Fast High-Resolution FE-based Simulation of Thermo-Elastic Behaviour of Machine Tool Structures

Galant, Alexander; Beitelschmidt, Michael; Großmann, Knut

doi:10.1016/j.procir.2016.04.020

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…It includes partial models for power loss, thermal coupling coefficients, and thermo-elastic displacement and approaches for calculating the axis correction values. A modular strategy for correcting structure models in the context of machine tools has been developed through fundamental research and is undergoing validation on numerous machine components and structures [36,[40][41][42][43]. The modules can be divided into three areas: data acquisition, modeling, and correction, whereby each area is calculated in its own time domain (control real time and thermal real time) that can be calculated decoupled from each other (control internal and control external).…”

Section: Structure Model-based Correctionmentioning

confidence: 99%

Correction of Thermal Errors in Machine Tools by a Hybrid Model Approach

Friedrich,

Geist,

Yaqoob

et al. 2024

Applied Sciences

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Thermally induced position errors are one of the main error sources on the workpiece caused by the behavior of the machine tool. In today’s industrial environment, the correction of thermal errors is usually based on simple regression approaches, where the characteristic diagrams for correction are generated experimentally. The performance of these approaches is only valid for the corresponding load regimes, which often results in insufficient correction quality in practical applications. Consequently, there is only a limited benefit or even a deterioration in machine behavior if the correction characteristic is based on an inapplicable load case compared to the initial experiment. Simulation-generated characteristic diagrams using finite element models solve this disadvantage, but do not answer the question about the choice of the right characteristic matching the current load situation, and, in addition, calculate very slowly. Structural model-based correction using reduced models, on the other hand, calculates quickly, but requires a high modeling effort for accurate correction. The approach, presented in this contribution, combines simulation-generated characteristic diagrams and a structural model-based decision algorithm for a new hybrid model in order to select the appropriate characteristic diagram for the present load situation in the control system. This paper presents the simulative characteristic diagram generation by a finite element model validated by experiments in a climate chamber and a validated structural model including the concept for the decision algorithm.

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Section: Structure Model-based Correctionmentioning

confidence: 99%

Correction of Thermal Errors in Machine Tools by a Hybrid Model Approach

Friedrich,

Geist,

Yaqoob

et al. 2024

Applied Sciences

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show abstract

“…This allows estimating temperatures in the machine tool based on task-specific, locally resolved losses based on finite element and structural models. The later models mimic the thermal mechanisms of the real machine tool [28,29] and thus contribute to the integral digital twin.…”

Section: Thermal Behaviour Of the Machine Toolmentioning

confidence: 99%

Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach

Hänel

Seidel

Frieß

et al. 2021

JMMP

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This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

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“…Another commonly used method to model the thermo-elastic TCP error focuses on finite element (FE) methods. Galant et al use an FE model in combination with model order reduction (MOR) techniques to compute the thermo-elastic deformation of a machine structure component with high accuracy and a low computation time [4]. The same technique is used by Brecher et al when simulating the machine spindle [5], reaching similar results.…”

Section: Introductionmentioning

confidence: 99%

A Data-Based Model of the Thermo-Elastic TCP Error Using the Encoder Difference and Neural Networks

Brecher

Dehn

Neus

2023

Lecture Notes in Production Engineering

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The thermo-elastic tool center point (TCP) error has been an ongoing research focus, due to its large effect on the workpiece quality. Existing models to compute the thermo-elastic TCP error already perform quite well regarding the accuracy and speed of computation. However, the models are often time consuming in their parameterization, expensive to apply or are error-prone due to the used model inputs. The work presented in this paper addresses these issues by introducing the encoder difference as model input. Since the encoder difference easy and inexpensive to measure, it yields a high potential for industrial use. Therefore, in this paper, the correlation between the encoder difference and the thermo-elastic TCP error is investigated. Since the physical relationship between the encoder difference and the thermo-elastic TCP error is complex, it is necessary to use an artificial neural network to compute the resulting TCP error. Due to the variety of artificial neural network (ANN) types, with different capabilities, a range of different networks is tested regarding their capability to compute the thermo-elastic TCP error. To conclude the paper, a method to parametrize such models is derived from the gathered results.

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Fast High-Resolution FE-based Simulation of Thermo-Elastic Behaviour of Machine Tool Structures

Cited by 10 publications

References 4 publications

Correction of Thermal Errors in Machine Tools by a Hybrid Model Approach

Correction of Thermal Errors in Machine Tools by a Hybrid Model Approach

Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach

A Data-Based Model of the Thermo-Elastic TCP Error Using the Encoder Difference and Neural Networks

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