Browsing by Author "Vijay, G.S."

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Design and Optimization of Multi-ring Permanent Magnet Bearings for High-speed Rotors - A Computational Framework
(Engineered Science Publisher, 2021) Kamath, C.R.; Bhat, R.; Bekinal, S.I.; Vijay, G.S.; Shetty, T.S.; Doddamani, M.
This article presents a computational framework (MATLAB app) suitable for the industrial use for selecting optimum multi-ring radial and thrust permanent magnet bearings (PMB) based on two general variables (outer diameter/air gap and length of a bearing). Such an approach eliminates the usage of complex design equations and optimization methods. The detailed methodology adopted in optimizing PMB for maximum characteristics is presented with mathematical equations of force and stiffness. Then, the steps involved in the development of the computational framework are discussed in depth. Further, usage of the computational framework is explained with examples of PMB, and results obtained are validated with the mathematical model results. Regarding the mathematical model results, deviations of 2.22 % and 1.78 % are observed among the maximized radial and axial force values in the app results. Finally, the effectiveness of the proposed framework is demonstrated by discussing the case studies from the literature. © Engineered Science Publisher LLC 2021.
Regression analysis and ANN models to predict rock properties from sound levels produced during drilling
(2013) Rajesh, Kumar, B.; Vardhan, H.; Govindaraj, M.; Vijay, G.S.
This study aims to predict rock properties using soft computing techniques such as multiple regression, artificial neural network (MLP and RBF) models, taking drill bit speed, penetration rate, drill bit diameter and equivalent sound level produced during drilling as the input parameters. A database of 448 cases were tested for determination of uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (?), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) and the prediction capabilities of the models were then analyzed. Results from the analysis demonstrate that neural network approach is efficient when compared to statistical analysis in predicting rock properties from the sound level produced during drilling. 2012 Elsevier Ltd.
Regression analysis and ANN models to predict rock properties from sound levels produced during drilling
(Elsevier Ltd, 2013) Rajesh Kumar, B.; Vardhan, H.; Govindaraj, M.; Vijay, G.S.
This study aims to predict rock properties using soft computing techniques such as multiple regression, artificial neural network (MLP and RBF) models, taking drill bit speed, penetration rate, drill bit diameter and equivalent sound level produced during drilling as the input parameters. A database of 448 cases were tested for determination of uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (?), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) and the prediction capabilities of the models were then analyzed. Results from the analysis demonstrate that neural network approach is efficient when compared to statistical analysis in predicting rock properties from the sound level produced during drilling. © 2012 Elsevier Ltd.
Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite
(2016) Shetty, N.; Herbert, M.A.; Shetty, R.; Shetty, D.S.; Vijay, G.S.
Due to the intricacy of machining processes and inconsistency in material properties, analytical models are often unable to describe the mechanics of machining of carbon fiber reinforced polymer (CFRP) composites. Recently, soft computing techniques are used as alternate modeling and analyzing methods, which are usually robust and capable of yielding comprehensive, precise, and unswerving solutions. In this paper, drilling experiments as per the Taguchi L27 experimental layout are carried out on bi-directional carbon fiber reinforced polymer (BD CFRP) composite laminates using three types of drilling tools: high speed steel (HSS), uncoated solid carbide (USC) and titanium nitride coated SC (TiN-SC). The focus of this work is to determine the best drilling tool that produces good quality drilled holes in BD CFRP composite laminates. This paper proposes a novel prediction model 'genetic algorithm optimised multi-layer perceptron neural network' (GA-MLPNN) in which genetic algorithm (GA) is integrated with Multi-Layer Perceptron Neural Network. The performance capability of response surface methodology (RSM) and GA-MLPNN in prediction of thrust force is investigated. RSM is also used to evaluate the influence of process parameters (spindle speed, feed rate, point angle and drill diameter) on thrust force. GA is used to optimize the thrust force and its optimization performance is compared with that of RSM. It is observed that the GA-MLPNN is better predicting tool than the RSM model. The investigation in this paper demonstrates that TiN-SC is the best tool for drilling BD CFRP composite laminates as minimum thrust force is developed during its use. 2016 Elsevier B.V. All rights reserved.
Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite
(Elsevier Ltd, 2016) Shetty, N.; Herbert, M.A.; Shetty, R.; Shetty, D.S.; Vijay, G.S.
Due to the intricacy of machining processes and inconsistency in material properties, analytical models are often unable to describe the mechanics of machining of carbon fiber reinforced polymer (CFRP) composites. Recently, soft computing techniques are used as alternate modeling and analyzing methods, which are usually robust and capable of yielding comprehensive, precise, and unswerving solutions. In this paper, drilling experiments as per the Taguchi L27 experimental layout are carried out on bi-directional carbon fiber reinforced polymer (BD CFRP) composite laminates using three types of drilling tools: high speed steel (HSS), uncoated solid carbide (USC) and titanium nitride coated SC (TiN-SC). The focus of this work is to determine the best drilling tool that produces good quality drilled holes in BD CFRP composite laminates. This paper proposes a novel prediction model 'genetic algorithm optimised multi-layer perceptron neural network' (GA-MLPNN) in which genetic algorithm (GA) is integrated with Multi-Layer Perceptron Neural Network. The performance capability of response surface methodology (RSM) and GA-MLPNN in prediction of thrust force is investigated. RSM is also used to evaluate the influence of process parameters (spindle speed, feed rate, point angle and drill diameter) on thrust force. GA is used to optimize the thrust force and its optimization performance is compared with that of RSM. It is observed that the GA-MLPNN is better predicting tool than the RSM model. The investigation in this paper demonstrates that TiN-SC is the best tool for drilling BD CFRP composite laminates as minimum thrust force is developed during its use. © 2016 Elsevier B.V. All rights reserved.