A novel approach to predict surface roughness in machining operations using fuzzy set theory

Tseng, Tzu Liang; Konada, Udayvarun; Kwon, Yongjin

doi:10.1016/j.jcde.2015.04.002

Cited by 54 publications

(30 citation statements)

References 11 publications

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“…Many researchers have proposed different approaches to predict surface roughness based on machining theory . Surface measurement is primarily divided into two categories: (a) contact measurement and (b) noncontact measurement .…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have proposed different approaches to predict surface roughness based on machining theory. [4][5][6][7][8][9] Surface measurement is primarily divided into two categories: (a) contact measurement and (b) noncontact measurement. [10][11][12] Contact-type measurements are currently used due to the compact design, high measurement accuracy, and the ability to deliver consistent output for surface inspection.…”

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confidence: 99%

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Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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In the present work, a machine vision system is introduced, which captures images and extracts surface texture features of machined surfaces. The texture feature parameters are extracted using the gray‐level co‐occurrence matrix and correlated with different surface roughness parameters recorded by a contact‐type surface profilometer. The image acquisition carried out at different roughness levels in order to extract texture features. The variation between each texture features and surface roughness parameter is investigated. Multiple regression models are developed to predict the subjective estimation of surface roughness parameter (Ra) and qualitative detection of the degree of surface roughness. It is observed that the linear detection model shows better performance characteristics compared with a nonlinear detection model. The comparison between measured and predicted results shows that the linear detection model had a maximum relative error of 2.01%, drastically better than nonlinear detection model of −9.60% error parts, hence indicating better surface detection capability over the nonlinear detection model. The results demonstrate that the prediction of surface roughness using linear regression model is a reliable approach of noncontact measurement.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Patel

Kiran

Vakharia

2020

Engineering Reports

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“…Kalidass et al [13] developed a mathematical model for surface roughness prediction in terms of several cutting parameters, such as rotational speed, feed rate, and depth of cut using a response surface methodology. Tseng et al [14] developed an equation to predict surface profile of machined parts in Computerized Numerical Control (CNC) milling by determining the significant factors from experiments and fuzzy set theory.…”

Section: Introductionmentioning

confidence: 99%

Models for Prediction of Surface Roughness in a Face Milling Process Using Triangular Inserts

Wang

Barber

et al. 2019

Lubricants

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Research was carried out to develop a mathematical model based on the cutting tool surface profile geometry to predict surface roughness in face milling. Previous models were derived using either the simple assumption of a perfectly round tool nose or statistical analysis based on a large number of experiments. In this research, three milling cases were defined based on the magnitude of the feed rate using a triangular insert with a round corner. In case 1, the machine marks only consisted of a series of arcs. In case 2, the machine marks included a series of an arc and one straight line. In case 3, the machine marks consisted of a series of an arc and two straight lines. Three different equations for surface roughness prediction were obtained based on each of the three cases. Experiments were done to validate the models, and the results showed that the mathematical models had good correlation with experimental results.

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“…The input parameters of the ANNs were the following: rotational speed (n), feed (f), depth of cut (a p ), pre-tool flank wear and vibration level. The output parameters were the corresponding calculated fractal parameters: fractal dimension "D" and vertical scaling parameter "G." Tseng et al [6] used design of experiments (DoE) to determine the significant factors and then fuzzy logic approach for the prediction of surface roughness. The factors considered for DoE were the depth of cut (a p ), feed per tooth (f z ), cutting speed (v c ), tool nose radius (r ε ), the use of cutting fluid and the three components of the cutting force (Fx, Fy, Fz).…”

Section: Introductionmentioning

confidence: 99%

Effects of Setting Errors (Insert Run-Outs) on Surface Roughness in Face Milling When Using Circular Inserts

Felhő

Kundrák

2018

Machines

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In face milling, the roughness of the machined surface varies due to the movement of the cutting edge. Changes in roughness parameter values in the axis of rotation (symmetry plane) have been examined at a constant depth of cut for symmetrical milling. In this paper, the effect of increasing feed per tooth on the topography of the surface is studied in fly-cutting and in multi-point face milling. The study takes into account the axial run-out of the inserts. Theoretical roughness values were modelled, the real values were tested in experiments and in both cases the impact of the run-out of the cutting edges and the change of the chip cross-section were also taken into account. Based on the performed experiments it can be stated that the accuracy of the introduced roughness prediction method increases with the increase in feed and therefore the application of the method in the case of high-feed milling is particularly effective. The results have also shown that the run-out of the insert significantly effects the roughness of the milled surfaces and therefore the measurement and minimization of these setting errors is essential.

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A novel approach to predict surface roughness in machining operations using fuzzy set theory

Cited by 54 publications

References 11 publications

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Modeling and prediction of surface roughness using multiple regressions: A noncontact approach

Models for Prediction of Surface Roughness in a Face Milling Process Using Triangular Inserts

Effects of Setting Errors (Insert Run-Outs) on Surface Roughness in Face Milling When Using Circular Inserts

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