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Intelligent system supporting technological process planning for machining and 3D printing

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The study aimed to develop a system supporting technological process planning for machining and 3D printing. Such a system should function similarly to the way human experts act in their fields of expertise and should be capable of gathering the necessary knowledge, analysing data, and drawing conclusions to solve problems. This could be done by utilising artificial intelligence (AI) methods available within such systems. The study proved the usefulness of AI methods and their significant effectiveness in supporting technological process planning. The purpose of this article is to show an intelligent system that includes knowledge, models, and procedures supporting the company’s employees as part of machining and 3D printing. Few works are combining these two types of processing. Nowadays, however, these two types of processing overlap each other into a common concept of hybrid processing. Therefore, in the opinion of the authors, such a comprehensive system is necessary. The system-embedded knowledge takes the form of neural networks, decision trees, and facts. The system is presented using the example of a real enterprise. The intelligent expert system is intended for process engineers who have not yet gathered sufficient experience in technological-process planning, or who have just begun their work in a given production enterprise and are not very familiar with its machinery and other means of production.
Rocznik
Strony
art. no. e136722
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
  • Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  • Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  • Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
autor
  • Faculty of Mechatronics, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  • Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
  • Faculty of Psychology, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
Bibliografia
  • [1] T. Pereira, J.V. Kennedy, and J. Potgieter, “A comparison of traditional manufacturing vs additive manufacturing, the best method for the job”, Procedia Manuf. 30, 11–18 (2019).
  • [2] S. Mirzababaei and S. Pasebani, “A Review on Binder Jet Additive Manufacturing of 316L Stainless Steel”, J. Manuf. Mater. Process 3, 82, 1–36 (2019).
  • [3] J-P. Kruth, M.C. Leu, and T. Nakagawa, “Progress in Additive Manufacturing and Rapid Prototyping”, CIRP Ann. 47(2), 525–540 (1998).
  • [4] J. Maszybrocka, B. Gapiński, M. Dworak, G. Skrabalak, and A. Stwora, “Modelling, manufacturability and compression properties of the CpTi grade 2 cellular lattice with radial gradient TPMS architecture”, Bull. Pol. Acad. Sci. Tech. Sci. 67(4), 719–727 (2019).
  • [5] E. Talhi, J-C. Huet, V. Fortineau, and S. Lamouri, “A methodology for cloud manufacturing architecture in the context of industry 4.0”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 271–284 (2020).
  • [6] I. Rojek, D. Mikołajewski, P. Kotlarz, M. Macko, and J. Kopowski, “Intelligent System Supporting Technological Process Planning for Machining”, in: Machine Modelling and Simulations MMS 2020. Lecture Notes in Mechanical Engineering. Springer, Cham, (to be published).
  • [7] W. Grzesik, “Hybrid machining processes. Definitions, generation rules and real industrial importance”, Mechanik 5–6, 338‒342 (2018), [in Polish].
  • [8] C.F. Tan, V.K. Kher, and N Ismail, “An expert system carbide cutting tools selection system for CNC lathe machine”, Int. Rev. Mech. Eng. 6(7), 1402–1405 (2012).
  • [9] I. Rojek, E. Dostatni, and A. Hamrol, “Ecodesign of Technological Processes with the Use of Decision Trees Method”, in International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO 2017, CISIS 2017, ICEUTE 2017. Advances in Intelligent Systems and Computing, vol. 649, pp. 318–327, eds. H. Pérez García, J. Alfonso-Cendón, L. Sánchez González, H. Quintián and E. Corchado, Springer, Cham, 2018.
  • [10] G. Halevi and K. Wang, “Knowledge based manufacturing system (KBMS)”, J. Intell. Manuf. 18(4), 467–474 (2007).
  • [11] S. Butdee, Ch. Noomtong, and S. Tichkiewitch, “A Process Planning System with Feature Based Neural Network Search Strategy for Aluminum Extrusion Die Manufacturing”, Asian Int. J. Sci. Technol. Prod. Manuf. Eng. 2(1), 137–157 (2009).
  • [12] I. Rojek, “Hybrid neural networks as prediction models”, in Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science ICAISC 2010, vol. 6114, pp. 88–95, eds. L. Rutkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, and J.M. Zurada, Springer, Berlin, Heidelberg, 2010.
  • [13] D. Rajeev, D. Dinakaran, and S. Singh. “Artificial neural network based tool wear estimation on dry hard turning processes of aisi4140 steel using coated carbide tool”, Bull. Pol. Acad. Sci. Tech. Sci. 65(4), 553–559 (2017).
  • [14] I. Rojek, “Classifier Models in Intelligent CAPP Systems”, in Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol. 59, pp. 311–319, eds. K.A. Cyran, S. Kozielski, J.F. Peters, U. Stańczyk, and A. Wakulicz-Deja, Springer, Berlin, Heidelberg, 2009.
  • [15] S. Igari, F. Tanaka, and M. Onosato, “Customization of a Micro Process Planning System for an Actual Machine Tool based on Updating a Machining Database and Generating a Database-Oriented Planning Algorithm”, J. Trans. Inst. Syst. Control Inf. Eng. 26(3), 87–94 (2013).
  • [16] I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in eco-design”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 199–206 (2020).
  • [17] M. Hazarika, S. Deb, U.S. Dixit, and J.P. Davim, “Fuzzy setbased set-up planning system with the ability for online learning”, Proc. Inst. Mech. Eng. Part B-J. Eng. Manuf. 225(2), 247–263 (2011).
  • [18] N. Guo and M.C. Leu, “Additive manufacturing: Technology, applications and research needs”, Front. Mech. Eng. 215–243 (2013).
  • [19] J. Yang, Y. Chen, W. Huang, and Y. Li, “Survey on artificial intelligence for additive manufacturing”, in 23rd International Conference on Automation and Computing (ICAC), Huddersfield, 2017, pp. 1–6, doi: 10.23919/IconAC.2017.8082053.
  • [20] I.J. Petrick and T.W. Simpson, “3D printing disrupts manufacturing: how economies of one create new rules of competition”, Res.-Technol. Manage. 56(6), 12–16 (2013).
  • [21] B. Stucker, “Additive manufacturing technologies: Technology introduction and business implications”, in Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2011 Symposium, pp. 19–21, National Academies Press: Washington, DC, USA, 2012.
  • [22] Y. Wang, P. Zheng, and T. Peng, “Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives”, Sci. China Technol. Sci. 1–12 (2020).
  • [23] H. Chen, and Y.F. Zhao, “Process parameters optimization for improving surface quality and manufacturing accuracy of binder jetting additive manufacturing process”, Rapid Prototyp. J. 22, 527–538 (2016).
  • [24] M.A. Kaleem and M. Khan, “Significance of Additive Manufacturing for Industry 4.0 With Introduction of Artificial Intelligence in Additive Manufacturing Regimes”, in 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 2020, pp. 152–156, doi: 10.1109/ IBCAST47879.2020.9044574.
  • [25] L. Meng, B. McWilliams, and W. Jarosinski, “Machine Learning in Additive Manufacturing” A Review. JOM 72, 2363–2377 (2020).
  • [26] Z. Jin, Z. Zhang, and G.X. Gu, “Automated Real-Time Detection and Prediction of Interlayer Imperfections in Additive Manufacturing Processes Using Artificial Intelligence”, Adv. Intell. Syst. 2(1), 1900130(1–7) (2020).
  • [27] K. Wasmer, C. Kenel, C. Leinenbach, and S.A. Shevchik, “In Situ and Real-Time Monitoring of Powder-Bed AM by Combining Acoustic Emission and Artificial Intelligence.”, in Industrializing Additive Manufacturing – Proceedings of Additive Manufacturing in Products and Applications – AMPA2017, pp. 200–209, eds. M. Meboldt and C. Klahn, Springer, Cham, 2018, https://doi.org/10.1007/978-3-319-66866-6_20.
  • [28] C. Wang, S Li, D Zeng, and X. Zhu, “An Artificial-intelligence/ Statistics Solution to Quantify Material Distortion for Thermal Compensation in Additive Manufacturing”, Cornell University, arXiv:2005.09084v1 [cs.CE], 2020.
  • [29] P. Hong-Seok and N. Dinh-Son, “AI-Based Optimization of Process Parameters in Selective Laser Melting”, in Advances in Manufacturing Technology XXXII, eds. P. Thorvald and K. Case, IOS Press, 2018, doi: 10.3233/978-1-61499‒902-7-119.
  • [30] J. Kopowski, D. Mikołajewski, M. Macko, and I. Rojek, “Bydgostian hand exoskeleton – own concept and the biomedical factors”, Bio-Algorithms and Med-Systems 15(1), 20190003 (2019).
  • [31] J. Kopowski, I. Rojek, D. Mikołajewski, and M. Macko, “3D Printed Hand Exoskeleton – Own Concept”, in Advances in Manufacturing II. MANUFACTURING 2019. Lecture Notes in Mechanical Engineering, pp. 306‒298, J. Trojanowska, O. Ciszak, J. Machado, and I. Pavlenko, Springer, Cham, 2019, https://doi.org/10.1007/978-3-030-18715-6_25.
  • [32] R. Tadeusiewicz, R. Chaki, and N. Chaki, “Exploring Neural Networks with C#”, CRC Press Taylor & Francis Group, Boca Raton, 2014.
  • [33] L.A. Zadeh, “Fuzzy sets. Information and Control”, 8, pp. 338–353 (1965).
  • [34] S. Jige Quan, J. Park, A. Economou, and S. Lee, “Artificial intelligence-aided design: Smart Design for sustainable city development,” Environment and Planning B 46(8), 1581‒1599 (2019).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0e4dde24-8a38-4c02-aac2-6e51bcab20ea
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