Development of Empirical Models for Surface Roughness Prediction in Finish Turning

Wang, X.; Feng, Cheng

doi:10.1007/s001700200162

Cited by 250 publications

(60 citation statements)

References 12 publications

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“…Using the data mining approach [39][40][41][42], carry out three variables functions approximation based on experimental data with the help of the neural network.…”

Section: And Step Twomentioning

confidence: 99%

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

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High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of highstrength steel featuring three factors-cutting speed V, feed rate f, and depth of cut t-on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.

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“…Using the data mining approach [39][40][41][42], carry out three variables functions approximation based on experimental data with the help of the neural network.…”

Section: And Step Twomentioning

confidence: 99%

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

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“…Suresh et al (2002) concluded that genetic algorithms can be applied to determine minimum and maximum values of surface roughness and hence, optimum machining conditions. Feng and Wang (2002) reported that surface quality influenced the properties like wear resistance and friction of machined parts. Kirby et al (2004) developed a model to predict surface roughness in turning operation.…”

Section: Introductionmentioning

confidence: 99%

Influence of machining parameters on surface roughness and dry friction

Kumar¹,

Kumar²

2016

10.5267/j.esm

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Dry friction depends on the surface topography which, in turn, is governed by machining parameters in addition to several other factors,. Therefore, in order to establish a qualitative relationship among these factors, the surface roughness and coefficient of static friction are measured for specimens machined on lathe and shaper machines with different values of machining parameters. For the case of lathe, the measured Ra value is found to increase with increase in feed rate and depth of cut, whereas, a marginal decrease is observed with increasing spindle speed. Similar results with respect to cutting speed have been obtained for the case of shaper machine. On the other hand, the coefficient of static friction, measured on steel substrate using inclined plane method, is found to decrease with increasing Ra values for both the specimen types.

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“…The techniques employed include response surface methodology (RSM) combined with factorial design approach [11], RSM combined with data extracted from workpiece surface profile simulated from images of tool inserts [12], statistical models based on experimental data [13] and artificial neural network-based methodologies [14][15][16]. Hessainia et al [17] developed a surface roughness model in hard turning by exploiting the response surface model.…”

Section: Sung Loh Ratnam: Simulation Approach For Surface Roughnessmentioning

confidence: 99%

Simulation Approach for Surface Roughness Interval Prediction in Finish Turning

Sung¹,

Loh²,

Ratnam³

2016

Int. j. simul. model.

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Existing simulation models used in predicting the surface roughness of a workpiece in finish turning are based on an ideal circular cutting tool nose profile. This leads to a single predicted roughness value for a given set of input parameters. In this paper, a simulation approach that considers the random tool nose profile micro-deviations as well as the tool chatter vibration to predict a roughness interval is proposed. The nose profiles used in the simulation were extracted from images of the real cutting tool inserts using sub-pixel edge location. The chatter vibration signal was reconstructed from the measured signals and was superimposed onto the extracted nose profile. The roughness data were computed from 24 simulated workpiece surface profiles and used to determine the 95 % roughness prediction interval. Comparison with the experimental results showed that 100 %, 96 % and 96 % of the R t , R a and R q roughness values obtained experimentally fell within the predicted roughness intervals.

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Development of Empirical Models for Surface Roughness Prediction in Finish Turning

Cited by 250 publications

References 12 publications

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Influence of machining parameters on surface roughness and dry friction

Simulation Approach for Surface Roughness Interval Prediction in Finish Turning

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