tariq, A., Masud, M. & Rehman, Z. (2021). Minimizing the casting defects in high pressure die casting using taguchi analysis. Scientia Iranica. 29(1), 53-69. DOI:10.24200/sci.2021.56545.4779.
  17. Chongwatpol, J. (2015). Prognostic analysis of defects in manufacturing. Industrial Management & Data Systems. 115(1), 64-87. DOI: 10.1108/IMDS-05-2014-0158.
  18. Dargusch, M.S., Dour, G., Schauer, N., Dinnis, C.M. & Savage, G. (2006). The influence of pressure during solidification of high pressure die cast aluminium telecommunications components. Journal of Materials Processing Technology. 180(1-3), 37-43. https://doi.org/10.1016/j.jmatprotec.2006.05.001.
  19. Okuniewska, A., Perzyk, M.A. & Kozłowski, J. (2021). Methodology for diagnosing the causes of die-casting defects, based on advanced big data modelling. Archives of Foundry Engineering. 21(4), 103-109. DOI: 10.24425/afe.2021.138687.
  20. Adamane, A.R., Arnberg, L., Fiorese, E., Timelli, G. & Bonollo, F. (2015). Influence of injection parameters on the porosity and tensile properties of high-pressure die cast Al-Si alloys: A Review. International Journal of Meterials. 9(1), 43-53. https://doi.org/10.1007/BF03355601.
  21. Obregon, J. & Jung, J. (2024). Rule based visualization of faulty process conditions in the die-casting manufacturing. Journal of Intelligent Manufacturing. 35(2), 521-537. DOI:10.1007/s10845-022-02057-1.
  22. Biernacki, R., Myszka, D. (2005). Examination of casting defects. Retrieved June 15, 2024, from https://www.scribd.com/document/501206908/Badanie-Wad-Odlewow-%C4%87w19
  23. MetalTek International. (2024). The beginners guide to metal casting defects. Retrieved June 8, 2024, from https://www.metaltek.com/blog/the-beginners-guide-to-metal-casting-defects
  24. (2024). Causes and prevention of porosity defects in castings, steel casting foundry, china dawang steel casting company. Retrieved June 8, 2024, from https://dawangcasting.com/
  25. ASM Committee on Nondestructive Inspection of Casting. (2024). International classification of casting defects, Solutions Fonderie. Retrieved June 30, 2024, from https://www.solutionsfonderie.com
  26. Mozammil, S., Karloopia, J. & Jha, P.K. (2018). Investigation of porosity in Al casting. Materials Today: Proceedings. 5(1), 17270-17276. DOI: 10.1016/j.matpr.2018.04.138.
  27. Gursoy, O., Nordmak, A., Syversten, F., Colak, M., Tur, K. & Dispinar, D. (2021). Role of metal quality and porosity formation in low pressure die casting of A356: experimental obsercations. Archives of Foundry Engineering. 21(1), 5-10. DOI: 10.24425/afe.2021.136071.
  28. Rapid Direct. (2022). Porosity in die casting: how to prevent them. Retrieved June 23, 2024, from www.rapiddirect.com/blog/porosity-in-die-casting/
  29. Jackowski, J. (2018). A mechanism of porosity formation in metal composite casts with saturated reinforcement. Retrieved June 23, 2024, from http://composites.ptmk.net/article,a-mechanism-of-porosity-formation-in-metal
  30. Kaufman, J.G., Rooy, E.L. (2004). Aluminium alloy castings: properties. Processes and Applications. US of America: ASM International.
  31. Felberbaum, M., Landry-Desy, E., Weber, L. & Rappaz, M. (2011). Effective hydrogen diffusion coefficient for solidifying aluminium alloys. Acta Materialia. 59(6), 2302-2308. https://doi.org/10.1016/j.actamat.2010.12.022.
  32. Kendrick, R., Muneratti, G., Consoli, S., Voltazza, F. & Barison, S. (2012). The use of metal treatment to control the quality of an aluminium casting produced by the high-pressure die-casting process. Metall Science and Technology. 2(30), 3-11.
  33. Khrychikov, V., Semenov, O., Meniailo, H., Aftamdiliants, Y. & Gnyloskurenko, S. (2022). The process of vacuum formation in the shrinkage cavity at castings crystallization. Archives of Foundry Engineering. 22(4), 79-84. DOI: 10.24425/afe.143953.
  34. Chelladurai, C., Mohan, N.S., Hariharashayee, D., Manikandan, S. & Sivaperumal, P. (2021). Analyzing the casting defects in small scale casting industry. Materials Today: Proceedings. 37(2), 386-394. DOI: 10.1016/j.matpr.2020.05.382.
  35. Papanikolaou, M. & Saxena, P. (2021). Chapter 7 - Sustainable casting processes through simulation-driven optimization. Sustainable Manufacturing. 165-198. DOI: 10.1016/B978-0-12-818115-7.00003-1.
  36. Goover, M.P. (2010). Fundamentals of Modern Manufacturing: Materials, Processes, and Systems. Wiley.
  37. Haworth Casting. (2018). The differences between cold shuts and misruns. Retrieved June 23, 2024, from www.haworthcasting.co.uk
  38. Kacprzyk, J., Zadrożny, S. (2010). Modern data-driven decision support systems: the role of computing with words and computational linguistics. International Journal of General Systems. 39(4), 379-393. DOI:10.1080/03081071003706618.
  39. Marjanovic O. (2013) Improving Data-Driven Decision Making through Human-Centered Knowledge Sharing, Retrieved June 23, 2024, from
    https://aisel.aisnet.org/acis2013/125
  40. Holsapple, C.W., Whinston, A.B. (1996). Decision support systeems: a knowledge-based approach. Minneapolis: West Publishing.
  41. Power, D. (2006). What was the first computerized decision support system (DSS)? Retrieved June 23, 2024, from https://dssresources.com/faq/index.php?action=artikel&id=186
  42. Power, D. (2007). A brief history of decision support systems, DSSResources.com. Retrieved June 23, 2024, from https://dssresources.com/history/dsshistory.html
  43. Power, D.J., Heavin, C. (2017). Decision support analytics and business intelligence (third Edition). ISBN:1631573918 Business Expert Press.
  44. Kapadia, R., Stalney, G., Walker, M.G. (2007). Real Word Model-based Fault Management. Retrieved June 23, 2024, from https://gregstanleyandassociates.com/dx07-final-submission.pdf
  45. Silva Perez R., et al. (2020) Industrial artificial intelligence in industry 4.0. – systematic review, challenges and outlook. IEEE Access. 8, 22021-220139. DOI: 10.1109/access.2020.3042874.
  46. Codd, E.F., Codd, S.B., Salley, C.T. (1993). Providing OLAP to User-Analysts: An IT Mandate. Arbor Software Corporation. Retrieved June 23, 2024, from http://www.estgv.ipv.pt/paginaspessoais/jloureiro/esi_aid2007_2008/fichas/codd.pdf
  47. Power, D. (2008). Understanding data-driven decision support systems. Information Systems Management. 25(2), 149-154. https://doi.org/10.1080/10580530801941124.
  48. Kareska, K. (2024). Kaizen in manufacturing: transforming productivity and quality through continuous improvement. Available at SSRN. DOI: 10.2139/ssrn.4844999.
  49. Kochanski A., Kozlowski J., Perzyk M. & Sadłowska, H. (2024). Data-driven advisory systems for industrial manufacturing. Application to the aluminium extrusion process. Knowledge Based Systems. 294, 111631, 1-22. DOI:10.1016/j.knosys.2024.111631.
  50. Delen, D., Sharda, R. (2008). Artificial Neural Networks in Decision Support Systems. In: Handbook on Decision Support Systems 1. International Handbooks Information System. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48713-5_26
  51. Perzyk, M., Kochanski, A. & Kozlowski, J. (2022). Fundaments of recommendation system for the aluminum extrusion process based on data-driven modeling. Computer Methods in Materials Science. 22(4), 173-188. DOI:10.7494/cmms.2022.4.0782.
  52. Okuniewska, A., Perzyk, M. & Kozlowski, J. (2023). Machine learning methods for diagnosing the causes of die-casting defects. Computer Methods in Materials Science. 23(2), 45-56. DOI: 10.7494/cmms2023.2.0809.
  53. Wilk-Kołodziejczyk, D., Rojek, G., Regulski, K. (2014). The decision support system in the domain of casting defects diagnosis. Archives of Foundry Engineering. 14(3), 107-110. DOI: 10.2478/afe-2014-0072.
  54. Zhao, Y., Qian, F., Gao, Y. (2018). Data Driven Die Casting Smart Factory Solution: First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Chongqing, China, September 21-23, 2018, Proceedings, Part I. 10.1007/978-981-13-2396-6_2.
  55. Kim, J.S., Kim, J., Lee, J.Y. (2020). Die-casting defect prediction and diagnosis system using process condition data. Procedia Manufacturing. 51, 359-364. DOI: 10.1016/j.promfg.2020.10.051.
  56. Kim, J., Kang, H.S., Lee, J.Y. (2020). Development of intelligence data analytics system for quality enhancement of die-casting process. Journal of Korean Society for Precision Engineering. 37(4), 247-254. http://doi.org/10.7736/JKSPE.019.136.
  57. Zhang Y., Gao Z., Sun J. & Liu L. (2023) Machine-learning algorithms for process condition data-based inclusion prediction in continuous-casting process: a case study. 23(15), 1-17. https://doi.org/10.3390/s23156719.
  58. Saravanan, N., Siddabattuni, V.N.S.K., Ramachandran & K.I. (2010). Fault diagnosis of spur bevel gear box using artificial neural network (ANN) and proximal support vector machune (PSVM). Applied Soft Computing. 10(1), 344-360. https://doi.org/10.1016/j.asoc.2009.08.006.
  59. Chorowski, J., Wang, J. & Zurada, J.M. (2014). Review and performance comparison of SVM and ELM-based classifiers. Neurocomputing. 128, 507-516. https://doi.org/10.1016/j.neucom.2013.08.009.
  60. Menezes, A.G.C., Araujo, M.M., Almeida, O.M., Barbosa, F.R. & Braga, A.P.S. (2022). Induction to decision trees to diagnose incipient faults in power transformers. IEEE Transactions on Dielectrics and Electrical Insulation. 29(1), 279-286. DOI: 10.1109/TDEL.2022.3148453.
  61. Kramer, O. (2013). Dimensionality reduction with unsupervised nearest neighbors. Intelligent Systems Reference Library. 51, 13-23.
  62. Schapire, R.E. (1990). The strengh of weak learnability. Machine Learning. 5, 197-227.
  63. Jou, Y.T., Silitonga, R.M. & Sukwadi, R. (2023). A study on the construction of die-casting production prediction model by machine learning with Taguchi methods. Journal of the Chinese Institute of Engineers. 46(5), 540-550. https://doi.org/10.1080/02533839.2023.2204880.
  64. Omar, F., Sohrab, H., Saad, M., Hameed, A., & Bakhsh, F. I. (2022, January). Deep learning binary-classification model for casting products inspection. In 2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC) (pp. 1-6). IEEE.
  65. Purushothaman, H. (2022). Defect inspection of casting product surface using CNN. International Journal of Research in Engineeing and Science (IJRES). 10(10).
  66. Papagianni, Z., Vosniakos, G.-Ch. (2022). Surface Defects Detection on Pressure die casting by machine learning  exploiting machine vision features. In Design, Simulation, Manufacturing: The Innovation Exchange (pp. 51-61). Cham: Springer International Publishing.
  67. Pandey, A., Kumar, A. (2022). Casting fault detection by deep convolutional neural networks. In 2nd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), 11-12 November 2022. Bhubaneswar, India.
  68. Fu, J.-L. Shen, K. (2021). Automated detection of defects with casting dr image based on deep learning. In IEEE Far East NDT New Technology & Application Forum (FENDT), 14-17 December 2021. Kunming, China. DOI: 10.1109/FENDT54151.2021.9749682.
  69. Duan, L., Yang, K. & Ruan, L. (2021). Research on automatic recognition of casting defects based on deep learning. IEEE Access. 9, 12209-12216. DOI: 10.1109/ACCESS.2020.3048432.
  70. Maheswari, M., Brintha, N.C. (2024). A survey on detection of various casting defects using deep learning techniques. In 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), January 2024. Bengaluru, India. DOI: 10.1109/IDCIoT59759.2024.10467829.
  71. Awtoniuk, M., Majerek, D., Myziak, A. & Gajda, C. (2022). Industrial application of deep neural network for aluminum casting defect detection in case of unbalanced dataset. Advances in Science and Technology Research Journal. 16(5), 120-128. DOI: 10.12913/22998624/154963.
  72. Konovalenko, I., Maruschak, P., Brezinowa, J. & Prentkovskis, O. (2023). Research of U-Net-Based CNN architectures for metal surface defect detection. Machines. 10(5), 327, 1-19. DOI: 10.3390/machines10050327.
  73. Neven, R. & Goedeme, T.A. (2021). Multi-branch U-Net for steel surface defect type and severity segmentation. Metals. 11(6), 870, 1-19. https://doi.org/10.3390/met11060870.
  74. Tao, X., Zhang, D., Ma, W., Liu, X. & Xu, D. (2018) Automatic metallic sufrace defect detection and recognition with convolutional neural networks. Applied Sciences. 8(9), 1575, 1-15. https://doi.org/10.3390/app8091575.
  75. Norrena, J., Louhenkilpi, S., Visuri, V., Alatravas, T. (2024). Coupling of solidification and heat transfer simulations with interpretable machine learning algorithms to predict transverse cracks in continuous casting of steel. Steel Research International. 95(4), 2300529, 1-16. DOI:10.1002/srin.202300529
  76. Shikun, C. & Kaufmann, T. (2021). Development of data-driven machine learning models for the prediction of casting surface defects. Metals. 12(1), 1-15. DOI: 10.3390/met12010001.
  77. Shrivastava, S., Banerjee, D., Kumar, M., Rawat, R. (2024). A unified framework for casting defects classificaion: CNN meets random forest. In International Conference on Intelligent Systems for Cybersecurity (ISCS), 3-4 May 2024 (pp. 1-6). IEEE. DOI: 10.1109/ISCS61804.2024.10581352.
  78. Shrivastava, S., Banerjee, D., Unadhay, D., Dangi, S. (2024). Casting defect forecasting with integrated convolutional neural networks and random forest. In International Conference on Intelligent Systems for Cybersecurity (ISCS), Gurugram, India (pp. 1-6). DOI: 10.1109/iscs61804.2024.10581136.
  79. Frayman, Y. & Nahavandi, S. (2006). Machine vision system for automatic inspection of surface defects in aluminum die casting. Journal of Advanced Computational Intelligence and Intelligent Informatics. 10(3), 281-286. DOI:10.20965/jaciii.2006.p0281.
  80. Dougherty, E.R. (1992). An introduction of morphological image processing. Washington: SPIE Press, Bellingham, USA.
  81. Qung, Z., Hao, J., Yongwei, N. & Wei-Shi, Z. (2023). Pyramid texture filtering. ACM Transactions on Graphics (TOG). 42(4). DOI: 10.1145/3592120.
  82. T., Kralova. Y. & Hampl. J. (2015). Expert system for analysis of casting defects ESVOD. Archives of Foundry Engineering. 15(1), 17-20. DOI: 10.1515/afe-2015-0004.
  83. Sika, R., Rogalewicz, M., Popielarski, P. & Szymański, P. (2020). Decision support system in the field of defects assessment in the metal matrix composites castings. 13(16), 3552, 1-27. DOI: 10.3390/MA13163552.
  84. Liu, D., Du, Y., Chai, W., Lu, C., & Cong, M. (2022). Digital twin and data-driven quality prediction of complex die-casting manufacturing. IEEE Transactions of Industrial Informatics. 18(11), 2881-8128. DOI: 10.1109/TII.2022.3168309.
  85. Lee, J., Singh, J., Azamfar, M. (2019). Industrial artificial intelligence. Intelligent Maintenance Systems. Retrieved 23 June 2024 from https://www.researchgate.net/publication/335013398_Industrial_Artificial_Intelligence, DOI: 10.48550/arXiv.1908.02150
  86. Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X. (2021). Digital twins-based smart manufacturing system design in industry 4.0. a review. Journal of Manufacturing Systems. 60, 119-137. DOI: 10.1016/j.jmsy.2021.05.011.
  87. Grendys, A. (2021). Why factories need the digital tween? Platform of the future manufacturing. Retrieved June 18, 2024, from www.przemyslprzyszlosci.gov.pl.
  88. Zhang H, Liu, Q., Chen, X., Zhang, D., & Leng, J. (2017) A digital twin-based approach for design and multi-objective optimization of hollow glass production line. IEEE Access. 5, 26901-26911. DOI: 10.1109/ACCESS.2017.2766453.
  89. Mahmoud, M.A., Grace, J.A. (2019). A generic evaluation framework of smart manufacturing systems. Procedia Computer Science. 161, 1292-1299. https://doi.org/10.1016/j.procs.2019.11.244.

Date

30.12.2024

Type

Article

Identifier

DOI: 10.24425/afe.2024.151320
×