Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Rao, S.S.Subject: search.filters.subject.Face milling

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    Determination of transient and steady state cutting in face milling operation using recurrence quantification analysis
    (2009) Mhalsekar, S.D.; Mohan, G.; Rao, S.S.; Gangadharan, K.V.
    Typical face milling operation involves transient and steady state cutting phases. Identification and distinction of the cutting state will primarily help in understanding the fundamentals of forced vibration, deflection and dynamic stability in milling system at the beginning and end of a cutting pass. Such type of investigation has advantages in process planning, tool geometry optimization and on-line fault diagnosis. An effort to provide estimation of transient and steady state cutting has been made using Recurrence Quantification Analysis (RQA) of vibration signals. RQA is a novel nonlinear analytical tool. It starts with construction of recurrence plot using embedded dimension and time delay. The recurrence plot is than quantified resulting in RQA. Face milling of H11 chromium steel has been carried out at two different cutting conditions and analyzed. The resulting RQA parameters could identify and distinguish transient and steady state cutting. © 2006-2009 Asian Research Publishing Network (ARPN).
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    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.; Shriram
    An 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).
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    Application of back propagation algorithms in neural network based identification responses of AISI 316 face milling cryogenic machining technique
    (Taylor and Francis Ltd., 2022) Karthik, K.R.; Malghan, R.L.; Shettigar, A.; Rao, S.S.; Herbert, M.A.
    The paper explores the potential study of artificial neural network (ANN) for prediction of response surface roughness (Ra) in face milling operation with respect to cryogenic approach. The model of Ra was expressed as the main factor in face milling of spindle speed, feed rate, depth of cut and coolant type. The ANN is trained using four various back propagation algorithms (BPA). The emphasis of the paper is to investigate the performance and the accuracy of the attained results depicts the effectiveness of the trained ANN in identifying the predicted Ra. The incorporated various BPA in predicting the Ra. The performance comparative study is made among statistical (Response Surface Methodology (RSM)) and ANN (BPA–training algorithm) methods. The various incorporated BPA algorithms are Gradient Descent (GD), Scaled Conjugate Gradient Descent (SCGD), Levenberg Marquardt (LM) and Bayesian Neural Network (BNN). Afterwards the best suitable BPA is identified in predicting Ra for AISI 316 in face milling operation using liquid nitrogen (LN2) as cutting fluid. The outperformed BPA is identified based on the attained deviation percentage and time required for the training the network. © 2020 Engineers Australia.