A machining test to evaluate thermal influence on the kinematics of a five-axis machine tool

Ibaraki, Soichi; Okumura, Rin

doi:10.1016/j.ijmachtools.2021.103702

Cited by 39 publications

(12 citation statements)

References 34 publications

(48 reference statements)

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“…aerospace and healthcare industries. Due to the geometric errors of machine tools, the actual position of the tool differs from the nominal value, often resulting in significant dimensional deviations in the machined products [1]. As the manufacturing industry is constantly challenged with products with increasingly complex geometry and demands for higher accuracy, the determination and consequent reduction (through adjustment or compensation) of geometric errors play an increasingly significantly role in achieving accurate and repeatable machining quality [2].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty assessment of machine tool squareness error identification using on-machine measurement

Tang,

Feng,

et al. 2023

Meas. Sci. Technol.

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Identification and compensation of geometric errors in the machine tool are widely performed to increase machining accuracy. Periodic verification of the geometric errors, as introduced in ISO 230-2, helps monitor the machine tool accuracy during continued manufacturing operation and detect accuracy degradation early on. While interferometry-based error identification techniques are commonly applied, on-machine measurement (OMM), with its increasing availability to machine tools, can be used to identify geometric errors as an alternative to traditional interferometry-based techniques. As geometric errors also contribute to OMM error, assessment of the uncertainty of error identification is essential to ensure the reliability of the identification. This work presents a method to evaluate the uncertainty in the OMM-based geometric error identification process by Monte Carlo simulation. The error ellipse model, which represents OMM errors with better accuracy, is utilized to improve the identification uncertainty. The squareness errors, as position-independent geometric errors that contribute to machining inaccuracy, are taken as an example to demonstrate the presented method. The influence of the artifact setup errors on the error identification is also investigated. A series of experiments are conducted to evaluate the uncertainty of the OMM-based identification method. The geometric error obtained with the presented method is found to deviate by less than 10% from that obtained with an interferometry-based commercial instrument. The uncertainty obtained from the proposed Monte Carlo simulation method matches well with the uncertainty results obtained by the Guide to the Expression of Uncertainty in Measurement method and repeated measurements.

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Section: Introductionmentioning

confidence: 99%

Uncertainty assessment of machine tool squareness error identification using on-machine measurement

Tang,

Feng,

et al. 2023

Meas. Sci. Technol.

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show abstract

“…Therefore, scholars at home and abroad have carried out much research on geometric error modelling and accurate prediction techniques, and the commonly used methods include the mechanism method [2], rigid body kinematics method [3], HTM [4] and multi-body system (MBS) theory [5]. Among them MBS is the mainstream modelling method, which aims to establish a coordinate system mainly in each motion unit, conduct a high degree of abstraction and generalization for a complex motion system, and clearly express the accuracy model error factors and machine tooltip offset relationship [6].…”

Section: Introductionmentioning

confidence: 99%

A method of sensitivity analysis and precision prediction for geometric errors of five-axis machine tools based on multi-body system theory

Tan

Liao

Jiang

et al. 2022

Int J Adv Manuf Technol

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In order to ensure the mechanical performance and machining accuracy of the machine tool, the problem of mutual deviations of the machine tool moving parts in the machining process, which cause machining errors and subsequent accuracy prediction difficulties, is solved.Firstly, the structure and motion mechanism of the machine tool are analysed, and a static accuracy model of the machine tool machining posture relationship is established using multi-body system theory and coordinate transformation, and the measured deviation values are fitted and solved according to the formula, and the geometric error law affecting machining accuracy and the distribution of machining point error values in the machine tool motion space are explored. Then the response surface method is used to simplify the solution. Finally, the blade is selected as the machined part and the machining trajectory is extracted for experiments to obtain the error distribution range of the machining surface of the blade.The final experimental results surface, along the Z-directional component and the integrated average compensation rate reached 42.6% and 89.6% respectively, verifying the effectiveness of the method in this paper.

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“…Although the non-cutting test method is essential, the workpiece cutting accuracy in actual machining has attracted more attention. 22 The dynamic accuracy is often related to the actual machining conditions; hence, it is necessary to analyze it from the test piece cutting.…”

Section: Introductionmentioning

confidence: 99%

Identification method for the dynamic motion error of the rotary axis based on circular and elliptical test piece cutting

Zhang

Xiang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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The rotary axis is crucial for the machining accuracy of five-axis machines, and its motion accuracy is significantly influenced by the dynamic motion error. A test of circular and elliptical test piece cutting is proposed to identify and analyze the dynamic motion error of the rotary axis. The influence of the feed rate on the dynamic motion accuracy is explained. The dynamic motion error can be divided into two types, depending on its source and manifestation: dynamic error inside the servo loop (DEIS) and dynamic error outside the servo loop (DEOS). At different feed rates, elliptical test piece cutting and kinetics analyses are conducted to identify the DEIS, and circular test piece cutting together with double ball bar (DBB) tests are conducted to identify the DEOS. On-machine inspection and the coordinate measuring machine (CMM) calibration are performed. By combining the DBB test and kinetics analysis results, the DEIS and DEOS are identified and analyzed. The experimental verification shows that the DEIS and DEOS change according to the feed rate and can be determined from the machining error of the test piece. Consequently, the proposed method offers an effective reference for improving the motion accuracy of the rotary axis.

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A machining test to evaluate thermal influence on the kinematics of a five-axis machine tool

Cited by 39 publications

References 34 publications

Uncertainty assessment of machine tool squareness error identification using on-machine measurement

Uncertainty assessment of machine tool squareness error identification using on-machine measurement

A method of sensitivity analysis and precision prediction for geometric errors of five-axis machine tools based on multi-body system theory

Identification method for the dynamic motion error of the rotary axis based on circular and elliptical test piece cutting

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