Surface roughness prediction in turning using artificial neural network

Pal, Surjya K.; Chakraborty, Debabrata

doi:10.1007/s00521-005-0468-x

Cited by 85 publications

(27 citation statements)

References 24 publications

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“…It indicates that the developed model can be effectively used to predict the surface roughness on the machining of D2 steel with 95% confidence intervals. Pal and Chakraborty (2005) developed a back propagation neural network model for the prediction of surface roughness in turning operation of mild steel workpiece using high speed steel as the cutting tool. The performance of the trained neural network was tested with experimental data, and found to be in good agreement.…”

Section: Review Of Literaturementioning

confidence: 99%

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Sahoo¹,

Rout²,

Das³

2015

10.5267/j.ijiec

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The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R 2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

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Section: Review Of Literaturementioning

confidence: 99%

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Sahoo¹,

Rout²,

Das³

2015

10.5267/j.ijiec

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“…ANN has an input layer and an output layer with different numbers of neurons, which are interconnected to each other by one or more hidden layers. Among the various ANN architectures, the most well-known is a feed-forward neural network (FFNN), which has three distinct layers with several neurons: the input layer, the hidden layer(s) and the output layer (Pal & Chakraborty, 2005). In order to illustrate a simple topology of ANN, the ANN architecture used in this research article is present in Figure 1.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Jelatin Çözeltilerinin Dinamik Viskozitesine Yapay Sinir Ağı (YSA) Yaklaşımı: Esnek Hesaplama Çalışması

Demirbay¹,

Karakullukçu²

2020

European Journal of Science and Technology

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In this research, we present a multi-layered feed-forward neural network (ANN) model developed for prediction of dynamic viscosity of aqueous gelatin solutions using experimental data collected from a number of measurements. In ANN architecture, shear stress, shear strain, torque of spindle, the angular velocity of spindle together with mass concentrations of gelatin solutions were introduced as input neurons, whereas dynamic viscosity of aqueous gelatin solutions was assigned as a single output neuron to be predicted. Developed ANN model was trained using backpropagation algorithm optimized with Bayesian regulation. Optimal geometry of the hidden layer was first studied to search out the ANN architecture which yields the most accurate performance results. Mean squared error (MSE), mean absolute error (MAE), root-mean-squared error (RMSE), determination of coefficient ( 2 ), the variance accounted for (VAF) and regression analyses were used as performance assessment parameters for suggested network models. Sensitivity analysis was carried out to investigate the most effective input neuron strongly influencing the performance of the developed ANN model. As a result, the use of 8 neurons in the hidden layer has shown excellent performance results yielding the least MSE and the highest 2 values compared to other suggested ANN models. Upon sensitivity analysis, the shear rate was found to be the most effective input neuron significantly affecting network performance. ANN-based predicted dynamic viscosity values were found to be in excellent agreement with measured viscosity values, demonstrating the robustness as well as the accuracy of the developed ANN model. Developed ANN model can, therefore, be effectively used to predict the dynamic viscosity of aqueous polymer solutions using the same input and output parameters in specific data range reported in this paper with statistical details.

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“…The ANN model for surface roughness prediction was assessed with an error varying from 2 to 25% under different process conditions. Pal and Chakraborty (2005) presented a surface roughness model based on ANN for a milling operation where cutting force measurements were included. The inputs of the neural network model were the cutting speed, feed rate and depth of cut as process parametric conditions, and the feed and cutting forces as the measured parameters.…”

Section: Ann Models Applied To Part Accuracy Predictionmentioning

confidence: 99%

“…In spite of these strategies for choosing the number of hidden neurons, the number of neurons of ANN models related to part accuracy have been commonly obtained by trial and error procedures using ANN structure combinations of one, two and three hidden layers (e.g. Tsai, ; Pal and Chakraborty (2005)). …”

Section: Guidelines For Ann Modellingmentioning

confidence: 99%

A review of artificial intelligent approaches applied to part accuracy prediction

Abellán-Nebot

2010

IJMMM

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Nowadays, despite the large volume of worldwide academic research on various aspects of metal cutting the control of workpiece precision still relies on machine-tool operator's experience and trial and error runs. In order to increase the efficiency of machining systems, many empirical models based on Artificial Intelligent (AI) approaches have been proposed in the past, where important process improvements were reported. This paper overviews the AI approaches applied in machining operations to predict part accuracy in terms of dimensional deviations and surface roughness. Successful techniques applied in this field such as Artificial Neural Networks, Fuzzy Logic, Adaptive-Network-based Fuzzy Inference Systems and Bayesian Networks are briefly reviewed and compared to facilitate its use. For each AI approach, the most relevant research works are described and based on those works some guidelines are proposed for its implementation. In addition, advantages and drawbacks of each approach are summarised and a generic guideline for AI approaches selection is proposed.

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Surface roughness prediction in turning using artificial neural network

Cited by 85 publications

References 24 publications

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Response surface and artificial neural network prediction model and optimization for surface roughness in machining

Jelatin Çözeltilerinin Dinamik Viskozitesine Yapay Sinir Ağı (YSA) Yaklaşımı: Esnek Hesaplama Çalışması

A review of artificial intelligent approaches applied to part accuracy prediction

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