An ANN-Based Method to Predict Surface Roughness in Turning Operations

Arapoğlu, R. Aykut; Sofuoğlu, Mehmet Alper; Orak, Sezan

doi:10.1007/s13369-016-2385-y

Cited by 20 publications

(9 citation statements)

References 28 publications

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“…The achieved results reveal the development of output quality combined with lower production cost, which is evident of the efficiency of the established ANFIS model. Arapoglu et al [28] suggested new variable selection method based on artificial neural networks (ANN) for the prediction of the surface roughness. A statistical hypothesis test is used as an elimination criterion.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

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Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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“…Adaptive neuro-fuzzy inference system (ANFIS) is a class of an artificial neural network combined with fuzzy logic interface which is used for nonlinear data sampling to create a robust structure [12][13][14]. Sparham et al [15] investigated cutting force during machining process using ANFIS model.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

et al. 2021

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This study investigated the predicted cutting force model of a turning operation for Al–Si–Cu cast alloy modified with modifiers based on adaptive neuro-fuzzy inference system (ANFIS) approach. Feed rate, cutting speed and Silicon spacing were considered as the input parameters. A series of turning experiments were conducted at various feed rates and cutting speeds. The prediction result showed that the ANFIS model successfully predicted the cutting force value in terms of cutting speed, feed rate and Si spacing. A mathematical model was proposed to describe the cutting force changes during the machining of Al–Si–Cu cast alloy. Moreover, the addition of Bismuth into the base alloy decreased the cutting force compared to other refinement elements.

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“…Various studies have made important contributions to the prediction and control of surface roughness. For instance, Arapoğlu et al 26 and Imani et al 27 established surface roughness prediction models based on artificial neural networks (ANN) in turning and milling, respectively. Debnath et al 28 studied the influence of various cutting fluid levels and cutting parameters on surface roughness in turning.…”

Section: Introductionmentioning

confidence: 99%

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

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An ANN-Based Method to Predict Surface Roughness in Turning Operations

Cited by 20 publications

References 28 publications

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

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