Modeling of surface roughness in turning process by using Artificial Neural Networks

Dahbi, Samya; Ezzine, Latifa; Moussami, Haj El

doi:10.1109/gol.2016.7731681

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…This article constitutes a considerable extension of our previous study, 8 where we modeled just one cutting performance (surface roughness) in turning of AISI 1042 steel under the same turning parameters considered in the current study. This modeling was performed by exploiting the same artificial intelligence technique and the developed network estimated surface roughness with high accuracy (R 2 > 95%, MSE < 0.1%, and APE < 10%).…”

Section: Resultsmentioning

confidence: 99%

“…The current article constitutes a considerable extension of our previous study, 8 where we modeled the surface roughness in turning of AISI 1042 steel using the artificial neural networks (ANNs) approach. The developed network estimated this cutting performance with high accuracy (correlation coefficient (R 2 ) > 95%, mean squared error (MSE) < 0.1%, and average absolute error e < 10%).…”

Section: Introductionmentioning

confidence: 99%

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Modeling of cutting performances in turning process using artificial neural networks

Dahbi

Ezzine

Moussami

2017

International Journal of Engineering Business Management

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In this article, we present the modeling of cutting performances in turning of 2017A aluminum alloy under four turning parameters: cutting speed, feed rate, depth of cut, and nose radius. The modeled performances include surface roughness, cutting forces, cutting temperature, material removal rate, cutting power, and specific cutting pressure. The experimental data were collected by conducting turning experiments on a computer numerically controlled lathe and by measuring the cutting performances with forces measuring chain, an infrared camera, and a roughness tester. The collected data were used to develop an artificial neural network that models the pre-cited cutting performances by following a specific methodology. The adequate network architecture was selected using three performance criteria: correlation coefficient (R 2), mean squared error (MSE), and average percentage error (APE). It was clearly seen that the selected network estimates the cutting performances in turning process with high accuracy: R 2 > 99%, MSE < 0.3%, and APE < 6%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of cutting performances in turning process using artificial neural networks

Dahbi

Ezzine

Moussami

2017

International Journal of Engineering Business Management

Self Cite

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“…As presented in [42], without pre-processing, the results of ANFIS models are not considerable, so using a pre-processing function is unavoidable. This study used the Min and Max normalisation method [43] and the principal component analysis (PCA) method [44,45] as pre-processing processes for the experimental dataset. The dataset was then divided randomly into a test dataset (30%) and a training dataset (70%).…”

Section: Resultsmentioning

confidence: 99%

Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation

et al. 2021

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Five major operations for the conversion of lignocellulosic biomasses into bioethanol are pre-treatment, detoxification, hydrolysis, fermentation, and distillation. The fermentation process is a significant biological step to transform lignocellulose into biofuel. The interactions of biochemical networks and their uncertainty and nonlinearity that occur during fermentation processes are major problems for experts developing accurate bioprocess models. In this study, mechanical processing and pre-treatment on the palm trunk were done before fermentation. Analysis was performed on the fresh palm sap and the fermented sap to determine the composition. The analysis for total sugar content was done using high-performance liquid chromatography (HPLC) and the percentage of alcohols by volume was determined using gas chromatography (GC). A model was also developed for the fermentation process based on the Adaptive-Network-Fuzzy Inference System (ANFIS) combined with particle swarm optimization (PSO) to predict bioethanol production in biomass fermentation of oil palm trunk sap. The model was used to find the best experimental conditions to achieve the maximum bioethanol concentration. Graphical sensitivity analysis techniques were also used to identify the most effective parameters in the bioethanol process.

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“…Hazza et al [13] used SVM, EML and ANN and other prediction methods for signal analysis, to find the relationship between the vibration surface roughness, the predicted value and the actual measurement error. Dahbi et al [14] analyzed the processing conditions, such as cutting speed, feed rate, depth of cut and tool nose radius, so as to learn from the surface roughness of the neural network, from the forecast results can be learned very well, the error range within 10 %. Srinivasa Pai et al [15] considered the critical effect of tool radius in processing, and then used multi-layer sensing neural network to predict surface roughness.…”

Section: Introductionmentioning

confidence: 99%

A histogram statistical method for the detection of localized faults in deep groove ball bearing

Lin

Huang

2017

MATEC Web Conf.

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Abstract. This study aims to use the histogram statistical method to establish a deep groove ball bearing fault diagnosis strategy. First, statistical indicators are used to excavate the fault characteristics buried in the vibration signal, and use the histogram to define the characteristic area for fault diagnosis. The results show that the indicators 1, 3, 6 have better statistical differences. Based on this, the accuracy of pattern recognition for all test data is 100 %. Finally, the statistical significance of ball damage was significant, and the results showed high correlation (56~73 %). The correlation between inner race damage model was 49~57 % and healthy model was 52 %. As the inner race damage and health model in the statistical sense, there are some similar, so there is a relatively high correlation. In the future research work, it will be committed to mining more representative indicators to enhance the relevance of abnormal characteristics.

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Modeling of surface roughness in turning process by using Artificial Neural Networks

Cited by 6 publications

References 15 publications

Modeling of cutting performances in turning process using artificial neural networks

Modeling of cutting performances in turning process using artificial neural networks

Experimental and Artificial Intelligence Modelling Study of Oil Palm Trunk Sap Fermentation

A histogram statistical method for the detection of localized faults in deep groove ball bearing

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