Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems

Hung, Jui‐Pin; Lin, Wei-Zhu; Chen, Yongjun; Luo, Tzou-Lung

doi:10.3390/app6030076

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…1) was used. A representative compliance [Wöste 2019, Hung 2016 was synthesized by applying a specific, simulation-based design of a flexure hinge module. The interrupted cut was realized by implementing notches in a workpiece consisting of the aluminum alloy EN AW-7075.…”

Section: Interrupted Cut and Specific Compliancementioning

confidence: 99%

Experimental Setup for Analyzing Fundamentals of Cutting Processes Using a Modular System

Wöste¹,

Baumann²,

Bergmann³

et al. 2020

MM SJ

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Machining operations like milling exhibit complex process kinematics, which restricts the identification and analysis of fundamental cause-effect interrelations. In order to differentiate the impact of relevant process parameters, a reduction of influencing factors is necessary. For this purpose, a cutting operation with a linear cutting motion, like orthogonal cutting, is beneficial. Such an experimental setup requires suitable tools, workpieces and measurement systems. The experimental assessment of a developed modular design is presented in this publication. In addition to orthogonal cuts, oblique cuts, which are representative for machining operations with respect to the helix angle, were investigated. To analyze process dynamics, interrupted cuts, including a specifically compliant tool system, were performed.

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Section: Interrupted Cut and Specific Compliancementioning

confidence: 99%

Experimental Setup for Analyzing Fundamentals of Cutting Processes Using a Modular System

Wöste¹,

Baumann²,

Bergmann³

et al. 2020

MM SJ

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show abstract

“…Research studies [2][3][4] have shown that a rolling guide exhibits different vibration characteristics, depending on the preload applied on the rolling elements. The interface characteristic of mechanical components with rolling elements has been shown to be an important factor that affects the static and dynamic characteristics of the machine tool [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network

et al. 2021

Self Cite

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In this paper, we propose an artificial neural network (ANN) predictive model to identify the linear guide preload based on the measured vibration features of the feeding stage. In this study, the relationship between the contact stiffness and preload level of a linear guide was investigated by an experimental analysis. Furthermore, the stage was assembled with different linear guide preloads for the motion test to assess the vibrations. Vibration levels with changes in preload values and feeding rates were examined. The predictive models were established and verified based on a dataset collected from tests using an ANN approach. The ANN models were shown to have an excellent accuracy of 96.5% in the training datasets, which were collected from stages with sliding blocks rated at consistent preloads. The average percentage prediction error in the verification dataset was approximately 8.54–11.23%. This is probably because the stage with an unevenly distributed preload in the sliding blocks induces vibration with more fluctuation, which eventually affects the prediction accuracy. The results verify the feasibility of online preload identification for the condition monitoring of the feeding system.

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“…These efforts led to joint stiffness modeling and stiffness identification of the robot, and there are some groups of stiffness identification algorithms. For example, the finite elements analysis (FEA) method constructs the physical model based on many of finite elements and the corresponding relative stiffness matrices [4][5][6]. Mekaouche presented a method of evaluating the relationship between a numerical tool and a general stiffness map of the robot based on FEA [7].…”

Section: Introductionmentioning

confidence: 99%

Joint Stiffness Identification and Deformation Compensation of Serial Robots Based on Dual Quaternion Algebra

Zhang

et al. 2018

Applied Sciences

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As the application of industrial robots is limited by low stiffness that causes low precision, a joint stiffness identification algorithm for serial robots is presented. In addition, a deformation compensation algorithm is proposed for the accuracy improvement. Both of these algorithms are formulated by dual quaternion algebra, which offers a compact, efficient, and singularity-free way in robot analysis. The joint stiffness identification algorithm is derived from stiffness modeling, which is the combination of the principle of virtual work and dual quaternion algebra. To validate the effectiveness of the proposed identification algorithm and deformation compensation algorithm, an experiment was conducted on a dual arm industrial robot SDA5F. The robot performed a drilling operation during the experiment, and the forces and torques that acted on the end-effector (EE) of both arms were measured in order to apply the deformation compensation algorithm. The results of the experiment show that the proposed identification algorithm is able to identify the joint stiffness parameters of serial industrial robots, and the deformation compensation algorithm can improve the accuracy of the position and orientation of the EE. Furthermore, the performance of the forces and torques that acted on the EE during the operation were improved as well.

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Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems

Cited by 8 publications

References 23 publications

Experimental Setup for Analyzing Fundamentals of Cutting Processes Using a Modular System

Experimental Setup for Analyzing Fundamentals of Cutting Processes Using a Modular System

Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network

Joint Stiffness Identification and Deformation Compensation of Serial Robots Based on Dual Quaternion Algebra

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