Chatter detection in milling process based on VMD and energy entropy

Liu, Changfu; Zhu, Lida; Ni, Chenbing

doi:10.1016/j.ymssp.2017.11.046

Cited by 224 publications

(70 citation statements)

References 39 publications

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“…Cutting force, which is originated by the shearing of the material, friction between the chip and the cutter and so on, can convey key information on the conditions of machining processes. The cutting forces can reflect the machinability of the material [ 1 , 2 ] or used to identify machining malfunctions, such as machining vibrations [ 3 , 4 ] or tool wear [ 5 ]. Therefore, measuring cutting forces in the machining process is fundamental for condition monitoring and process optimization [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Cutting Forces Measurement for Milling Process by Using Working Tables with Integrated PVDF Thin-Film Sensors

Luo

Chong

Liu

2018

Sensors

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In the milling process, cutting forces contain key information about the machining process status in terms of workpiece quality and tool condition. On-line cutting force measurement is key for machining condition monitoring and machined surface quality assurance. This paper presents a novel instrumented working table with integrated polyvinylidene fluoride (PVDF) thin-film sensors, thus enabling the dynamic milling force measurement with compact structures. To achieve this, PVDF thin-film sensors are integrated into the working table to sense forces in different directions and the dedicated cutting force decoupling model is derived. A prototype instrumented working table is developed and validated. The validation demonstrates that profiles of the forces measured from the developed instrumented working table prototype and the dynamometer match well. Furthermore, the milling experiment results convey that the instrumented working table prototype could also identify the tool runout due to tool manufacturing or assembly errors, and the force signal spectrum analysis indicates that the developed working table can capture the tool passing frequency correctly, therefore, is suitable for the milling force measurement.

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Section: Introductionmentioning

confidence: 99%

Cutting Forces Measurement for Milling Process by Using Working Tables with Integrated PVDF Thin-Film Sensors

Luo

Chong

Liu

2018

Sensors

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“…Finally, the conclusion is presented in Section 5. en, each component is separately Hilbert transformed to obtain the transformed amplitude function a i (t). Finally, the Fourier transform is performed on each amplitude function signal to obtain the envelope spectrum [30,31].…”

Section: Organization Of Is Papermentioning

confidence: 99%

Fault Diagnosis of Bearings Based on KJADE and VNWOA‐LSSVM Algorithm

Liu

2019

Mathematical Problems in Engineering

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In order to accurately diagnose the faulty parts of the rolling bearing under different operating conditions, the KJADE (Kernel Function Joint Approximate Diagonalization of Eigenmatrices) algorithm is proposed to reduce the dimensionality of the high-dimensional feature data. Then, the VNWOA (Von Neumann Topology Whale Optimization Algorithm) is used to optimize the LSSVM (Least Squares Support Vector Machine) method to diagnose the fault type of the rolling bearing. The VNWOA algorithm is used to optimize the regularization parameters and kernel parameters of LSSVM. The low-dimensional nonlinear features contained in the multidomain feature set are extracted by KJADE and compared with the results of PCA, LDA, KPCA, and JADE methods. Finally, VNWOA-LSSVM is used to identify bearing faults and compare them with LSSVM, GA-LSSVM, PSO-LSSVM, and WOA-LSSVM. The results show that the recognition rate of fault diagnosis is up to 98.67% by using VNWOA-LSSVM. The method based on KJADE and VNWOA-LSSVM can diagnose and identify fault signals more effectively and has higher classification accuracy.

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“…Many works have been devoted to the issues of detection (Berczyński et al, 2001;Kuljanic et al, 2008;Liu et al, 2018;Yao et al, 2010;Jasiewicz and Powałka, 2018b), identification (Cao et al, 2013;Rusinek et al, 2015) and counteracting this phenomenon (Hayasaka et al, 2017;Munoa et al, 2016;Suzuki et al, 2016). Researchers mention the high importance of developing a reliable model, validated based on the results of experimental research, with particular emphasis on the representation of machine tool dynamics.…”

Section: Introductionmentioning

confidence: 99%

Application of the finite element method in the milling process stability diagnosis

Dunaj

Marchelek

Chodźko

2019

Journal of Theoretical and Applied Mechanics

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The article presents results of both experimental and FEM model-based investigations on chatter phenomena that occur in the end milling process. On the basis of chatter symptoms observed during machining, the mechanism of its occurrence has been identified. Then, a FEM model has been built to indicate machine tool elements responsible for loss of stability. Afterwards, experimental modal analysis has been conducted in order to validate the FEM model. Finally, on the basis of the validated model, the structural changes have been proposed and applied in the real object resulting in increased stability.

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Chatter detection in milling process based on VMD and energy entropy

Cited by 224 publications

References 39 publications

Cutting Forces Measurement for Milling Process by Using Working Tables with Integrated PVDF Thin-Film Sensors

Cutting Forces Measurement for Milling Process by Using Working Tables with Integrated PVDF Thin-Film Sensors

Fault Diagnosis of Bearings Based on KJADE and VNWOA‐LSSVM Algorithm

Application of the finite element method in the milling process stability diagnosis

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