Prediction of machining accuracy degradation of machine tools

Fan, Kuang‐Chao; Chen, Hsi-Ming; Kuo, Tzu-Hsin

doi:10.1016/j.precisioneng.2011.11.002

Cited by 49 publications

(27 citation statements)

References 7 publications

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“…Qiu et al [165] developed a stiffness-based model to achieve accurate bearing prognosis considering the bearing system as single-degree-offreedom system with its natural frequency related to the system stiffness. More recently, Fan et al [55] proposed a mathematical model for the wear analysis of the slide guideway under cutting conditions by revealing the inherent interactions between cutting force, wear, and deformation of the slide guideway, geometric errors, and final accuracy degradation of machine tools. Similar researches on determinations on crack propagation models can be found in [2,22,113].…”

Section: Fuzzy Theorymentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

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Section: Fuzzy Theorymentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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“…Regarding the sliding contact guide, the contact stiffness of a sliding surface is the function of surface finish, pairing of contact material, and interface pressure distribution [11]. The interface pressure was also verified to be an important factor affecting the contact stiffness and the machining performance [12].…”

Section: Introductionmentioning

confidence: 98%

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

et al. 2016

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This study was aimed to investigate the machining stability of a horizontal milling machine with hybrid guideway systems by finite element method. To this purpose, we first created finite element model of the milling machine with the introduction of the contact stiffness defined at the sliding and rolling interfaces, respectively. Also, the motorized built-in spindle model was created and implemented in the whole machine model. Results of finite element simulations reveal that linear guides with different preloads greatly affect the dynamic responses and machining stability of the horizontal milling machine. The critical cutting depth predicted at the vibration mode associated with the machine tool structure is about 10 mm and 25 mm in the X and Y direction, respectively, while the cutting depth predicted at the vibration mode associated with the spindle structure is about 6.0 mm. Also, the machining stability can be increased when the preload of linear roller guides of the feeding mechanism is changed from lower to higher amount.

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“…However, aiming at the problems of the accuracy variations of machine tools, most studies have focused on the accuracy degradation and accuracy reliability evaluation caused by the wearing of guideways, bolt relaxation, and stress relaxation-induced deformation of machine bodies [29,[31][32][33]. It is obvious that all of these factors reduce the geometric accuracy gradually, which affects the geometric accuracy retentivity in an essential way [34]. e main difference between accuracy reliability and accuracy retentivity is that the reliability of CNC machine tools is generally characterized by the fault possibility of functional components, while accuracy retentivity research mainly studies the accuracy variations of a machine tool in the time domain [29,30].…”

Section: Introductionmentioning

confidence: 99%

A Recognition Methodology for the Key Geometric Errors of a Multi‐Axis Machine Tool Based on Accuracy Retentivity Analysis

2019

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This paper proposes a recognition methodology for key geometric errors using the feature extraction method and accuracy retentivity analysis and presents the approach of optimization compensation of the geometric error of a multiaxis machine tool. The universal kinematics relations of the multiaxis machine tool are first modelled mathematically based on screw theory. Then, the retentivity of geometric accuracy with respect to the geometric error is defined based on the mapping between the constitutive geometric errors and the time domain. The results show that the variation in the spatial error vector is nonlinear while considering the operation time of the machine tool and the position of the motion axes. Based on this aspect, key factors are extracted that simultaneously consider the correlation, similarity, and sensitivity of the geometric error terms, and the results reveal that the effect of the position-independent geometric errors (PIGEs) on the error vectors of the position and orientation is greater than that of the position-dependent geometric errors (PDGEs) of the linear and rotary axes. Then, the fruit fly optimization algorithm (FOA) is adopted to determine the compensation values through multiobjective tradeoffs between accuracy retentivity and fluctuation in the geometric errors. Finally, an experiment on a four-axis horizontal boring machine tool is used to validate the effectiveness of the proposed approach. The experimental results show that the variations in the precision of each test piece are lower than 25.0%, and the maximum variance in the detection indexes between the finished test pieces is 0.002 mm when the optimized parameters are used for error compensation. This method not only recognizes the key geometric errors but also compensates for the geometric error of the machine tool based on the accuracy retentivity analysis results. The results show that the proposed methodology can effectively enhance the machining accuracy.

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Prediction of machining accuracy degradation of machine tools

Cited by 49 publications

References 7 publications

Cloud-enabled prognosis for manufacturing

Cloud-enabled prognosis for manufacturing

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

A Recognition Methodology for the Key Geometric Errors of a Multi‐Axis Machine Tool Based on Accuracy Retentivity Analysis

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