Estimation of stability lobe diagrams in milling with continuous learning algorithms

Friedrich, Jens; Hinze, Christoph; Renner, Anton; Verl, Alexander; Lechler, Armin

doi:10.1016/j.rcim.2015.10.003

Cited by 44 publications

(12 citation statements)

References 27 publications

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“…Milling process limit stability is also determined by the use of methods based on constant training algorithms. The works reported in Friedrich et al [38] introduced a novel method for the assessment of process stability, carried out by measuring acceleration signals. The paper introduced a new criterion providing information on the prediction assessment for a given input region—the multidimensional stability lobe diagram (MSDL).…”

Section: Introduction—state-of-the-artmentioning

confidence: 99%

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%

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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“…Neural network modelling of machining dynamics is an exciting, powerful and yet not fully harnessed method [21]. When implemented, it may facilitate the choice of most suitable technological parameters, which foster optimisation of milling cutting (efficiency boost) [22,23].…”

Section: State Of the Artmentioning

confidence: 99%

Effect of technological parameters on vibration acceleration in milling and vibration prediction with artificial neural networks

Zagórski

Kulisz

2019

MATEC Web Conf.

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This paper reports on the study of vibration acceleration in milling and vibration prediction by means of artificial neural networks. The milling process, carried out on AZ91D magnesium alloy with a PCD milling cutter, was monitored to observe the extent to which the change of selected technological parameters (vc, fz, ap) affects vibration acceleration ax, ay and az. The experimental data have shown a significant impact of technological parameters on maximum and RMS vibration acceleration. The simulation works employed the artificial neural networks modelled with Statistica Neural Network software. Two types of neural networks were employed: MLP (Multi-Layered Perceptron) and RBF (Radial Basis Function).

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“…The paper [8] offers a new approach to assessing process stability by measuring acceleration signals. A multidimensional stability lobe diagram (MSDL) is used with two new algorithms of constant training.…”

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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Estimation of stability lobe diagrams in milling with continuous learning algorithms

Cited by 44 publications

References 27 publications

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Effect of technological parameters on vibration acceleration in milling and vibration prediction with artificial neural networks

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

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