A double input describing function approach for stability analysis in centerless grinding under interrupted cut

Bianchi, Giacomo; Leonesio, Marco; Safarzadeh, Hossein

doi:10.1007/s00170-020-05362-2

Cited by 9 publications

(8 citation statements)

References 23 publications

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“…A more comprehensive simulation-based analysis, including multiple technological constraints, is provided by Cui et al in [5]. Recent researches consider, in the frequency-domain stability analysis, also nonlinear effects, such as contact loss between WP and grinding wheel [4] [14].…”

Section: Fig 1: Centerless Grinding Setup Schemementioning

confidence: 99%

“…The WP profile is discretized in P points, via a radial z-buffer vector v. The final profile v f , produced by a process composed by N stages, can be computed ap-plying a linear transformation M on the initial profile v i : v f = M(γ 1 , .., γ N , h w1 , ..., h wN )v i (4) the effect of the transformation on waviness components in v i can be evaluated by M eigenvalues: the process attenuates all waviness components if the module of all eigenvalues is lower than 1, i.e. the spectral radius of M is lower than 1.…”

Section: Process Reconfiguration Usementioning

confidence: 99%

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Geometrical optimization of centerless grinding process by profiled workrest

Leonesio¹,

Wójcicki²,

Bianchi

2021

Int J Adv Manuf Technol

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This work presents a method to optimize centerless grinding geometrical configuration. It is based on the regulation of the horizontal axes of operating and regulating wheels of the machine, coupled with an opportune blade profile, allowing a continuous selection of workrest angle and workpiece height, without requiring blade substitution and/or manual interventions. The regulation of workrest angle and height cannot be done independently: their relationship is defined during blade profile design. In machines with two independent axes for regulating wheel and blade horizontal displacement, this regulation can be performed in process or in the setup phase. This results in optimal processing parameter choice leading to improved quality and shorter processing time as well as reduced setup time.

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Section: Fig 1: Centerless Grinding Setup Schemementioning

confidence: 99%

Section: Process Reconfiguration Usementioning

confidence: 99%

Geometrical optimization of centerless grinding process by profiled workrest

Leonesio¹,

Wójcicki²,

Bianchi

2021

Int J Adv Manuf Technol

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“…Rowe et al [10] introduced the geometric stability parameter derived from the Nyquist stability criterion, limited to integer lobes. Using these bases, Bianchi et al [11] considered the nonlinearity due to the loss of contact under large waviness, investigating its effect on process stability. Also Lizarralde [12] has applied similar approaches to guide setup and optimization of centreless plunge grinding processes, in order to reduce setup time and avoid geometric instabilities as a function of WP height and blade angle, taking into account machine-WP dynamic interaction.…”

Section: Centreless Grindingmentioning

confidence: 99%

“…Contact length l cs has been computed using Eqs. (9), (10) and (11). All the Figure 3 depicts the Short-time Fourier transform (STFT) of the WP profile for a simulated unstable operation.…”

Section: Simulation Modelmentioning

confidence: 99%

Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

Safarzadeh

Leonesio

Bianchi

et al. 2020

Int J Adv Manuf Technol

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This work proposes a model for suggesting optimal process configuration in plunge centreless grinding operations. Seven different approaches were implemented and compared: first principles model, neural network model with one hidden layer, support vector regression model with polynomial kernel function, Gaussian process regression model and hybrid versions of those three models. The first approach is based on an enhancement of the well-known numerical process simulation of geometrical instability. The model takes into account raw workpiece profile and possible wheel-workpiece loss of contact, which introduces an inherent limitation on the resulting profile waviness. Physical models, because of epistemic errors due to neglected or oversimplified functional relationships, can be too approximated for being considered in industrial applications. Moreover, in deterministic models, uncertainties affecting the various parameters are not explicitly considered. Complexity in centreless grinding models arises from phenomena like contact length dependency on local compliance, contact force and grinding wheel roughness, unpredicted material properties of the grinding wheel and workpiece, precision of the manual setup done by the operator, wheel wear and nature of wheel wear. In order to improve the overall model prediction accuracy and allow automated continuous learning, several machine learning techniques have been investigated: a Bayesian regularized neural network, an SVR model and a GPR model. To exploit the a priori knowledge embedded in physical models, hybrid models are proposed, where neural network, SVR and GPR models are fed by the nominal process parameters enriched with the roundness predicted by the first principle model. Those hybrid models result in an improved prediction capability.

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“…The second wheel (regulating wheel) has a carrier function, and its influence regulates the rotation of the workpiece. The rotation of the workpiece is also influenced by the positioning of the workpiece between the axes of the wheels, and the workrest helps to maintain the correct positioning [ 14 ]. Based on the geometric position of the grinding wheel, the regulating wheel, and the workpiece itself, we find a closed triangle of forces (marked with a red dashed line in Figure 1 ); these forces help us to guide the workpiece flawlessly and safely throughout the grinding cycle.…”

Section: Introductionmentioning

confidence: 99%

Research on the Oscillation in Centerless Grinding Technology When Machining Bearing Steel

et al. 2022

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In today’s engineering industry, technical diagnostics presents many advantages for improving the management of machining centers and automated production lines. As the fourth industrial revolution is currently being implemented, which includes machine diagnostics, the idea of adding information from the field of vibrodiagnostics was born. The vibration of the workpiece or machine tool negatively affects the geometric parameters of the machined surfaces of the workpiece. Through vibrodiagnostics, the influence of cutting parameters on the oscillation of a bearing steel workpiece during centerless grinding is investigated. The presented publication deals with the vibration of the mechanical parts of a centerless grinding machine. The oscillations are recorded by acceleration sensors, which are also placed on the support ruler in which the workpieces are guided, and the recorded data are input parameters for statistical processing of acceleration values in the form of statistical characteristics (minimum, lower quartile, median, upper quartile, maximum). In this paper, this procedure was applied for the selection of the optimum cutting parameters (for the speed of the support wheel), where the machining parameters at which the minimum oscillation values occur were selected based on the above-mentioned statistical characteristics. This optimization procedure revealed increased vibration values which reached the highest amplitude on the ruler, namely accelerations of 11 m/s2, the origin of which was subsequently detected by STFT because the occurrence of resonance events or the excitation of natural frequencies of the machine were suspected. The STFT analysis identified a resonant region at machine start-up determined by the spindle speed which excites the resonance on the machine. The speed range between 1950 and 2150 rpm, which corresponds to the built-up resonance, was provided to the technologists to ensure that the machine was not operated around this resonance region at 400 and 760 Hz until the undesired phenomenon was eliminated. The results of the individual measurements provided information on the ideal setting of the cutting parameters and the current state of the machine.

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A double input describing function approach for stability analysis in centerless grinding under interrupted cut

Cited by 9 publications

References 23 publications

Geometrical optimization of centerless grinding process by profiled workrest

Geometrical optimization of centerless grinding process by profiled workrest

Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

Research on the Oscillation in Centerless Grinding Technology When Machining Bearing Steel

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