Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-26T20:35:11.894Z Has data issue: false hasContentIssue false
  • English
  • Français

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Published online by Cambridge University Press:  03 May 2019

Angkush Kumar Ghosh ,
AMM Sharif Ullah  and
Akihiko Kubo
Angkush Kumar Ghosh
Affiliation:
Graduate School of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido 090-8507, Japan
AMM Sharif Ullah*
Affiliation:
Faculty of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido 090-8507, Japan
Akihiko Kubo
Affiliation:
Faculty of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami, Hokkaido 090-8507, Japan
*
Author for correspondence: AMM Sharif Ullah, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper addresses the construction of digital twins (exact mirror images of real-world in cyberspace) using hidden Markov models for the futuristic manufacturing systems known as Industry 4.0. The proposed digital twin consists of two components namely model component and simulation component. The model component forms a Markov chain that encapsulates the dynamics underlying the phenomenon by using some discrete states and their transition probabilities. The simulation component recreates the phenomenon using a Monte Carlo simulation process. The efficacy of the proposed digital twin construction methodology is shown by a case study, where the digital twin of the surface roughness of a surface created by successive grinding operations is described. The developers of the cyber-physical systems will be benefitted from the outcomes of this study because these systems need the computable virtual abstractions of the manufacturing phenomena to address the issues related to the maturity index of futuristic manufacturing systems (i.e., understand, predict, decide, and adopt).

Keywords

Complex phenomenadigital twinhidden Markov modelmanufacturing systemssurface roughness
Type
Research Article
Information
AI EDAM , Volume 33 , Issue 3 , August 2019 , pp. 317 - 331
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alam, KM and Saddik, AE (2017) C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 5, 2050–2062.Google Scholar
Baum, LE and Petrie, T (1966) Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Statist. 37, 1554–1563.Google Scholar
Berners-Lee, T, Hendler, J and Lassila, O (2001) The semantic web. Sci. Am. 284, 34–43.Google Scholar
Bhat, NN, Dutta, S, Pal, SK and Pal, S (2016) Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images. Measurement 90, 500–509.Google Scholar
Botkina, D, Hedlind, M, Olsson, B, Henser, J and Lundholm, T (2018) Digital twin of a cutting tool. Proc. CIRP 72, 215–218.Google Scholar
Cai, Y, Shi, X, Shao, H, Wang, R and Liao, S (2018) Energy efficiency state identification in milling processes based on information reasoning and hidden Markov model. J. Clean. Prod. 193, 397–413.Google Scholar
Choi, W, Shi, F, Lowe, MJS, Skelton, EA, Craster, RV and Daniels, WL (2018) Rough surface reconstruction of real surfaces for numerical simulations of ultrasonic wave scattering. NDT & E Int. 98, 27–36.Google Scholar
Chui, MW, Feng, YQ, Wang, W, Li, PL and Li, ZC (2013) Numerical simulation of rough surface with crossed texture. Appl. Mech. Mater. 321–324, 196–200.Google Scholar
Fill, H-G (2017) SeMFIS: a flexible engineering platform for semantic annotations of conceptual models. Semant. Web 8, 747–763.Google Scholar
Fill, H-G (2018) Semantic annotations of enterprise models for supporting the evolution of model-driven organizations. Enterprise Model. Info. Syst. Archit. 13, 5:1–5:25.Google Scholar
Fraser, AM (2008) Hidden Markov Models and Dynamical Systems. Philadelphia: SIAM.Google Scholar
Glaessgen, EH and Stargel, DS (2012) The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles. Proc. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Structures, Structural Dynamics, and Materials and Co-located Conferences. Honolulu, Hawaii: American Institute of Aeronautics and Astronautics.Google Scholar
Grieves, M and Vickers, J (2017) Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Kahlen F-J, Flumerfelt S and Alves A (eds), Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Cham: Springer International Publishing, pp. 85–113.Google Scholar
Haag, S and Anderl, R (2018) Digital twin – Proof of concept. Manuf. Letter. 15(B), 64–66.Google Scholar
Higuchi, M, Yamaguchi, T, Yano, A, Yamamoto, N, Ueshima, R, Matumori, N and Yoshizawa, I (2001) Development of design technology of porous superfinishing stone using fractal geometry (2nd report): geometric modeling of stone topography and design support system. J. Japan Soc. Prec. Eng. 67(3), 428–432. (In Japanese)Google Scholar
Hu, L, Nguyen, N-T, Tao, W, Leu, MC, Liu, XF, Shahriar, MR and Sunny, SMNA (2018) Modeling of cloud-based digital twins for smart manufacturing with MT connect. Proc. Manuf. 26, 1193–1203.Google Scholar
Khilwani, N and Harding, JA (2014) Managing corporate memory on the semantic web. J. Intell. Manuf. 27(1), 101–118.Google Scholar
Kim, K-Y and Ahmed, F (2018) Semantic weldability prediction with RSW quality dataset and knowledge construction. Adv. Eng. Inform. 38, 41–53.Google Scholar
Kritzinger, W, Karner, M, Traar, G, Henjes, J and Sihn, W (2018) Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine 51(11), 1016–1022.Google Scholar
Kumar, A, Chinnam, RB and Tseng, F (2018) An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools. Comput. Ind. Eng. 128, 1008–1014.Google Scholar
Kunath, M and Winkler, H (2018) Integrating the digital twin of the manufacturing system into a decision support system for improving the order management process. Proc. CIRP 72, 225–231.Google Scholar
Li, J, Pedrycz, W and Jamal, I (2017) Multivariate time series anomaly detection: a framework of hidden Markov models. Appl. Soft Comput. 60, 229–240.Google Scholar
Li, Z, Fang, H, Huang, M, Wei, Y and Zhang, L (2018) Data-driven bearing fault identification using improved hidden Markov model and self-organizing map. Comput. Ind. Eng. 116, 37–46.Google Scholar
Liao, TW, Hua, G, Qu, J and Blau, PJ (2006) Grinding wheel condition monitoring with hidden Markov model-based clustering methods. Mach. Sci. Technol. Int. J. 10, 511–538.Google Scholar
Liao, W, Li, D and Cui, S (2016) A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis. J. Intell. Manuf. 29, 1845–1857.Google Scholar
Lu, Y and Xu, X (2018) Resource virtualization: a core technology for developing cyber-physical production systems. J. Manuf. Syst. 47, 128–140.Google Scholar
Luo, W, Hu, T, Zhu, W and Tao, F (2018) Digital twin modeling method for CNC machine tool. Proc. IEEE 15th International Conference on Networking, Sensing and Control, ICNSC,18. Zhuhai, China: IEEE.Google Scholar
Mba, CU, Makis, V, Marchesiello, S, Fasana, A and Garibaldi, L (2018) Condition monitoring and state classification of gearboxes using stochastic resonance and hidden Markov models. Measurement 126, 76–95.Google Scholar
Moreau, L, Groth, P, Cheney, J, Lebo, T and Miles, S (2015) The rationale of PROV. J. Web Semant. 35, 235–257.Google Scholar
Nguyen, N (2017) An analysis and implementation of the hidden Markov model to technology stock prediction. Risks 5, 62:1–62:18.Google Scholar
Oliveira, W, Ambrósio, LM, Braga, R, Ströele, V, David, JM and Campos, F (2017) A framework for provenance analysis and visualization. Proc. Comput. Sci. 108, 1592–1601.Google Scholar
Olivotti, D, Dreyer, S, Lebek, B and Breitner, MH (2018) Creating the foundation for digital twins in the manufacturing industry: an integrated installed base management system. Inf. Syst. E-Bus. Manage. doi: 10.1007/s10257-018-0376-0.Google Scholar
Padovano, A, Longo, F, Nicoletti, L and Mirabelli, G (2018) A digital twin based service oriented application for a 4.0 knowledge navigation in the smart factory. IFAC-PapersOnLine 51, 631–636.Google Scholar
Petropoulos, A, Chatzis, SP and Xanthopoulos, S (2017) A hidden Markov model with dependence jumps for predictive modeling of multidimensional time-series. Info. Sci. 412–413, 50–66.Google Scholar
Qi, Q, Tao, F, Zuo, Y and Zhao, D (2018) Digital twin service towards smart manufacturing. Proc. CIRP 72, 237–242.Google Scholar
Ramos, L (2015) Semantic Web for manufacturing, trends and open issues: toward a state of the art. Comput. Ind. Eng. 90, 444–460.Google Scholar
Rosen, R, Wichert, Gv, Lo, G and Bettenhausen, KD (2015) About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48, 567–572.Google Scholar
Scaglioni, B and Ferretti, G (2018) Towards digital twins through object-oriented modelling: a machine tool case study. IFAC-PapersOnLine 51, 613–618.Google Scholar
Schleich, B, Anwer, N, Mathieu, L and Wartzack, S (2017) Shaping the digital twin for design and production engineering. CIRP Ann. Manuf. Technol. 66, 141–144.Google Scholar
Schroeder, GN, Steinmetz, C, Pereira, CE and Espindola, DB (2016) Digital twin data modeling with AutomationML and a communication methodology for data exchange. IFAC-PapersOnLine 49, 12–17.Google Scholar
Schuh, G, Anderl, R, Gausemeier, J, Hompel, Mt and Wahlster, W (eds) (2017) Industrie 4.0 maturity Index. In Managing the Digital Transformation of Companies (acatech STUDY). Munich: Herbert Utz Verlag, pp. 1–53.Google Scholar
Sizov, S (2007) What makes You think that? The semantic web's proof layer. IEEE Intell. Syst. 22, 94–99.Google Scholar
Söderberg, R, Wärmefjord, K, Carlson, JS and Lindkvist, L (2017) Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann. Manuf. Technol. 66, 137–140.Google Scholar
Talkhestani, BA, Jazdi, N, Schloegl, W and Weyrich, M (2018) Consistency check to synchronize the digital twin of manufacturing automation based on anchor points. Proc. CIRP 72, 159–164.Google Scholar
Tao, F, Cheng, J, Qi, Q, Zhang, M, Zhang, H and Sui, F (2018) Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94, 3563–3576.Google Scholar
Tunjic, C, Atkinson, C and Draheim, D (2018) Supporting the model-driven organization vision through deep, orthographic modeling. Enterprise Model. Info. Syst. Archit. 13, 7:1–7:39.Google Scholar
Uhlemann, TH-J, Schock, C, Lehmann, C, Freiberger, S and Steinhilper, R (2017) The digital twin: demonstrating the potential of real time data acquisition in production systems. Proc. Manuf. 9, 113–120.Google Scholar
Ullah, AMMS (2017) Surface roughness modeling using Q-sequence. Math. Comput. Appl. 22, 33:1–33:12.Google Scholar
Ullah, AMMS (2019) Modeling and simulation of complex manufacturing phenomena using sensor signals from the perspective of Industry 4.0. Adv. Eng. Inform. 39, 1–13.Google Scholar
Ullah, AMMS and Harib, KH (2006) Knowledge extraction from time series and its application to surface roughness simulation. Info. Knowl. Syst. Manag. 5, 117–134.Google Scholar
Ullah, AMMS and Shamsuzzaman, M (2013) Fuzzy Monte Carlo simulation using point-cloud-based probability- possibility transformation. Simulation 89, 860–875.Google Scholar
Ullah, AMMS, Tamaki, J and Kubo, A (2010) Modeling and simulation of 3D surface finish of grinding. Adv. Mater. Res. 126–128, 672–677.Google Scholar
Ullah, AMMS, Arai, N and Watanabe, M (2013) Concept map and internet-aided manufacturing. Proc. CIRP 12, 378–383.Google Scholar
Ullah, AMMS, Fuji, A, Kubo, A, Tamaki, J and Kimura, M (2015) On the surface metrology of bimetallic components. Machining Sci. Technol. 19, 339–359.Google Scholar
Ullah, A, Caggiano, A, Kubo, A and Chowdhury, M (2018) Elucidating grinding mechanism by theoretical and experimental investigations. Materials 11, 274:1–274:19.Google Scholar
Visser, I (2011) Seven things to remember about hidden Markov models: a tutorial on Markovian models for time series. J. Math. Psychol. 55, 403–415.Google Scholar
Wu, D, Terpenny, J and Schaefer, D (2016) Digital design and manufacturing on the cloud: a review of software and services. Artif. Intell. Eng. Des. Anal. Manuf. 31, 104–118.Google Scholar
Xie, F-Y, Hu, Y-M, Wu, B and Wang, Y (2016) A generalized hidden Markov model and its applications in recognition of cutting states. Int. J. Preci. Eng. Manuf. 17, 1471–1482.Google Scholar
Zhang, S, Zhang, Y and Zhu, J (2018) Residual life prediction based on dynamic weighted Markov model and particle filtering. J. Intell. Manuf. 29, 753–761.Google Scholar
Zheng, Y, Yang, S and Cheng, H (2018) An application framework of digital twin and its case study. J. Ambient Intell. Humanized Comput. 10, 1141–1153.Google Scholar
Zhuang, C, Liu, J and Xiong, H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int. J. Adv. Manuf. Technol. 96, 1149–1163.Google Scholar