Identification of significant parameters and appropriate levels in honing of cylinder liners

Kanthababu, M.; Shunmugam, M.S.; Singaperumal, M.

doi:10.1504/ijmmm.2009.023114

Cited by 21 publications

(16 citation statements)

References 11 publications

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“…Other factors considered were lubricating oil and workpiece material [35]. Kanthababu et al, who did not take into account grain size as a factor, found that pressure was one of the main parameters affecting roughness, together with honing time [36].…”

Section: Figure 12 Surface Plot Ra Vs De;gsmentioning

confidence: 99%

Indirect model for roughness in rough honing processes based on artificial neural networks

Sivatte-Adroer

Parra

Buj-Corral

et al. 2016

Precision Engineering

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In the present paper an indirect model based on neural networks is presented for modelling the rough honing process. It allows obtaining values to be set for different process variables (linear speed, tangential speed, pressure of abrasive stones, grain size of abrasive and density of abrasive) as a function of required average roughness Ra. A multilayer perceptron (feedforward) with a backpropagation (BP) training system was used for defining neural networks. Several configurations were tested with different strategies, number of layers, number of neurons and transfer function. Best configuration for the network was searched by means of two different methods, trial and error and Taguchi design of experiments (DOE). In both cases, best configuration corresponds to a single network with two hidden layers. Once best configuration was found, a network was defined for obtaining honing parameters as a function of required roughness parameters related to Abbott-Firestone curve, Rk, Rpk and Rvk.

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Section: Figure 12 Surface Plot Ra Vs De;gsmentioning

confidence: 99%

Indirect model for roughness in rough honing processes based on artificial neural networks

Sivatte-Adroer

Parra

Buj-Corral

et al. 2016

Precision Engineering

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“…Kanthababu et al monitored acoustic emission for rough, semi-finish and plateau honing for fresh and worn out tools. They found that the dominant frequency of the signal is sensitive to the cutting conditions [12] The acoustic signal is usually analysed in the frequency domain, by means of Fast Fourier Transform (FFT), and also in the time-frequency domain (TFS) [13]. Other time-frequency (TF) methods are available for analysing the acoustic signal, such as Hilbert Huang Transform (HHT) and wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission analysis for the detection of appropriate cutting operations in honing processes

Buj-Corral

Alvarez-Florez

Domínguez-Fernández

2018

Mechanical Systems and Signal Processing

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In the present paper, acoustic emission was studied in honing experiments obtained with different abrasive densities, 15, 30, 45 and 60. In addition, 2D and 3D roughness, material removal rate and tool wear were determined. In order to treat the sound signal emitted during the machining process, two methods of analysis were compared: Fast Fourier Transform (FFT) and Hilbert Huang Transform (HHT). When density 15 is used, the number of cutting grains is insufficient to provide correct cutting, while clogging appears with densities 45 and 60. The results were confirmed by means of treatment of the sound signal. In addition, a new parameter S was defined as the relationship between energy in low and high frequencies contained within the emitted sound. The selected density of 30 corresponds to S values between 0.1 and 1. Correct cutting operations in honing processes are dependent on the density of the abrasive employed. The density value to be used can be selected by means of measurement and analysis of acoustic emissions during the honing operation. Thus, honing processes can be monitored without needing to stop the process.

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“…The expression β0 is the sample intercept, i.e. the value of Y when X is zero [3]. The final adapted models for the industrial production machine are shown in Table 8.…”

Section: Correlation Analysis Between the Values Of The Industrial Mamentioning

confidence: 99%

Approximation of An Experimental Model Obtained in A Testing Machine for use in an Industrial Production Machine

Salcedo¹,

Buj-Corral²,

Carrillo³

2018

IJCTR

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Design of experiments (DOE) is a powerful tool that allows performing the modeling and analysis of the influence of factors of the process on the specified variables, which are often called response variables. Being a systematic procedure to analyze the effect of the response variables, and the controllable factors that are modified during the execution of the experiment.Within the different strategies of the design of experiments, the factorial design is one of the most widely used. The problem with factorial designs is a large number of experiments that may be necessary.It is not convenient to perform the tests of experimental designs in an industrial machine, because it is more difficult to control and measure the variables accurately and in an agile way. Moreover, if an industrial machine has to be used, it is not feasible to perform a large number of trials, because it would be necessary to interrupt production.Therefore it was decided to conduct an investigation in which experimental models were obtained in a testing machine that was specially developed for that purpose. Later, data were collected from an industrial machine that performed the same process, and with the same machining conditions. The problem to be solved is to what extent the models obtained in the testing machine can be adjusted or not to predict the process in industrial machines.A study was done by means of regression analysis of the correlation between the valuesobtained in the test machine and those obtained in the industrial machine. It was observed that the trends of roughness parameters given by the models obtained in the testing machine are similar to those obtained in the industrial process. For this reason it is feasible to adapt the models obtained in the testing machine for the industrial machine.

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Identification of significant parameters and appropriate levels in honing of cylinder liners

Cited by 21 publications

References 11 publications

Indirect model for roughness in rough honing processes based on artificial neural networks

Indirect model for roughness in rough honing processes based on artificial neural networks

Acoustic emission analysis for the detection of appropriate cutting operations in honing processes

Approximation of An Experimental Model Obtained in A Testing Machine for use in an Industrial Production Machine

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