Mode shape database-based estimation for machine tool dynamics

Liu, Jiahui; Kizaki, Toru; Ren, Zongwei; Sugita, Naohiko

doi:10.1016/j.ijmecsci.2022.107739

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…Although TOMA methods mitigate the problems of both the need for force simulation and the need for a specific force excitation, not all modal parameters can be estimated [9]. In fact, only natural frequencies and damping ratios can be estimated.…”

Section: Review Of the State-of-the-art Methodsmentioning

confidence: 99%

“…Since the modal parameters of machine tools are pose-dependent, the method cannot be used when the axes of a machine tool are moving. Liu et al [9] built a mode shape database by conducting classical modal analysis at different points in the workspace of the machine tool. Between the measuring points, they use linear interpolation.…”

Section: Estimation Of Modal Parametersmentioning

confidence: 99%

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Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Oexle,

Heimberger,

Puchta

et al. 2024

Machines

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The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached.

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Section: Review Of the State-of-the-art Methodsmentioning

confidence: 99%

Section: Estimation Of Modal Parametersmentioning

confidence: 99%

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Oexle,

Heimberger,

Puchta

et al. 2024

Machines

View full text Add to dashboard Cite

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“…where G(ω) is the frequency response function (FRF) of the DBSFDS, which can be expressed as Equation ( 20) [46].…”

Section: Xmentioning

confidence: 99%

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Liu,

Lu,

Yonezawa

et al. 2023

Mathematics

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Dual-ball-screw feed drive systems (DBSFDSs) are designed for most high-end manufacturing equipment. However, the mismatch between the dynamic characteristic parameters (e.g., stiffness and inertia) and the P-PI cascade control method reduces the accuracy of the DBSFDSs owing to the structural characteristic changes in the motion. Moreover, the parameters of the P-PI cascade controller of the DBSFDSs are always the same even though the two axes have different dynamic characteristics, and it is difficult to tune two-axis parameters simultaneously. A new application of the combination of the grey wolf optimization (GWO) algorithm and the P-PI cascade controller is presented to solve these problems and enhance the motion performance of DBSFDSs. The novelty is that the flexible coupling model and dynamic stiffness obtained from the motor current can better represent the two-axis coupling dynamic characteristics, and the GWO algorithm is used to adjust the P-PI controller parameters to address variations in the positions of the moving parts and reflect characteristic differences between the two axes. Comparison of simulation and experimental results validated the superiority of the proposed controller over existing ones in practical applications, showing a decrease in the tracking error of the tool center and non-synchronization error of over 34% and 39%, respectively.

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