Experimental Study of Thin Wall Milling Chatter Stability Nonlinear Criterion

Wu, Shi; Li, Rongyi; Liu, Xianli; Yang, Lin; Zhu, Meiwen

doi:10.1016/j.procir.2016.10.075

Cited by 20 publications

(12 citation statements)

References 10 publications

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“…Another reason why vibration is highly undesirable in production is the randomness of the end product characteristics (i.e., fulfilling target dimensions, shape and surface finish). Vibrations are even less acceptable in finishing [28] and milling thin-walled structures [29,30,31]. It is for these reasons that the stability of the process is of utmost importance.…”

Section: Introduction—state-of-the-artmentioning

confidence: 99%

“…An alternative approach for stability analysis was employed in Wu et al [31], which combined phase plane method, Poincare method and spectral analysis. The researchers explored the relationship between the maximum Lyapunov exponent, and the changes in milling depth and spindle speed, which allowed them to determine stable milling conditions in contouring.…”

Section: Introduction—state-of-the-artmentioning

confidence: 99%

“…The most relevant factors to the stability of milling are: machining strategy, machine tool, the cutting tool and its mounting system, chucks, the workpiece, cooling and lubrication and cutting data. Due to their specific machinability characteristics, resulting in their susceptibility to chatter during milling, thin-walled aluminium alloys, such as EN AW-7075 T6 [31,35], EN AW-2024-T351 [36] and EN AW-6061-T6 [30,37], typically undergo stability analyses. Rusinek et al [35] investigated the cutting forces and acceleration to specify stable machining conditions for workpieces withdecreasing wall thickness.…”

Section: Introduction—state-of-the-artmentioning

confidence: 99%

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Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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This paper set out to investigate the effect of cutting speed vc and trochoidal step str modification on selected machinability parameters (the cutting force components and vibration). In addition, for a more detailed analysis, selected surface roughness parameters were investigated. The research was carried out for two grades of magnesium alloys—AZ91D and AZ31—and aimed to determine stable machining parameters and to investigate the dynamics of the milling process, i.e., the resulting change in the cutting force components and in vibration. The tests were performed for the specified range of cutting parameters: vc = 400–1200 m/min and str = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in vc resulted in the reduction of the cutting force components and the displacement and level of vibration recorded in tests. Selected cutting parameters were modelled by means of Statistica Artificial Neural Networks (Radial Basis Function and Multilayered Perceptron), which, furthermore, confirmed the suitability of neural networks as a tool for prediction of the cutting force and vibration in milling of magnesium alloys.

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Section: Introduction—state-of-the-artmentioning

confidence: 99%

Section: Introduction—state-of-the-artmentioning

confidence: 99%

Section: Introduction—state-of-the-artmentioning

confidence: 99%

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Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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“…The results demonstrate that under unstable conditions the amplitude of vibration increases even by 5 times, which affects the surface quality after machining. Stability analyses are often performed for thin-walled parts made of aluminium alloys such as EN AW-7075 T6 [17,22], EN AW-2024-T351 [2] and EN AW-6061-T6 [23,24]. The works [17,18] investigate acceleration and cutting force components to determine the stability limit for parts with a varying (decreasing) thickness of the wall.…”

Section: The State Of Knowledgementioning

confidence: 99%

“…The stability of a milling process can also be investigated with the following methods [22]: chase plane method, Poincare's method and spectra analysis. This paper discusses the relationship between the maximum Lyapunov exponent and the spindle speed and milling depth.…”

Section: The State Of Knowledgementioning

confidence: 99%

Artificial Neural Network Modelling of Vibration in the Milling of AZ91D Alloy

Zagórski¹,

Kulisz²,

Semeniuk³

et al. 2017

Adv. Sci. Technol. Res. J.

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The paper reports the results of artificial neural network modelling of vibration in a milling process of magnesium alloy AZ91D by a TiAlN-coated carbide tool. Vibrations in machining processes are regarded as an additional, absolute machinability index. The modelling was performed using the so-called "black box" model. The best fit was determined for the input and output data obtained from the machining process. The simulations were performed by the Statistica software using two types of neural networks: RBF (Radial Basis Function) and MLP (Multi-Layered Perceptron).

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Min Axial Interference Measurement in Elastic Elements of Vibration-Damping Mandrels for Thin-Wall Shell

Yamnikov

Bogomolov

2022

Lecture Notes in Mechanical Engineering

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Experimental Study of Thin Wall Milling Chatter Stability Nonlinear Criterion

Cited by 20 publications

References 10 publications

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Artificial Neural Network Modelling of Vibration in the Milling of AZ91D Alloy

Min Axial Interference Measurement in Elastic Elements of Vibration-Damping Mandrels for Thin-Wall Shell

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