Prediction of workpiece surface roughness using soft computing

Samanta, Biswanath; Erevelles, Winston F.; Omurtag, Y.

doi:10.1243/09544054jem1035

Cited by 36 publications

(22 citation statements)

References 17 publications

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“…The obtained accuracy of hardness prediction was acceptable; however, work cycle prediction was not at the desired accuracy level. Samanta et al 11 presented to model surface roughness in end-milling using soft computing (SC) or computational intelligence (CI) techniques. The machining parameters, namely, the spindle speed, feed rate, and depth of cut, were used as inputs to model the workpiece surface roughness.…”

Section: İ Asiltürkmentioning

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: İ Asiltürkmentioning

confidence: 99%

Application of Artificial Intelligent to Predict Surface Roughness

Asiltürk

2012

Experimental Techniques

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“…Artificial Neural Networks (ANN) are the most widely used AI technique Grzenda et al 2012;Díez-Pastor et al 2012;Benardos and Vosniakos 2002;Samanta et al 2008;Correa et al 2008), although other techniques like neuro-fuzzy inference systems (Samanta et al 2008), Bayesian networks (Correa et al 2008), genetic algorithms (Brezocnik et al 2004), swarm optimization techniques (Zainal et al 2016), and support vector machines (Prakasvudhisarn et al 2008) have also been tested for the same industrial task. Unfortunately, ANN models are highly dependent on the parameters of the neural networks (Bustillo et al 2011) and the process of fine-tuning these parameters is a highly time-consuming task that frequently requires expertise for good results.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised roughness prediction with partly unlabeled vibration data streams

Grzenda

Bustillo

2018

J Intell Manuf

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Experimental data sets that include tool settings, tool and machine-tool behavior, and surface roughness data for milling processes are usually of limited size, due mainly to the high costs of machining tests. This fact restricts the application of machine-learning techniques for surface roughness prediction in industrial settings. The primary objective of this work is to investigate the way data streams that are missing product features (i.e. unlabeled data streams) can contribute to the development of prediction models. The investigation is followed by a proposal for a semi-supervised approach to the development of roughness prediction models that can use partly unlabeled data to improve the accuracy of roughness prediction. Following this strategy, records collected during the milling process, which miss roughness measurements, but contain vibration data are used to increase the accuracy of the prediction models. The method proposed in this work is based on the selective use of such unlabelled instances, collected at tool settings that are not represented in the labeled data. This strategy, when applied properly, yields both extended training data sets and higher accuracy in the roughness prediction models that are derived from them. The scale of accuracy improvement and its statistical significance are shown in the study case of high-torque face milling of F114 steel. The semi-supervised approach proposed in this work has been used in combination with supervised k Nearest Neighbours and random forest techniques. Furthermore, the study of both continuous and discretized roughness prediction, showed higher gains in accuracy in the second.

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“…In papers [14][15][16] the authors model the surface roughness in end-milling of 6061 aluminium. The authors in paper [14] carry out the machining with high speed steel (HSS) and carbide tools under dry and wet conditions and they have developed a mathematical model using response surface methodology integrated with GAs.…”

Section: Introductionmentioning

confidence: 99%

“…The results show improvement in comparison with the other soft computing techniques like genetic programming (GP) and ANN. The authors in [16] model surface roughness by the application of multivariate regression analysis (MRA), ANN and ANFIS. The model input variables are the same as in paper [15].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Simulation of Surface Roughness in Face Milling

Šimunović¹,

Šimunović²,

Šarić³

2013

Int. j. simul. model.

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The paper presents a research on machined surface roughness in face milling of aluminium alloy on a low power cutting machine. Based on the results of the central-composite plan of experiment with varying machining parameters (number of revolutions -spindle speed n, feed rate f and depth of cut a) and the roughness observed as output variable, two models have been developed: a regression model and a model based on the application of neural networks (NN model). The regression model (coefficient of determination of 0.965 or 0.952 adjusted) with insignificant lack of fit, provides a very good fit and can be used to predict roughness throughout the region of experimentation. Likewise, the model based on the application of neural networks approximates well the experimental results with the level of RMS (Root Mean Square) error in the phase of validation of 4.01 %.

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Prediction of workpiece surface roughness using soft computing

Cited by 36 publications

References 17 publications

Application of Artificial Intelligent to Predict Surface Roughness

Application of Artificial Intelligent to Predict Surface Roughness

Semi-supervised roughness prediction with partly unlabeled vibration data streams

Modelling and Simulation of Surface Roughness in Face Milling

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