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Vol 14, 2025
Pages: 303 - 310
Professional scientific paper
Engineering, Technology and Materials Editor: Darjana Sredić
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Received: 16.09.2025. >> Accepted: 30.10.2025. >> Published: 21.11.2025. Professional scientific paper Engineering, Technology and Materials Editor: Darjana Sredić

ARTIFICAL INTELLIGENCE SUPPORTED APPLICATION FOR EXPLOSIVE CLADDING PROCESS SPECIFICATION

By
Zoltán Nyikes ,
Zoltán Nyikes
Contact Zoltán Nyikes

DEPARTMENT OF INFORMATICS, Milton Friedman University, , Budapest , Hungary

Tünde Anna Kovács
Tünde Anna Kovács

Faculty of Mechanical and Safety Engineering,, Óbuda University , Budapest , Hungary

Abstract

Explosive cladding is becoming increasingly widespread in the field of metalworking
technologies. The advantage of this technology is that it cannot be combined with other
welding technologies, and dissimilar metals can be joined by cohesion joint. The wide range
of materials and the different properties of metallic materials (modulus of elasticity, tensile
strength, hardness, ductility, etc.) are the reasons for the difficulty of determining the welding
process specification. In addition, many explosives (with blast velocities below the speed of
sound) are suitable for creating the appropriate bond strength. AI is a good tool for several
applications and process parameter calculations. The innovative application supported by AI
can help the welding engineer in the explosive welding process parameter determinations.
For the welding process, the engineer chooses suitable metal and explosive materials. AI,
based on the explosive material parameters and the metallic materials' mechanical properties,
calculate the explosive welding setup parameters. In this article, the algorithm of the
application and the theoretical and practical elements of the technological design are
presented in detail. The developed application facilitates the technological design of the
otherwise complex blast welding process.

References

Alwazae, M. M., Perjons, E., & Kjellin, H. (2014). Quality measures for documentation of best practices. 2014 47th Hawaii International Conference on System Sciences, 3410–3419.
Arbaz, A., Fan, H., Ding, J., Qiu, M., & Feng, Y. (2024). GenFlowchart: parsing and understanding flowchart using generative AI. International Conference on Knowledge Science, Engineering and Management, 99–111.
ASM Handbook. (1992). Welding, Brazing and Soldering. ASM International, 6, 525–537.
Catovic, A. (2020). An overview of the Gurney method for estimating the initial velocity of fragments for high explosive munition. Defence Secur Stud, 1(1), 16–28.
Davis, J. R. (2002). Surface Hardening of Steels. ASM International, 1-16.
Ejiwale, J. (2014). Facilitating collaboration across science, technology, engineering and mathematics (STEM) fields in program development. Journal of STEM Education, 15(2), 151097.
Findik, F. (2011). Recent developments in explosive welding. Materials & Design, 32(3), 1081–1093. https://doi.org/10.1016/j.matdes.2010.10.017
Fucheng, Z., Lv, B., Wang, T., Zheng, C., Zhang, M., Luo, H. H., & Liu, H. (2008). Microstructure and Properties of Purity High Mn Steel Crossing Explosion Hardened. ISIJ International, 48, 1766–1770.
Goka, S., Narayana, G. S., Divya Jyothi, G., Shaik, H. S., & Moinuddin, S. Q. (2024). AI and ML in Welding Technologies. Automation in Welding Industry: Incorporating Artificial Intelligence. Machine Learning and Other Technologies, 73-90. .
Greenberg, B. A., Ivanov, M. A., Kuzmin, S. V., & Lysak, V. I. (2019). Explosive welding: processes and structures. CRC Press. .
Greenhalgh, T., & Peacock, R. (2005). Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. Bmj, 331(7524), 1064–1065.
Grønbæk, K., Grudin, J., Bødker, S., & Bannon, L. (2017). Achieving cooperative system design: shifting from a product to a process focus. In Participatory Design (pp. 79-97). CRC Press.
Havlícek, P., & Busóvá, K. (2012). Experience with explosive hardening of railway frogs from Hadfield steel, Metal, Brno, Czech Republic .
Hedman, E. (2015). Facilitating leadership team communication. In Jyväskylä studies in humanities (p. 266).
Hirsch, E. (1995). On the inconsistency of the asymmetric-sandwich gurney formula when used to model thin plate propulsion. Propellants, Explos, Pyrotech, 20, 178–181.
Holtzman, A.H., & Cowan , G. R. (1965). Bonding of metal with explosives, Welding Research Council bulletin. 1–21.
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83–111.
Jeon, S. M., & Kim, G. (2016). A survey of simulation modeling techniques in production planning and control (PPC. Production Planning & Control, 27(5), 360–377.
Kamaruzaman, F. M., Hamid, R., & Mutalib, A. A. (2017). A review on issues and challenges in incorporating complex engineering problems in engineering curriculum and proposed solutions. In 2017 7th World Engineering Education Forum (WEEF) (pp. 697-701). IEEE. .
Kovács-Coskun, T. (2016). Explosive Surface Hardening of Austenitic Stainless Steel. IOP Conference Series: Materials Science and Engineering, 123, 1–5.
Kugyela, L., Daruka, N., & Kovács, T. A. (2025). Explosive Welding of Metals for Electronic Applications. In Advanced Sciences and Technologies for Security Applications (pp. 445–459). https://doi.org/10.1007/978-3-031-78544-3_35
Liu, F. C., Lv, B., Zhang, F. C., & Yang, S. (2011). Enhanced work hardening in Hadfield steel during explosive treatment, Materials letters 65. Elsevier. pp. 2333-2336. .
Lukács, L., Szalay, A., & Zádor, I. (2012). Explosive forming and aerospace. Aeronautical Science Bullettins, XXIV(2), 431–446.
Meyers, M. A., & Murr, L. E. (1980). Shock Waves and High-Strain-Rate Phenomena in Metals. International Conference on Metallurgical Effects of High-Strain-Rate Deformation and Fabrication, 91–111.
Mousavi, A. A. A., & Al-Hassani, S. T. S. (2005). Numerical and experimental studies of the mechanism of the of the wavy interface formations in explosive/impact welding. J Mech Phys Solids, 53, 2501–2528.
Patange, G. S., & Pandya, A. B. (2023). How artificial intelligence and machine learning assist in industry 4.0 for mechanical engineers. Materials Today: Proceedings, 72, 622–625.
Perkusich, M., Silva, L. C., Costa, A., Ramos, F., Saraiva, R., Freire, A., & Perkusich, A. (2020). Intelligent software engineering in the context of agile software development: A systematic literature review. Information and Software Technology, 119, 106241.
Peters, N. W. (2005). The performance of Hadfield’s manganese steel as it relates manufacture. Conference Proceedings, 1–22.
Pival, P. R. (2023). How to incorporate artificial intelligence (AI) into your library workflow. Library Hi Tech News, 40(7), 15–17.
Pocalyko, A. (1964). Explosion clad plate for corrosion service. North Central Region Conference, 2–6.
Pocalyko, A. (1981). Metallic Coatings (Explosively Clad. Encyclopedia of Chemical Technology, 15, 275–296.
Quick, D., & Choo, K. K. R. (2014). Impacts of increasing volume of digital forensic data: A survey and future research challenges. Digital Investigation, 11(4), 273–294.
Rios, V. S., Avansi, G. D., & Schiozer, D. J. (2020). Practical workflow to improve numerical performance in time-consuming reservoir simulation models using submodels and shorter period of time. Journal of Petroleum Science and Engineering, 195, 107547.
Sadeghi-Bazargani, H., Bangdiwala, S. I., Mohammad, K., Maghsoudi, H., & Mohammadi, R. (2011). Compared application of the new OPLS-DA statistical model versus partial least squares regression to manage large numbers of variables in an injury case-control study. Sci Res Essays, 6(20), 4369–4377.
Standley, T. (2010). Finding optimal solutions to cooperative pathfinding problems. Proceedings of the AAAI Conference on Artificial Intelligence, 24(1), 173–178.
Staudhammer, K. P., Frantz, C. E., & Hecker, S. S. (1981). Shock Waves and High Strain Rate Phenomena in Metals, Plenum. 91–12.
Wasserman, A. I. (2010). Software engineering issues for mobile application development. Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, 397–400.
Weman, K. (2011). Welding processes handbook. Elsevier. .
Whitfield, C. A., Freuler, R. J., Allam, Y., & Riter, E. A. (2011). An overview of highly successful first-year engineering cornerstone design projects. Proceedings of the 2011 International Conference on Engineering Education, 21–26.
Zhang, F. C., Yang, Z. N., Qian, L.-H., Liu, F. C., Lv, B., & Zhang, M. (2011). High speed bounding: A novel technique for the preparation of a thick surface layer with a hardness gradient distribution on Hadfield steel. Scipta Materialia, 64, 560–563.
Zhang, M., Zhang, Bl., F., & Feng, X. (2012). Explosion Deformation and Hardening Behaviours of Hadfield Steel Crossing. ISIJ International, 52(11), 2093–2095.
Zhou, Z. H. (2021). Machine learning. Springer nature. .

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