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Title

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

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

2

Authors

Rojek, Izabela ; Mikołajewski, Dariusz ; Kotlarz, Piotr ; Macko, Marek ; Kopowski, Jakub

Affiliation

Rojek, Izabela : Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland ; Mikołajewski, Dariusz : Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland ; Kotlarz, Piotr : Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland ; Macko, Marek : Faculty of Mechatronics, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland ; Kopowski, Jakub : Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland ; Kopowski, Jakub : Faculty of Psychology, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland

Keywords

artificial intelligence ; intelligent system ; technological process ; machining ; 3D printing

Divisions of PAS

Nauki Techniczne

Coverage

e136722

Bibliography

  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
  14. steel using coated carbide tool”, Bull. Pol. Acad. Sci. Tech. Sci. 65(4), 553–559 (2017).
  15.  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.
  16.  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).
  17.  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).
  18.  M. Hazarika, S. Deb, U.S. Dixit, and J.P. Davim, “Fuzzy set-based set-up planning system with the ability for online learning”, Proc. Inst. Mech. Eng. Part B-J. Eng. Manuf. 225(2), 247–263 (2011).
  19.  N. Guo and M.C. Leu, “Additive manufacturing: Technology, applications and research needs”, Front. Mech. Eng. 215–243 (2013).
  20.  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.
  21.  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).
  22.  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.
  23.  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).
  24.  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).
  25.  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.
  26.  L. Meng, B. McWilliams, and W. Jarosinski, “Machine Learning in Additive Manufacturing” A Review. JOM 72, 2363–2377 (2020).
  27.  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).
  28.  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.
  29.  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.
  30.  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.
  31.  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).
  32.  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.
  33.  R. Tadeusiewicz, R. Chaki, and N. Chaki, “Exploring Neural Networks with C#”, CRC Press Taylor & Francis Group, Boca Raton, 2014.
  34.  L.A. Zadeh, “Fuzzy sets. Information and Control”, 8, pp. 338–353 (1965).
  35.  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).

Date

08.03.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.136722

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; 69; 2; e136722
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