Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool

Chavoshi, Saeed Zare; Tajdari, Mehdi

doi:10.1007/s12289-009-0679-2

Cited by 55 publications

(26 citation statements)

References 7 publications

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“…Özel and Karpat 9 developed a one‐output ANN to predict tool flank wear using machining parameters, such as cutting forces and material hardness, during the hard turning process. Chavoshi and Tajdari 10 machined AISI 4140 grade steel with hard turning process by using CBN inserts with hardness ( H ) and cutting speed as variables to study the variation of R a value. The models, which were produced using regression and ANN, were used in specifying optimum parameters for surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligent to Predict Surface Roughness

Asiltürk

2012

Experimental Techniques

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This article proposes for predicting the surface roughness of AISI 1040 steel material using the artificial intelligent. Cutting speed, feed rate, depth of cut, and nose radius have been taken into consideration as input factors and corresponding surface roughness values (Ra, Rt) as output. A series of experiments have been carried out in accordance with a full factorial design on the CNC lathe to obtain the data used for the training and testing of an artificial neural network (ANN). The developed MATLAB TM interface was used to predict surface roughness. Multilayer perceptron structure, which is a kind of feed forward ANNs, is applied to model and prediction of the surface roughness in turning operations. The number of iterations used was 20,000. MSE = 1E − 4 and R2 = 0.9991 were achieved using the developed ANN Model. The obtained results indicate that the ANN algorithm coupled with back propagation neural network is an efficient and accurate method in predicting of surface roughness in turning.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligent to Predict Surface Roughness

Asiltürk

2012

Experimental Techniques

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“…The different types of artificial neural network are, in practice back propagation neural networks (BPNNs), counter propagation neural networks (CPNNs), and radial basis function neural networks (RBFNs), and so forth. Even though BPNNs is widely used for a variety of systems, especially in the field of surface roughness prediction as it appears in these recent articles [22][23][24][25][26], it suffers from a number of drawbacks. First, it is very slow to converge because of the use of sigmoid nonlinear transformation functions.…”

Section: Radial Basis Function Network (Rbfn) Approachmentioning

confidence: 99%

“…The best result takes place when the number of generation is 500. After this number of generation, no improvement has been noticed in the fitness function (25). In the fourth stage, the number of bits has been changed in the range of 16-64.…”

Section: Performance Of Genetic Fuzzy Inference Systems (G-fis)mentioning

confidence: 99%

Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

Sharkawy

2011

Applied Computational Intelligence and Soft Computing

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A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insureperfectoptimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case.

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“…have been used widely in different machining operation. For example, Zare Chavoshi and Tajdari [13] modelled the surface roughness in hard turning operation of AISI 4140 using regression analysis and artificial neural network. Analysis and estimation of state variables in computer numerical control (CNC) face milling operation of AL6061 was performed by Soleymani Yazdi and Zare Chavoshi [14].…”

Section: Introductionmentioning

confidence: 99%

Prediction and analysis of radial overcut in holes drilled by electrochemical machining process

Tajdari

Chavoshi

2013

Open Engineering

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Abstract:Radial overcut predictive models using multiple regression analysis, artificial neural network and co-active neurofuzzy inference system are developed to predict the radial overcut during electrochemical drilling with vacuum extraction of electrolyte. Four process parameters, electrolyte concentration, voltage, initial machining gap and tool feed rate, are selected to develop the models. The comparison between the results of the presented models shows that the artificial neural network and co-active neuro-fuzzy inference system models can predict the radial overcut with an average relative error of nearly 5%. Main effect and interaction plots are generated to study the effects of process parameters on the radial overcut. The analysis shows that the voltage, electrolyte concentration and tool feed rate have significant effect on radial overcut, respectively, while initial machining gap has a little effect. It is also found that the increase of the voltage and electrolyte concentration increases the radial overcut and the increase of the tool feed rate decreases the radial overcut. Keywords

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Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool

Cited by 55 publications

References 7 publications

Application of Artificial Intelligent to Predict Surface Roughness

Application of Artificial Intelligent to Predict Surface Roughness

Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

Prediction and analysis of radial overcut in holes drilled by electrochemical machining process

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