Nonlinear process damping identification using finite amplitude stability and the influence analysis on five-axis milling stability

Tang, Xiaocheng; Peng, Fangyu; Yan, Rong; Zhu, Zhi‐Biao; Li, Zepeng; Xin, Shihao

doi:10.1016/j.ijmecsci.2020.106008

Cited by 21 publications

(3 citation statements)

References 48 publications

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“…Feng et al [22,23] found that process damping is affected by the ploughing mechanism and velocity-dependent mechanism, and thus, they proposed a unified process damping model in milling of thin-walled workpiece. Tang et al [24] proposed a novel indentation coefficient identification method of process damping to obtain the 3D stability lobe diagram, which avoid complex experimental identification. As the tool wear intensifies, the worn tool can improve process damping force which can avoid chatter [25].…”

Section: Introductionmentioning

confidence: 99%

Investigation of tool flank wear effect on system stability prediction in the milling of Ti-6AI-4 V thin-walled workpiece

Zhang

Zhao

et al. 2022

Int J Adv Manuf Technol

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Excessive tool wear can shorten tool life and cause low machining surface quality and efficiency in milling of Ti-6AI-4V thin-walled workpieces. To overcome the effect of time-varying tool flank wear on system stability prediction ignored by conventional method, a system stability prediction model considering tool flank wear effect in milling of Ti-6AI-4V thin-walled workpiece is proposed. In this work, the tool flank wear region is discretized into differential elements, then, the friction effect and process damping effect caused by extruding function between tool and workpiece are analyzed, and a time-varying milling force model is established.In this process, the relationship of cutting tool flank wear band width VB and section radius difference NB is determined, and the indentation volume between tool and workpiece is iteratively calculated, which is used to investigate process damping. After, time-varying milling force coefficients are derived considering different tool flank wear status. Then, in modal space, the evolutionary process of stability lobe diagrams considering tool flank wear effect is determined. Subsequently, to effectively predict system stability, tool flank wear curves and dynamic cutting force coefficients are calibrated by slot milling, and cutting force prediction values considering tool flank wear effect and experimental values in feed direction and normal direction are 7.3% and 12.1%, respectively. Finally, a series of machining tests are conducted to verify the effectiveness of tool flank wear on machining stability in milling of Ti-6AI-4V thin-walled workpieces to some extent, and the experimental results show that the system stability prediction accuracy of the proposed method is improved by 23.8% compared with that using conventional method.

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Section: Introductionmentioning

confidence: 99%

Investigation of tool flank wear effect on system stability prediction in the milling of Ti-6AI-4 V thin-walled workpiece

Zhang

Zhao

et al. 2022

Int J Adv Manuf Technol

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“…These mathematical models must accurately measure the modal parameters of the processing system. Tang et al [2] proposed a nonlinear tool mark coefficient recognition method using finite amplitude. The results showed that the size distribution of the critical depth of the cut is affected by the process damping, and the percentage increase in the cut depth is closely related to the tool direction and the frequency response function (FRF).…”

Section: Introductionmentioning

confidence: 99%

Performance of Five-Axis Machine Tool and Intelligent Machining Process

Chan¹,

Lin²

2021

Preprint

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In this study, the processing performance of a five-axis machine tool was analyzed to identify processing weaknesses as the basis for subsequent structural improvements. Data were then integrated through the Abductory Induction Mechanism (AIM) polynomial neural network to predict intelligent processing quality, and an in-depth investigation was conducted by importing processing parameters to predict the surface quality of the finished product.The finite element analysis method was used to analyze the static and dynamic characteristics of the whole machine and to test the structural modal frequency and vibration shape. For modal testing, the experiment used various equipment, including impact hammers, accelerometers, and signal extractors. Subsequent planning of modal frequency band processing experiments was conducted to verify the influence of natural frequencies on the processing level. Finally, according to the machine processing characteristics, a processing experiment was planned. The measurement record was used as the training data of the AIM polynomial neural network to establish the processing quality prediction model.After analysis and an actual machine test comparison, the three-axis static rigidity values of the machine were X: 1.63 Kg/µm, Y: 1.93 Kg/µm, and Z: 3.95 Kg/µm. The modal vibration shape maximum error of the machine was within 6.2%. The processing quality prediction model established by the AIM polynomial neural network could input processing parameters to achieve the surface roughness prediction value, and the actual relative error of the Ra value was within 0.1 µm.Based on the results of cutting experiments, the influence of the dynamic characteristics of the machine on the processing quality was obtained, especially in the modal vibration environment, which had an adverse effect on the surface roughness. Hence, the surface roughness of the workpiece processed by the machine could be predicted.

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“…Model updating methods include, for instance, the identification of structural damping using the FRF-based model updating method and damping identification for accurate prediction of the measured FRFs using finite element updated models of the mechanical system [ 66 , 67 ]. Recently, several studies were conducted on dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

A Critical Review of Nonlinear Damping Identification in Structural Dynamics: Methods, Applications, and Challenges

Al-hababi

Cao

Saleh

et al. 2020

Sensors

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In recent decades, nonlinear damping identification (NDI) in structural dynamics has attracted wide research interests and intensive studies. Different NDI strategies, from conventional to more advanced, have been developed for a variety of structural types. With apparent advantages over classical linear methods, these strategies are able to quantify the nonlinear damping characteristics, providing powerful tools for the analysis and design of complex engineering structures. Since the current trend in many applications tends to more advanced and sophisticated applications, it is of great necessity to work on developing these methods to keep pace with this progress. Moreover, NDI can provide an effective and promising tool for structural damage detection purposes, where the changes in the dynamic features of structures can be correlated with damage levels. This review paper provides an overview of NDI methods by explaining the fundamental challenges and potentials of these methods based on the available literature. Furthermore, this research offers a comprehensive survey of different applications and future research trends of NDI. For potential development and application work for nonlinear damping methods, the anticipated results and recommendations of the current paper can assist researchers and developers worldwide to find out the gaps and unsolved issues in the field of NDI.

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Nonlinear process damping identification using finite amplitude stability and the influence analysis on five-axis milling stability

Cited by 21 publications

References 48 publications

Investigation of tool flank wear effect on system stability prediction in the milling of Ti-6AI-4 V thin-walled workpiece

Investigation of tool flank wear effect on system stability prediction in the milling of Ti-6AI-4 V thin-walled workpiece

Performance of Five-Axis Machine Tool and Intelligent Machining Process

A Critical Review of Nonlinear Damping Identification in Structural Dynamics: Methods, Applications, and Challenges

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