Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Development of a surface roughness prediction system for machining of hot chromium steel (AISI H11) based on artificial neural network(Medwell Journals medwellonline@gmail.com, 2010) Rai, R.; Shettigar, A.; Rao, S.S.; ShriramAn attempt have been made to apply the principles of artificial neural networks (ANN) towards developing a prediction model for surface roughness during the machining of high chromium steel through face milling process. Now a days, hot chromium steel is prominently used in die and mould industry as well as in press tools, helicopter rotor blades, etc. Initially, Taguchi design of experiments was applied while conducting the experiments to reduce the time and cost of experiment. Multilayer perceptron (MLP) network using Feed Forward Error Back propagation was chosen as the neural network architecture to describe the process model. The experiments were conducted on a C.N.C milling machine using carbide cutters. Pearson correlation coefficient was also calculated to analyze the correlation between the system inputs and selected system output i.e. surface roughness. The results of ANN modeling were substantiated by testing and validation of the resulting surface roughness values and the results have been encouraging. The outputs of Pearson correlation coefficient also showed a strong correlation between the feed per tooth and surface roughness, followed by cutting speed. © 2006-2010 Asian Research Publishing Network (ARPN).Item Discrete wavelet-Ann approach in time series flow forecasting-a case study of Brahmaputra river(2012) Deka, P.C.; Haque, L.; Banhatti, A.G.This paper deals with the prediction of hydrologic behavior of the runoff for the one of the largest discharge carrier International River, Brahmaputra, located in Assam (India) at the Pandu station, by using daily time unit. The flow regime dominated by high data non-stationary and seasonal irregularity due to Himalayan climate fallout. The influence of data preprocessing through wavelet transforms has been investigated. For this, the main time series of flow data were decomposed to multi resolution time series using discrete wavelet transformations. Then these decomposed data were used as input to Artificial Neural Network (ANN) for multiple lead time flow forecasting. Various types of wavelets were used to evaluate the optimal performance of models developed. The forecasting accuracy of the models has been tested for multiple lead time upto 4 days using different decomposition levels. The performance of the proposed hybrid model has been evaluated based on the performance indices such as root mean square error (RMSE), coefficient of efficiency (CE) and mean relative error (MRE).The results shows the better forecasting accuracy by the proposed combined hybrid model over the single ANN model in hydrological time series forecasting. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India(Springer London, 2017) Patil, A.P.; Deka, P.C.This paper evaluates the ability of wavelet transform in improving the accuracy of artificial neural network (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) models. In this study, the performance of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating daily evapotranspiration in arid regions was evaluated. Prior to the development of models, gamma test was used to identify the best input combinations that could be used under limited data scenario. Performance of the proposed hybrid models was compared to ANN, ANFIS, and conventionally used Hargreaves equation. The results revealed that use of wavelet transform as data preprocessing technique enhanced the efficiency of ANN and ANFIS models. Wavelet-ANN and Wavelet-ANFIS performed reasonably better than other models. Better handling of wavelet-decomposed input variables enabled Wavelet-ANN models to perform slightly better than the Wavelet-ANFIS models. W-ANN2 (RMSE = 0.632 mm/day and R = 0.96) was found to be the best model for estimating daily evapotranspiration in arid regions. The proposed W-ANN2 model used second-level db3 wavelet-decomposed subseries of temperature and previous day evapotranspiration values as inputs. The study concludes that hybrid Wavelet-ANN and Wavelet-ANFIS models can be effectively used for modeling evapotranspiration. © 2015, The Natural Computing Applications Forum.Item Inverse estimation of heat flux under forced convection conjugate heat transfer in a vertical channel fully filled with metal foam(Elsevier Ltd, 2022) Trilok, G.; Vishweshwara, P.S.; Gnanasekaran, G.In this work, for the first time, a heat flux at the boundary is estimated for a conjugate heat transfer under forced convection in the presence of high porosity metal foams. For the forward problem a vertical channel experimental set up reported in the literature is considered. The metal foam placed in the vertical channel is subjected to constant heat flux through aluminum plate and airflow of various velocities is passed through vertical channel for removal of heat from the high porosity metal foam placed in the vertical channel. Six different velocities are considered and the required temperature distribution of the aluminum plate is obtained by solving Darcy extended Forchheimer and Local Thermal non-equilibrium models for metal foams. The forward problem, created using computational fluid dynamics in ANSYS-FLUENT, is substituted with Neural Network for faster computation of the forward problem. The maximum errors between the computational fluid dynamics and Artificial Neural Network models for the heat flux values of 466.66, 666.66 and 1133.3 W/m2 are found to be 0.086, 0.043, 0.092 respectively. The heat flux to the forward problem is treated as unknown and the same is estimated using an inverse method that couples Particle Swarm Optimization with Bayesian framework. The result of inverse estimation of exact temperature data shows that for a heat flux of 1266.64 W/m2 the error is found to be 1.6e−4%. Similarly, for the noise added temperature data, the absolute % error in heat flux of 599.985, 733.315 and 1266.635 W/m2 is 4.80e−2%, 2.20e−2%, 2.30e−2% respectively. © 2022 Elsevier LtdItem Optimizing Seismic Earth Pressure Estimates for Battered Retaining Walls Using Numerical Methods and ANN(Springer Science and Business Media Deutschland GmbH, 2024) Thottoth, S.R.; Khatri, V.N.; Kolathayar, S.; Keawsawasvong, S.; Lai, V.Q.This study comprehensively analyzes seismic active earth pressure estimation for hunched retaining walls. The analysis utilizes the horizontal slices method within the modified pseudo-dynamic framework and incorporates depth-dependent dynamic parameters for the backfill soil. The friction angle of the backfill soil varied between 30° and 45°, while the hunch angle of the retaining wall increased from 0° to 20°. The findings of this study demonstrate that the use of hunched retaining walls results in a significant reduction in active earth pressure. In both static and dynamic cases, reductions of up to 23% and 18%, respectively, compared to vertical walls, were observed. Notably, this reduction is more pronounced for smooth walls under static conditions than for rough walls under dynamic conditions. The estimated active earth pressures for both vertical and hunched walls in static and dynamic scenarios closely align with those reported in the literature. Additionally, an empirical equation based on an artificial neural network model, utilizing the numerical analysis result, is proposed to establish a relationship between the investigated design parameters and the active earth pressure coefficient. The proposed equation demonstrated a high coefficient of determination (R2) value of 99.78% when compared to the numerical results. This study’s outcomes provide valuable insights and a tool for practicing engineers in the field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.Item Vibration reduction and intelligent control in SRM using optimised two stage commutation(Inderscience Publishers, 2025) Wilson, V.; Latha, P.G.; Jose, N.; Bhaktha, S.Switched reluctance motors (SRMs) have grown in popularity in a variety of industrial applications due to their inherent benefits such as high fault-tolerance, simplicity, affordability, and rare-earth free nature. However, the generation of undesirable vibrations due to radial force variations remains a significant challenge. Two stage commutation based on active vibration cancellation (AVC) is an effective method to reduce these vibrations. The focus of this paper is to address the major limitation with two stage commutation, namely the extended tail current causing increased copper loss. This is accomplished with optimal commutation parameters employing particle swarm optimisation (PSO) method. A MATLAB/Simulink model of SRM with vibration signal is developed and is used for demonstrating vibration cancellation. An intelligent control is also implemented which can track the dynamic changes in speed-load conditions. This paper showcases that this approach is an effective solution to reduce the vibrations issues in SRM, thereby improving the overall performance of the motor for industrial applications. © © 2025 Inderscience Enterprises Ltd.