Stability analysis in milling of the thin walled part considering multiple variables of manufacturing systems

Dong, Xinfeng; Zhang, Weimin

doi:10.1007/s00170-016-9072-8

Cited by 14 publications

(2 citation statements)

References 28 publications

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“…Considerations in the chatter detection tests include the cutting tool type and geometry, tool material, tool immersion, tool runout, workpiece material, chip load and milling types. The works by Jensen et al and Dong et al [305,306] studied the effect of diverse milling parameters in the SLD and the impact on chatter occurrence. Tool immersion was studied by Merdol and Altintas [307], while the effect of the workpiece material is evaluated by Ibañez et al [308].…”

Section: Cutting Parameters and Experiments Setupmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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Section: Cutting Parameters and Experiments Setupmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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show abstract

“…ese include the zero-order frequency method [1], numerical integration method [2], temporal finite element analysis (TFEA) [3], semidiscretization method (SDM) [4,5], semidiscretization method on a Shannon standard orthogonal basis [6,7], Lambert W function-based method [8], Legendre polynomial-based method [9], Adams-Simpson-based method [10], Runge-Kutta-based complete discretization method [11], and Chebyshev collocation method for delay differential equations [12].…”

Section: Introductionmentioning

confidence: 99%

Stability of Milling Process with Variable Spindle Speed Using Runge–Kutta-Based Complete Method

Zhao

2021

Mathematical Problems in Engineering

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The variable-spindle-speed (VSS) technique is effective in preventing regenerative chatter in milling processes. However, spindle-speed-modulation parameters should be deliberately selected to augment the material removal rate. Stability-prediction algorithms of stability predicting play an important role in this respect, as they allow the prediction of stability for all ranges of a given spindle speed. The increase in calculation time in variable-spindle-speed milling, which is caused by the modulation frequency, hinders its practical use in the workshop. In this paper, a Runge–Kutta-based complete discretization method (RKCDM) is presented to predict the stability of milling with variable spindle speeds, which is described by a set of delay differential equations (DDEs) with time-periodic coefficients and time-varying delay. The convergence and calculation efficiency are compared with those of the semidiscretization method (SDM) under different testing configurations and milling conditions. Results show that RKCDM is more accurate and saves at least 50% of the calculation time of SDM. The effects of modulation parameters on the stability of VSS milling are explored through stability lobe diagrams produced from RKCDM.

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Automated Upgraded Generalized Full-Discretization Method: Application to the Stability Study of a Thin-Walled Milling Process

Ozoegwu

Eberhard

2020

Mechanical Sciences

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Stability analysis in milling of the thin walled part considering multiple variables of manufacturing systems

Cited by 14 publications

References 28 publications

Chatter detection in milling processes—a review on signal processing and condition classification

Chatter detection in milling processes—a review on signal processing and condition classification

Stability of Milling Process with Variable Spindle Speed Using Runge–Kutta-Based Complete Method

Automated Upgraded Generalized Full-Discretization Method: Application to the Stability Study of a Thin-Walled Milling Process

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