Position-dependent FRF identification without force measurement in milling process

Altun, Barış; Çalışkan, Hakan; Özşahin, Orkun

doi:10.1007/s00170-023-11925-w

Cited by 1 publication

(5 citation statements)

References 22 publications

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“…Methods that incorporate a force simulation model into modal analysis require the force model to closely resemble real-world behavior [10,13]. Also, either the parameters of the tool system and workpiece must be known [10] or the milling force model has to be adjusted, if different tools are used [13].…”

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

confidence: 99%

“…Methods that conduct modal analysis under operating conditions often require specific toolpaths or force excitation to be executed [13,14]. Therefore, this makes these methods not practicable to observe the dynamic behavior of machine tools in industry.…”

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

confidence: 99%

“…It can be shown that modal parameters are dependent on the axis positions of machine tools [13]. However, this is often not considered or not investigated in proposed methods [7,8,10,14,18].…”

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

confidence: 99%

“…The coupling parameters between the machine tool-spindle substructure and the tool holder-tool substructure are estimated using optimization. Altun, Çalıskan and Özsahin 2023 [13] identify the FRF of the workpiece-fixture system by using a similar approach. The cutting forces are simulated using a two DoF milling model.…”

Section: Estimation Of Modal Parametersmentioning

confidence: 99%

“…Measuring force directly, as opposed to simulating [10,13], eliminates the inaccuracies associated with simulating force. However, there are additional challenges, such as measurement uncertainties and increased sensor requirements, which have an impact on the measurement quality, costs and installation space.…”

Section: Direct Force Measurementmentioning

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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