Stability of turning processes for periodic chip formation

Gyebrószki, Gergely; Bachrathy, Dániel; Csernák, Gábor; Stépàn, Gábor

doi:10.1007/s40436-018-0229-6

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…They considered the internal resonance and identified the most critical cutting parameters for chatter onset using time and frequency response curves from a theoretical point of view. Gyebroszki et al [52] studied the stability of turning systems when chatter can be influenced by periodic chip formation. These authors combined a model for the regenerative effect and a model for chip formation.…”

Section: Vibration Prediction In Turning Processesmentioning

confidence: 99%

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

et al. 2019

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The general trend towards lightweight components and stronger but difficult to machine materials leads to a higher probability of vibrations in machining systems. Amongst them, chatter vibrations are an old enemy for machinists with the most dramatic cases resulting in machine-tool failure, accelerated tool wear and tool breakage or part rejection due to unacceptable surface finish. To avoid vibrations, process designers tend to command conservative parameters limiting productivity. Among the different machining processes, turning is responsible of a great amount of the chip volume removed worldwide. This paper reports some of the main efforts from the scientific literature to predict stability and to avoid chatter with special emphasis on turning systems. There are different techniques and approaches to reduce and to avoid chatter effects. The objective of the paper is to summarize the current state of research in this hot topic, particularly (1) the mechanistic, analytical, and numerical methods for stability prediction in turning; (2) the available techniques for chatter detection and control; (3) the main active and passive techniques.

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Section: Vibration Prediction In Turning Processesmentioning

confidence: 99%

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

et al. 2019

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“…However, these variations are orders of magnitude larger than being explained as a measurement noise. There are high-speed phenomena during cutting, such as chip fragmentation [8,9], inhomogeneities in material quality [10][11][12], shear plane oscillation [13], rough surface of the workpiece, and friction. These phenomena play an important role in the amplitude of the forced vibrations, influencing the surface quality of the manufactured product and the detection of chatter [6,14,15].…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling of the cutting force in turning processes

Fodor

Sykora

Bachrathy

2020

Int J Adv Manuf Technol

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The main goal of this study is to introduce a stochastic extension of the already existing cutting force models. It is shown through orthogonal cutting force measurements how stochastic processes based on Gaussian white noise can be used to describe the cutting force in material removal processes. Based on these measurements, stochastic processes were fitted on the variation of the cutting force signals for different cutting parameters, such as cutting velocity, chip thickness, and rake angle. It is also shown that the variance of the measured force signal is usually around 4–9% of the average value, which is orders of magnitudes larger than the noise originating from the measurement system. Furthermore, the force signals have Gaussian distribution; therefore, the cutting force model can be extended by means of a multiplicative noise component.

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“…Gouskova et al [6] determined the stability of a continuous cutting process for an arbitrary arrangement of two cutters. Gyebroszki et al [7] created two models and combined them later, a surface regeneration model for turning operations and a chip formation model. They compared the results obtained and found that the time scale for the second model (chip formation) was smaller than the first one.…”

Section: Introductionmentioning

confidence: 99%

Machining Chatter Prediction Using a Data Learning Model

Cherukuri

Perez‐Bernabeu

Sellès

et al. 2019

JMMP

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Machining processes, including turning, are a critical capability for discrete part production. One limitation to high material removal rates and reduced cost in these processes is chatter, or unstable spindle speed-chip width combinations that exhibit a self-excited vibration. In this paper, an artificial neural network (ANN)—a data learning model—is applied to model turning stability. The novel approach is to use a physics-based process model—the analytical stability limit—to generate a (synthetic) data set that trains the ANN. This enables the process physics to be combined with data learning in a hybrid approach. As anticipated, it is observed that the number and distribution of training points influences the ability of the ANN model to capture the smaller, more closely spaced lobes that occur at lower spindle speeds. Overall, the ANN is successful (>90% accuracy) at predicting the stability behavior after appropriate training.

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Stability of turning processes for periodic chip formation

Cited by 8 publications

References 17 publications

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

Stochastic modeling of the cutting force in turning processes

Machining Chatter Prediction Using a Data Learning Model

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