Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm

Ho, Wen-Hsien; Tsai, Jinn-Tsong; Lin, Bor-Tsuen; Chou, Jyh–Horng

doi:10.1016/j.eswa.2008.01.051

Cited by 145 publications

(59 citation statements)

References 11 publications

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“…The best results were gained by using GP model with good accuracy. For modelling the surface roughness in end milling, Ho et al [16] made integration of the ANFIS with hybrid Taguchi-genetic algorithm, learning algorithm by using Lo's experimental results [14]. The purpose was to test reliability of the proposed hybrid approach.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

Al-Zubaidi

Ghani

Haron

2013

Modelling and Simulation in Engineering

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Surface roughness is considered as the quality index of the machine parts. Many diverse techniques have been applied in modelling metal cutting processes. Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end milling Ti6Al4V alloy with coated (PVD) and uncoated cutting tools under dry cutting conditions. Real experimental results have been used for training and testing of ANFIS models, and the best model was selected based on minimum root mean square error. A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5. The findings provided evidence of the capability of ANFIS in modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results.

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Section: Introductionmentioning

confidence: 99%

Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

Al-Zubaidi

Ghani

Haron

2013

Modelling and Simulation in Engineering

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“…Although these methods can accurately fit the nonlinear system, for they are model-free methods which are difficult to let students observe the mathematical properties existing in these systems for further applications. For the educational target at EEE, instead of those complicated system modeling kits this study adopted and integrated the Taguchi method (Ho, W.H., et al, 2009) and ANFIS (Huang, C.N. and Chang, C.C., 2011) to achieve system identification works.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Engineering Education via Physical Experiments: The Case of Learning Energy Storage with a Flywheel System

Huang

Cheng

Kuo

2015

Balkan Region Conference on Engineering and Business Education

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Design thinking is complex that is hard to learn and harder to teach. The purpose of engineering education is to graduate engineers who can design (Dym CL, 2005). How to enhance students' reasoning and cognizing about data has gained an increasingly visible role in the K-12 curriculum (Doerr H. M. and Lyn D., 2003). In order to enhance teaching and learning engineering mathematics, a modeling approach by shifting the focus on finding solutions to a particular problem to create a system is proposed in this study. Besides, a training process for the college students who are intending to be the instructors of guiding physical experiments is included. Nonlinear systems, signifying any system is not linear, cannot satisfy the superposition principle. In this paper, a new modeling method for nonlinear systems based on an Adaptive Network-Fuzzy Inference System (ANFIS) for operation and practice was used (Jang, J.S.R., 1993). Through this method, students were able to learn how the transfer functions work for identifying an unknown system. This method had well helped the participated students how the nature of nonlinear system is and how to find out the optimal solutions in controls.

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“…To enhance intelligent method performance hybrid methods are widely used, for instance the Taguchi-genetic algorithm for adaptive network-based fuzzy inference system training (Ho et al, 2009); the hybrid group method of data handling (GMDH) with genetic programming (Najafzadeh and Barani, 2011); evolutionary algorithms to optimize ANN layer weights ; and a combined firefly algorithm (FFA) and wavelet with support vector machines (SVM), i.e. SVM-FFA and SVM-Wavelet (Gocić et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

Ebtehaj

Bonakdari

Zaji

et al. 2016

Journal of Hydrology and Hydromechanics

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A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.

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Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm

Cited by 145 publications

References 11 publications

Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

Enhancing Engineering Education via Physical Experiments: The Case of Learning Energy Storage with a Flywheel System

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

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