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Title

Modern methods in the field of machine modelling and simulation as a research and practical issue related to Industry 4.0

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

2

Affiliation

Rojek, Izabela : 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 ; Mikołajewski, Dariusz : Institute of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland ; Sága, Milan : Department of Applied Mechanics, Faculty of Mechanical Engineering, University of Zilina, 010 26 Zilina, Slovakia ; Burczyński, Tadeusz : Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B; 02-106 Warsaw, Poland

Authors

Rojek, Izabela ; Macko, Marek ; Mikołajewski, Dariusz ; Sága, Milan ; Burczyński, Tadeusz

Keywords

Industry 4.0 ; Internet of Things ; Artificial intelligence ; models ; simulation

Divisions of PAS

Nauki Techniczne

Coverage

e136717

Bibliography

  1.  Artificial intelligence quotient –2nd edition, Digital Poland Microsoft Foundation, 2019.
  2.  IoT in the Polish economy, Report of the working group for the Internet of Things at the Ministry of Digital Affairs, Ministry of Digital Affairs, 2019.
  3.  Towards the Industry 4.0, HBR Polska, ICAN, How to do IT, ASTOR, 2019.
  4.  Industry 4.0, The new industrial revolution, How Europe will succeed, Company Report Roland Berger, 2019.
  5.  S. Tay, L. Te Chuan, A. Aziati, and A. Nur Aizat “An Overview of Industry 4.0: Definition, Components, and Government Initiatives”, J. Adv. Res. Dyn. Control Syst. 10(14), 1379‒1387 (2018).
  6.  H. Kagermann, W. Wahlster, and H. Johannes, “Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0”, Forschungsunion, 2013.
  7.  J. Qin, Y. Liu, and R. Grosvenor, “Categorical Framework of Manufacturing for Industry 4.0 and Beyond”, Procedia CIRP 52, 173–178 (2016).
  8.  D. Gorecky, M. Schmitt, M. Loskyll, and D. Zühlke “Human-Machine-Interaction in the Industry 4.0 Era”, in Proc. 12th IEEE International Conference on Industrial Informatics, 2014, pp. 289–294.
  9.  Y. Liao, F. Deschamps, E.D. Freitas, and R. Loures, “Past, present and future of Industry 4.0 – a systematic literature review and research agenda proposal”, Int. J. Prod. Res. 55(12), 3609–3629 (2017)).
  10.  D. Mourtzis, “Simulation in the design and operation of manufacturing systems: state of the art and new trends”, Int. J. Prod. Res. 58(7), 1927‒1949 (2020), doi: 10.1080/00207543.2019.1636321.
  11.  E. Chlebus, CAx computer techniques in production engineering, WNT, Warsaw, 2000, [in Polish].
  12.  W. Paprocki, “The concept of Industry 4.0 and its application in the conditions of a market economy”, in Digitization of the economy and society. Opportunities and challenges for infrastructure sectors, pp. 39‒58, eds. J. Gajewski, W. Paprocki and J. Pieriegud, Research Institute for Market Economics – Gdansk Academy of Banking, Gdansk, 2016 [in Polish].
  13.  B. Denkena, J. Schmidt, and M. Krüger, “Data mining approach for knowledge-based process planning”, Procedia Technol. 15, 406‒415 (2014).
  14.  D. Trentesaux and A. Thomas, “Product-Driven Control: Concept, Literature Review and Future Trends”, in Orientation in Holonic and Multi Agent Manufacturing and Robotics, Studies in Computational Intelligence, vol. 472, pp. 135‒150, eds., T. Borangiu, A. Thomas, and D. Trentesaux, Springer, Heidelberg, 2013.
  15.  E. Uhlmann, E. Hohwieler, and M. Kraft, “Self-organizing production with distributed intelligence. Intelligent workpieces control their way through production”, Werkstattstechnik 103(2), 114‒117 (2013) [in German].
  16.  X. Xu, L. Wang, and S.T. Newman, “Computer-aided process planning –A critical review of recent developments and future trends”, Int. J. Comput. Integr. Manuf. 24(1), 1‒31 (2011).
  17.  U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases”, AI Magazine 17(3), 37‒54 (1996).
  18.  G. Köksal, I. Batmaz, and M.C. Testik, “A review of data mining applications for quality improvement in manufacturing industry”, Expert Syst. Appl. 38(10), 13448‒13467 (2011).
  19.  T. Sánchez López, D.C. Ranasinghe, B. Patkai, and D. McFarlane, “Taxonomy, technology and applications of smart objects”, Inf. Syst. Front. 13(2), 281‒300 (2009).
  20.  L. Wang, “Machine availability monitoring and machining process planning”, CIRP J. Manuf. Sci. Technol. 6(4), 263‒273 (2013).
  21.  S. Wang, X. Lu, X.X. Li, and W.D. Li, “A systematic approach of process planning and scheduling”, J. Clean Prod. 87(1), 914‒929 (2015).
  22.  V. Roblek, M. Meško, and A. Krapež, “A complex view of industry 4.0”, SAGE Open 6(2), 1–11 (2016).
  23.  X.G. Ming, J.Q. Yan, X.H. Wang, S.N. Li, W.F. Lu, Q.J. Peng, and Y.S. Ma, “Collaborative Process Planning and Manufacturing in Product Lifecycle Management”, Comput. Ind. 59(2‒3), 154‒166 (2008).
  24.  C.M. Gonzalez, “The future of artificial Intelligence domination”, Mechanical Engineering, The Magazine of ASME 8, 2020.
  25.  O. de Weck, D. Reed, S. Sarma, and M. Schmidt, Trends in Advanced Manufacturing Technology Research, MIT, 2013.
  26.  J. Nukeaw and W. Pecharpa, Advances in Material Science and Technology, vol. 802, TTP, 2013.
  27.  J. Zhou, “Digitalization and intelligentization of manufacturing industry”, Adv. Manuf. l(1), 1–7 (2013).
  28.  G. Lanza, B. Haefner, and A. Kraemer, “Optimization of selective assembly and adaptive manufacturing by means of cyber-physical system based matching”, CIRP Ann-Manuf. Technol.64(1), 399‒402 (2015).
  29.  L. Monostori, “Cyber-physical Production Systems: Roots, Expectations and R&D Challenges”, Procedia CIRP 17, 9‒13 (2014).
  30.  J. Becker, R. Knackstedt, and J. Pöppelbuß, “Developing Maturity Models for IT Management: A Procedure Model and its Application”, Bus. Inf. Syst. Eng. 1(3), 213‒222 (2009).
  31.  R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu, “Cloud-enabled prognosis for manufacturing” , CIRP Ann- Manuf. Technol.64(2), 749‒772 (2015).
  32.  R. Berger, “INDUSTRY 4.0 – The new industrial revolution”, [Online]. Available: www.rolandberger.com [Accessed: 21. Dec. 2015]
  33.  G. Schuh, T. Potente, R. Varandani, and T. Schmitz, “Global Footprint Design based on genetic algorithms – An ‘Industry 4.0’ perspective”, CIRP Ann-Manuf. Technol.63(1), 433‒436 (2014).
  34.  M. Kohlegger, R. Maier, and S. Thalmann, “Understanding Maturity Models Results of a structured Content Analysis”, presented at The 9th International Conference on Knowledge Management and Knowledge Technologies (IKNOW ‘09), Graz, Austria, 2009.
  35.  K. Lichtblau, V. Stich, R. Bertenrath, M. Blum, M. Bleider, A. Millack, K. Schmitt, E. Schmitz, and M. Schröter, IMPULS – Industrie 4.0- Readiness, Impuls-Stiftung des VDMA, Aachen-Köln, 2015.
  36.  E.W. Ngai, D.C.K. Chau, J.K.L. Poon, and C.K.M. To, “Energy and utility management maturity model for sustainable manufacturing process”, Int. J. Prod. Econ. 146(2), 453–464 (2013).
  37.  D.C.A. Pigosso, H. Rozenfeld, and T.C. McAloone, “Ecodesign maturi-ty model: a management framework to support ecodesign implementation into manufacturing companies”, J. Clean Prod.59, 160‒173 (2013).
  38.  M.A. Maasouman and K. Demirli, “Assessment of Lean Maturity Level in Manufacturing Cells”, IFAC-PapersOnLine 48(3), 1876‒1881 (2015).
  39.  M. Pérez-Lara et al., “Vertical and horizontal integration systems in Industry 4.0”, Wireless Netw. 26, 4767–4775 (2020).
  40.  A.C. Pereira, and F. Romero, “A review of the meanings and the implications of the Industry 4.0 concept”, Procedia Manuf.13, 1206‒1214 (2017).
  41.  A. Schumacher, S. Erolb, and W. Sihna, “A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises”, Procedia CIRP 52,161‒166 (2016).
  42.  D.M. Gligor and M.C. Holcomb, “Understanding the role of logistics capabilities in achieving supply chain agility: a systematic literature review”, Supp. Chain Manag. 17(4), 438‒453 (2012).
  43.  J. Schlechtendahl, M. Keinert, F. Kretschmer, A. Lechler, and A. Verl, “Making existing production systems Industry 4.0-ready: Holistic approach to the integration of existing production systems in Industry 4.0 environments”, Prod. Eng 91(1), 143 (2014).
  44.  U. Dombrowski and T. Wagner, “Mental strain as field of action in the 4th industrial revolution”, Procedia CIRP 17, 100–105 (2014).
  45.  E. Fielt, “Conceptualising business models: Definitions, frameworks and classifications”, J. Bus. Mod. 1(1), 85 (2013).
  46.  B. Schumacher, S. Erol, and W. Sihn, “A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises”, Procedia CIRP 52, 161 (2016).
  47.  J.A. Saucedo-Martinez, M. Perez-Lara, J.A. Marmolejo-Saucedo, T.E. Salais-Fierro, and P. Vasant, “Industry 4.0 framework for management and operations: A review”, J. Ambient Intell. Humaniz. Comput. 9, 789–801 (2018).
  48.  B. Ślusarczyk, “Industry 4.0: are we ready?”, Pol. J. Manag. Stud. 17(1), 232‒248 (2018).
  49.  S.A. Abbas, “Entrepreneurship and information technology businesses in economic crisis”, Entrep. Sustain. Issues 5(3), 682‒692 (2018).
  50.  F. Baena, A. Guarin, J. Mora, J. Sauza, and S. Retat, “Learning Factory: The Path to Industry 4.0”, Procedia Manuf. 9, 73‒80 (2017).
  51.  J.K. Allen, S. Commuri, R. Jiao, J. Milisavljevic-Syed, F. Mistree, J. Panchal, D. Schaefer, and W. Chen, “Design Engineering in the Age of Industry 4.0”, J. Mech. Des. 142(8) 088001 (2020).
  52.  T.D. Oesterreich and F. Teuteberg, “Understanding the implications of digitization and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry”, Comput. Ind. 83, 121‒139 (2016).
  53.  J. Oláh, G. Karmazin, K. Pető, and J. Popp, “Information technology developments of logistics service providers in Hungary”, Int. J. Logist. Res. Appl. 21(3), 1‒13 (2017).
  54.  R. Krishnamurthi, A. Kumar, “Modeling and Simulation for Industry 4.0”, in: A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development, eds., A. Nayyar, A. Kumar, Springer, 2019.
  55.  B. Rodič, “Industry 4.0 and the New Simulation Modelling Paradigm”, Organizacija 50(3), 193–207 (2017).
  56.  I. Sittón Candanedo, E. Hernández Nieves, S. Rodríguez González, M.T. Santos Martín, and A. González Briones, “Machine learning predictive model for Industry 4.0”, in Knowledge Management in Organizations Project: IOTEC – Development of Technological Capabilities in the industrial Application of the Internet of Things, 13th International Conference KMO, Žilina, Slovakia, pp 501‒510, 2018, doi: 10.1007/978-3-319-95204-8.
  57.  I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in ecodesign”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 199‒206 (2020).
  58.  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.
  59.  I. Rojek and J. Studzinski, “Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities”, Sustainability 11(2), 518 (2019).
  60.  J. Kopowski, I. Rojek, D. Mikolajewski, and M. Macko, “3D Printed Hand Exoskeleton – Own Concept”, in Advances in Manufacturing II. MANUFACTURING 2019. Lecture Notes in Mechanical Engineering, pp. 298‒306, Eds., J. Trojanowska, O. Ciszak, J. Machado, and I. Pavlenko, Springer, Cham, 2019, https://doi.org/10.1007/978-3-030-18715-6_25.
  61.  W. Żagan, S. Zalewski, S. Słomiński, and K. Kubiak, “Methods for designing and simulating optical systems for luminaires”, Bull. Pol. Acad. Sci. Tech. Sci. 68(4), 739‒750 (2020).
  62.  M. Gad-El-Hak, “Coherent structures and flow control: genesis and prospect”, Bull. Pol. Acad. Sci. Tech. Sci. 67(3), 411‒444 (2019).
  63.  Ch. Cimini, G. Pezzotta, R. Pinto, and S. Cavalieri, “Industry 4.0 technologies impacts in the manufacturing and supply chain landscape: an overview”, in Service Orientation in Holonic and Multi-Agent Manufacturing. SOHOMA 2018. Studies in Computational Intelligence, vol. 803, pp. 109‒120, Eds., T. Borangiu, D. Trentesaux, A. Thomas, and S. Cavalieri, Springer, Cham, 2019 doi: 10.1007/978-3-030- 03003-2_8.
  64.  J. Flizikowski and M. Macko, “Competitive design of shredder for plastic in recycling”, in Proc. 5th International Symposium on Tools and Methods of Competitive Engineering, Lausanne, Switzerland, 2004, vol. 1‒2, pp. 1147‒1148.
  65.  J. Flizikowski and M. Macko, J. Czerniak, A. Mroziński “Implementation of genetic algorithms into development of mechatronic multi- edge’s grinder design”, in Proc. of ASME International Mechanical Engineering Congress and Exposition (IMECE), Denver, 2012, vol. 7, pp. 1227‒1235.
  66.  M. Macko, A. Mroziński, and A. Prentki “Simulations CAE of wood pellet machine”, in MATEC Web of Conferences, 2019, vol. 254, art. no. 02028.
  67.  A. Napoleone, M. Macchi, and A. Pozzetti, “A review on the characteristics of cyber-physical systems for the future smart factories”, J. Manuf. Syst. 54, 305‒335 (2020).
  68.  L. Monostori, “Cyber-physical production systems: roots, expectations and R&D challenges”, Procedia CIRP 17, 9‒13 (2014).
  69.  K.D. Thoben, S. Wiesner, and T. Wuest, “Industrie 4.0 and smart manufacturing – a review of research issues and application examples”. Int. J. Autom. Technol. 11, 4‒16 (2017).
  70.  M. Garetti, L. Fumagalli, and E. Negri, “Role of ontologies for CPS implementation in manufacturing”, Manag Prod Eng Rev 6, 26‒32 (2015).
  71.  L. Shi, G. Guo, X. Song, “Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment”, Int. J. Prod. Res. 59(2), 576‒597 (2021).
  72.  M. Thürer, H. Zhang, M. Stevenson, F. Costa, and L. Ma, “Worker assignment in dual resource constrained assembly job shops with worker heterogeneity: an assessment by simulation”, Int. J. Prod. Res. 58, 6336‒6349 (2020).
  73.  C. Wang, G. Zhou, and Z. Zhu, “Service perspective based production control system for smart job shop under industry 4.0”, Robot. Comput.-Integr. Manuf. 65, 101954 (2020).
  74.  W. Polini and A. Corrado, “Digital twin of composite assembly manufacturing process”, Int. J. Prod. Res. 58, 5238‒5252 (2020).
  75.  D. Mourtzis, J. Angelopoulos, and G. Dimitrakopoulos, “Design and development of a flexible manufacturing cell in the concept of learning factory paradigm for the education of generation 4.0 engineers”, Procedia Manuf. 45, 361‒366 (2020).
  76.  Q. Demlehner and S. Laumer, “Why Context Matters: Explaining the Digital Transformation of the Manufacturing Industry and the Role of the Industry’s Characteristics in It”, Pac. Asia J. Assoc. Inf. Syst. 12(3), 3 (2020).
  77.  G. Genta, M. Galetto, and F. Franceschini, “Inspection procedures in manufacturing processes: recent studies and research perspectives”, Int. J. Prod. Res. 58, 4767‒4788 (2020).
  78.  D. Mourtzis, V. Siatras, and J. Angelopoulos, “Real-Time Remote Maintenance Support Based on Augmented Reality (AR)”, Appl. Sci. 10(5), 1855 (2020).
  79.  R. Davidson, “Cyber-Physical Production Networks, Artificial Intelligence-based Decision-Making Algorithms, and Big Data-driven Innovation in Industry 4.0-based Manufacturing Systems”, Econ. Manag. Financ. Mark. 15(3), 16‒22 (2020).
  80.  D. Mourtzis, G. Synodinos, J. Angelopoulos, and N. Panopoulos, “An augmented reality application for robotic cell customization”, Procedia CIRP 90, 654‒659 (2020).
  81.  T. White, I. Grecu, and G. Grecu, “Digitized Mass Production, Real-Time Process Monitoring, and Big Data Analytics Systems in Sustainable Smart Manufacturing”, J. Self-Gov. Manag. Econ.8(3), 37‒43 (2020).
  82.  D. Mourtzis, “Adaptive Scheduling in the Era of Cloud Manufacturing”, in Scheduling in Industry 4.0 and Cloud Manufacturing, International Series in Operations Research & Management Science, vol. 289, pp. 61‒85, Eds., B. Sokolov, D. Ivanov, and A. Dolgui, Springer, Cham, 2020 https://doi.org/10.1007/978-3-030-43177-8_4.
  83.  N. Hangst, S. Junk, and T. Wendt, “Design of an Additively Manufactured Customized Gripper System for Human Robot Collaboration”, in Industrializing Additive Manufacturing. AMPA, pp. 415‒425, Eds., M. Meboldt and C. Klahn, Springer, Cham, 2021, http://doi-org-443. webvpn.fjmu.edu.cn/10.1007/978-3-030-54334-1_29.
  84.  Y. Tirupachuri, “Enabling Human-Robot Collaboration via Holistic Human Perception and Partner-Aware Control”, arXiv-CS-Robotics, 2020, doi: arxiv.org/abs/2004.10847v1
  85.  W. de Paula Ferreira, F. Armellini, and L.A. de Santa-Eulalia, “Simulation in industry 4.0: A state-of-the-art review”, Comput. Ind. Eng. 149, 106868 (2020).
  86.  R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and A. Parmentola, “Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review”, Appl. Sci. 10(8), 2897 (2020).
  87.  G. Li, S. Yang, Z. Xu, J. Wang, Z. Ren, and G. Li, “Resource allocation methodology based on object-oriented discrete event simulation: A production logistics system case study”, CIRP J. Manuf. Sci. Technol. 31, 394‒405 (2020).
  88.  R. Davis, M. Vochozka, J. Vrbka, and O. Neguriţă, “Industrial Artificial Intelligence, Smart Connected Sensors, and Big Data-driven Decision-Making Processes in Internet of Things-based Real-Time Production Logistics”, Econ. Manag. Financ. Mark.15(3), 9‒15 (2020).
  89.  C.H.D. Santos, J.A. de Queiroz, F. Leal, and J.A.B. Montevechi, “Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimise decision making on non-automated process”, J. Simul.(2020) doi: 10.1080/17477778.2020.1811172.
  90.  H. Sun, G. Pedrielli, G. Zhao, C. Zhou, W. Xu, and R. Pan, “Cyber coordinated simulation for distributed multi-stage additive manufacturing systems”, J. Manuf. Syst. 57, 61‒71 (2020).
  91.  G. Chryssolouris, N. Papakostas, and D. Mavrikios, “A Perspective on Manufacturing Strategy: Produce More with Less”, CIRP J. Manuf. Sci. Technol. 1(1), 45–52 (2008), doi: 10.1016/j.cirpj.2008.06.008.
  92.  D. Mourtzis, M. Doukas, and D. Bernidaki, “Simulation in Manufacturing: Review and Challenges”, Procedia CIRP 25, 213–229 (2014), doi: 10.1016/j.procir.2014.10.032.
  93.  T. Burczyński, W. Kuś, W. Beluch, A. Długosz, A. Poteralski, and M. Szczepanik, Intelligent computing in optimal design, Springer, 2020.

Date

30.03.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.136717

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; 69; 2; e136717
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