Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Wind Power Optimization: A Comparison of Meta-Heuristic Algorithms(Institute of Physics Publishing helen.craven@iop.org, 2018) Shetty, R.P.; Sathyabhama, A.; Srinivasa Pai, P.The wind being a most promising renewable energy, has become a strong contender for fossil fuels. Optimizing the blade pitch angle of a wind turbine is important to obtain the maximum power output, as the other variables are considered to be uncontrollable. In this paper an effort has been made to compare performances of three different optimization algorithms namely Particle swarm optimization (PSO), Artificial bee colony (ABC) and cuckoo search (CS) for optimizing the blade pitch angle and hence optimize the power output of a 1.5 MW capacity, pitch regulated, three-bladed horizontal axis wind turbine operating at a large wind farm in central dry zone of Karnataka. The objective function development is done using Artificial Neural Network. The CS algorithm is found to be faster and more efficient as compared to ABC and PSO for the problem under consideration. © Published under licence by IOP Publishing Ltd.Item Surface Roughness Prediction in High Speed Turning of Ti-6Al-4V: A Comparison of Techniques(Institute of Physics Publishing helen.craven@iop.org, 2018) D'Mello, D.; Srinivasa Pai, P.; Puneet, N.P.Surface finish of machined products is important and plays an important role in ascertaining its quality and other attributes. Surface roughness of difficult to machine materials like titanium alloys are difficult to model due to several factors influencing it. This study makes an attempt to compare the performance of a statistical technique, Response Surface Methodology (RSM) and two Artificial Neural Network (ANN) techniques namely Multi Layered Perceptron (MLP) and Radial Basis Function Neural Network (RBFNN) to model and predict the surface roughness parameters Ra and Rt in high speed turning of Ti-6Al-4V. Experiments have been carried out using uncoated carbide inserts under dry condition. The input parameters for the modeling studies include cutting speed, feed rate and depth of cut. This work also makes use of tool wear and cutting tool vibration (Vy) which are uncontrollable parameters as the inputs for modeling studies. The ANOVA analysis has revealed that feed rate and cutting tool vibrations are the most significant parameters affecting Rt and cutting speed and vibrations affect Ra. A comparison between the modeling techniques revealed that RBFNN performed better in terms of prediction accuracy when compared to MLP and RSM. © Published under licence by IOP Publishing Ltd.Item Influence of milling parameters on Al-Li alloy surface characteristics(Elsevier Ltd, 2023) Marakini, V.; Srinivasa Pai, P.; Udaya Bhat, K.; Thakur, D.S.; Achar, B.P.Lightweight alloys attract the aerospace industries due to their high specific strength. Al-Li alloy has been investigated in the present study to identify their functional performance in terms of surface characteristics namely surface roughness and hardness. Dry face milling was performed using uncoated carbide inserts for the experimental conditions obtained from Taguchi L27 design of experiments. The effect of milling parameters, such as feed rate, cutting speed and depth of cut on surface roughness and hardness have been investigated and presented. Further, the optimal milling conditions are identified using statistical techniques – Grey Relational Analysis (GRA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The study showed that feed rate is the most influential parameter on both surface characteristics. Both GRA and TOPSIS showed similarity in identifying the same condition as optimal for milling Al-Li alloy under dry condition. © © 2023 Elsevier Ltd. All rights reserved.