论文信息 - Optimization of Milling Parameters of Gun Metal Using Fuzzy Logic and Artificial Neural Network Approach - 字舞流文

Optimization of Milling Parameters of Gun Metal Using Fuzzy Logic and Artificial Neural Network Approach

J Gokulachandran | M Vignesh | Visnu Sasindran | M Vignesh | S Arvind Krishna | A Madusudhanan | S Arvind Krishna

[1] R. Padmanaban,et al. Comparison of Statistical and Soft Computing Models for Predicting Hardness and Wear Rate of Cu-Ni-Sn Alloy , 2018 .

[2] K. Mohandas,et al. Application of Regression and Fuzzy Logic Method for Prediction of Tool Life , 2012 .

[3] M. Nehdi,et al. Carbon dioxide emissions and climate change: policy implications for the cement industry , 2005 .

[4] L. B. Abhang,et al. Optimization of Machining Parameters in Steel Turning Operation by Taguchi Method , 2012 .

[5] Itziar Cabanes,et al. High-accuracy wire electrical discharge machining using artificial neural networks and optimization techniques , 2018 .

[6] Girish Kant,et al. Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm , 2015 .

[7] Pengyu Li,et al. A quantitative approach to analyze carbon emissions of CNC-based machining systems , 2015, J. Intell. Manuf..

[8] Vaira Vignesh Ramalingam,et al. Modelling Corrosion Behavior of Friction Stir Processed Aluminium Alloy 5083 Using Polynomial: Radial Basis Function , 2017, Transactions of the Indian Institute of Metals.

[9] Susmita Roy,et al. Application of grey fuzzy logic for the optimization of CNC milling parameters for Al–4.5%Cu–TiC MMCs with multi-performance characteristics , 2016 .