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Abstract
This paper aims to provide a viewpoint on the exploitation of physics-based dynamic simulation in product development and discrete manufacturing products. The dynamics models can be represented with computationally light models when the product and its dynamics are well known and thereby analyzing the performance e.g., with AI methods rapidly and accurately. The recent developments with methodologies, sensor development, measuring techniques and increased computing capacity are making the simulation world closer to reality and the ability for real-time operation simulations paralleled to the real system. This enables the exploitation of the digital twin paradigm at full capacity together with high-maturity digital twin models.
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References
- UKKO, J., SAUNILA, M., HEIKKINEN, J., SEMKEN, R. S., AND MIKKOLA, A. Real-Time Simulation for Sustainable Production: Enhancing User Experience and Creating Business Value. Routledge, 2021. http://dx.doi.org/10.4324/9781003054214
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- YU, X., ACEITUNO, J. F., KURVINEN, E., MATIKAINEN, M. K., KORKEALAAKSO, P., ROUVINEN, A., JIANG, D., ESCALONA, J. L., AND MIKKOLA, A. Comparison of numerical and computational aspects between two constraint-based contact methods in the description of wheel/rail contacts. Multibody System Dynamics (2022), 1–42. https://doi.org/10.1007/s11044-022-09811-6
- KHADIM, Q., HAGH, Y. S., PYRHÖNEN, L., JAISWAL, S., ZHIDCHENKO, V., KURVINEN, E., ... & HANDROOS, H. (2022). State estimation in a hydraulically actuated log crane using unscented Kalman filter. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3179591
References
UKKO, J., SAUNILA, M., HEIKKINEN, J., SEMKEN, R. S., AND MIKKOLA, A. Real-Time Simulation for Sustainable Production: Enhancing User Experience and Creating Business Value. Routledge, 2021. http://dx.doi.org/10.4324/9781003054214
LEI, Y., YANG, B., JIANG, X., JIA, F., LI, N., AND NANDI, A. K. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing 138 (2020), 106587. https://doi.org/10.1016/j.ymssp.2019.106587
KURVINEN, E., VIITALA, R., CHOUDHURY, T., & SOPANEN, J. (2020, October). Simulation model to investigate effect of support stiffness on dynamic behaviour of a large rotor. In 12th International Conference on Vibrations in Rotating Machinery (pp. 457-469). CRC Press. https://doi.org/10.1201/9781003132639-37
CHOUDHURY, T., KURVINEN, E., VIITALA, R., AND SOPANEN, J. Development and verification of frequency domain solution methods for rotor-bearing system responses caused by rolling element bearing waviness. Mechanical Systems and Signal Processing 163 (2022), 108117. https://doi.org/10.1016/j.ymssp.2021.108117
BOBYLEV, D., CHOUDHURY, T., MIETTINEN, J. O., VIITALA, R., KURVINEN, E., AND SOPANEN, J. Simulation-based transfer learning for support stiffness identification. IEEE Access 9 (2021), 120652–120664. https://doi.org/10.1109/ACCESS.2021.3108414
YU, X., ACEITUNO, J. F., KURVINEN, E., MATIKAINEN, M. K., KORKEALAAKSO, P., ROUVINEN, A., JIANG, D., ESCALONA, J. L., AND MIKKOLA, A. Comparison of numerical and computational aspects between two constraint-based contact methods in the description of wheel/rail contacts. Multibody System Dynamics (2022), 1–42. https://doi.org/10.1007/s11044-022-09811-6
KHADIM, Q., HAGH, Y. S., PYRHÖNEN, L., JAISWAL, S., ZHIDCHENKO, V., KURVINEN, E., ... & HANDROOS, H. (2022). State estimation in a hydraulically actuated log crane using unscented Kalman filter. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3179591
