A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning

Pontes, Fabrício José; Silva, Messias Borges; Ferreira, João Roberto; Paiva, Anderson Paulo de; Balestrassi, Pedro Paulo; Schönhorst, Gustavo Bonnard

doi:10.1590/s1678-58782010000500010

Cited by 7 publications

(12 citation statements)

References 30 publications

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“…Myers (1999) presented a thorough discussion of the RSM and a historical review of the state-of-the-art of RSM, as well as some suggestions for future research. Pontes et al (2010), in their concern with surface roughness in hard turning, proved that DOE is an efficient predictive statistical tool when designing Artificial Neural Networks of Radial Basis Function architectures. Santhanakumar, Adalarasan, Siddharth, and Velayudham (2016) employed a grey-based RSM and Taguchi's L27 orthogonal design on the study of the influence of cutting speed (Vc), feed rate (f) and depth of cut (ap) on tool's flank wear and on roughness in rough machining of high-strength materials; at the same time they reduced their multi-response optimization case to a single-response one, they found that, even though the surface finish's quality was improved by the increase of the cutting speed, as feed rate and depth of cut increase to elevated values the roughness also increased.…”

Section: Methodsmentioning

confidence: 99%

Optimizing production in machining of hardened steels using response surface methodology

et al. 2019

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This paper presents the modeling of tool life and surface roughness for machining AISI 52100 steel with a hardness of 50 HRC through Design of Experiments and Response Surface Methodology (RSM) with a view to enhance the quality and productivity. Knowing that the tool life and surface roughness are factors that influence the quality of the product, this study used the statistical tool of RSM in the search of factors that better determine optimal models. The models obtained prioritize the product quality and the cutting productivity. Results from Analysis of Variance demonstrated that the mathematical models elaborated allowed the prediction of surface roughness parameters' values and tool life (T) with a precision of 95% confidence interval and a coefficient of determination above 94%. The wiper geometry of the tool led to the achievement of low average surface roughness (Ra) ranging from 0.2 to 0.4 μm with relatively high advances (0.2-0.4 mm rev-1) and maximum height of the profile surface roughness (Rt) in the range of 1.4 to 2.8 μm, without making use of the cutting fluid.

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Section: Methodsmentioning

confidence: 99%

Optimizing production in machining of hardened steels using response surface methodology

et al. 2019

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“…Chromium layer thickness predictions on hard chrome plating processes´ results suggest that DOE may be successfully used for the optimization of ANNs´ backpropagation parameters. In another paper on turning, DOE was employed to determine factor levels that benefit network forecasting skills, concluding that the DOE methodology constitutes a better approach to the design of RBF networks for roughness prediction than most trial and error common approaches [ 35 ]. Authors in [ 36 ] comment that an efficient methodology is needed to obtain optimal values for various parameters in artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

et al. 2020

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This paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results.

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“…though it is costly and difficult to machine [7][8][9][10][11][12]. Researchers have used regression analysis and response surface methodology (RSM) [13][14][15][16][17][18][19][20][21][22] for prediction of R a . Deshpande et al [2] have used regression-based modelling for estimating R a in turning of Inconel 718.…”

Section: Introductionmentioning

confidence: 99%

“…Pontes et al [15] predicted R a using radial basis function neural network approach in hard turning of AISI 52100 steel. They concluded that the ANN model was accurately predicting the R a .…”

Section: Introductionmentioning

confidence: 99%

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Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718

Deshpande¹,

Andhare

Padole

2018

SN Appl. Sci.

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In this paper, artificial neural network approach is used to predict surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718. Experiments were performed by using cryogenically treated and untreated inserts, and various responses were measured. Then, these measured responses were used as input to the artificial neural network to predict surface roughness. It is found that the models developed by artificial neural network are predicting surface roughness with more than 98% accuracy. Further, the predictions obtained by artificial neural network are compared with the results of regression-based prediction models earlier proposed by the authors. The modified regression models were estimating surface roughness with more than 90% accuracy. Based on correlation coefficient values, the prediction results of modified regression model are compared with those obtained by artificial neural network. Finally, it is concluded that artificial neural network models are better for estimating surface roughness than the regression models and such predictions are useful for real-time control of the process to acquire the desired surface roughness. Keywords Artificial neural network • Surface roughness • Inconel 718 Abbreviations CNC Computer numerical control RSM Response surface methodology FOE M Modified first-order equation ANN Artificial neural network BP Backpropagation BR Bayesian regularization LM Levenberg-Marquardt AE Absolute error (%) MAE Mean absolute error (%) MSE Mean square error (%) List of symbols v Cutting speed (m/min) f Feed rate (mm/rev) d Depth of cut (mm) F c Cutting force (N) S Sound pressure level (Pa) V v Vibration velocity of workpiece (m/s) n Number of experiments R ai Average of measured surface roughness in μm R ai Estimated surface roughness h Number of neurons in single hidden layer R 2 Correlation coefficient

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A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning

Cited by 7 publications

References 30 publications

Optimizing production in machining of hardened steels using response surface methodology

Optimizing production in machining of hardened steels using response surface methodology

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718

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