Browsing by Author "Rai, R."

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    Development of a surface roughness prediction system for machining of hot chromium steel (AISI H11) based on artificial neural network
    (2010) Rai, R.; Kumar, 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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    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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    Experimental investigation on utilization of waste shredded rubber tire as a replacement to fine aggregate in concrete
    (Springer, 2019) Hiremath, P.N.; Jayakesh, K.; Rai, R.; Naganna, N.S.; Yaragal, S.C.
    Depletion of natural resources in the past few decades due to rapid construction activities all around the world has forced a threat to the availability of natural resources for future generation. The utilization of waste industrial by products, in the form of supplementary cementitious materials and waste tire rubber products replacing natural aggregates in production of concrete. In the present study performance of concrete mixes incorporating 2.5, 5, 7.5 and 10% Waste Shredded Rubber Tire (WSRT) as partial replacement of fine aggregate is investigated. Numerous research works have been conducted on replacement of aggregate by waste crumb rubber but data scarce on utilization of waste rubber in concrete directly. Hence to examine characteristics of shredded rubber tire based concretes, two sets of concrete specimen were produced. In the first set, shredded rubber tire is added directly without any pretreatment and in the second set the shredded rubber tire was immersed in NaOH solution for 24 h and then washed with water thoroughly and rubbed with sand paper to obtain the rough surface finish to facilitate improved bonding properties with cement matrix. To evaluate the performance of WSRT based concretes, fresh and hardened properties were determined by conducting slump tests on fresh mixes, and compression, flexural and impact tests on hardened concrete cubes and prisms. Proving results were obtained for potential use of WSRT in concretes for generalized applications. © Springer Nature Singapore Pte Ltd. 2019.
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    Incorporation of Sugarcane Bagasse Ash to investigate the mechanical behavior of Stone Mastic Asphalt
    (Elsevier Ltd, 2022) Akarsh, P.K.; Ganesh, G.O.; Marathe, S.; Rai, R.
    Stone Mastic Asphalt (SMA) is one kind of new generation gap graded hot mix asphalt with higher content of asphalt with coarse aggregate proportions. The stone-on-stone interlock in SMA makes it superior rut resistant mix and favorable in adverse conditions. The usage of conventional fillers in SMA will lead to the creation of many environmental nuisances and entail additional cost during the production. The use of industrial by-products in the place of the conventional filler can be proven favorable to overcome enhanced production cost of SMA. In the present research work, one such largely produced industrial waste called, Sugarcane Bagasse Ash (SBA) is used as a filler in SMA by replacing conventional Ordinary Portland Cement (OPC) filler, and the engineering performances and cost effectiveness is examined. The SMA mixes were cast with 6.25% optimum binder content are varied with SBA of 2.5% (ACS1), 5.0% (ACS2), 7.5% (ACS3) and 10% (ACS4) by weight of the mix as filler and the results were compared with conventional SMA mix (ACS0). The results showed that, the inclusion of SBA demonstrated superior performances indicating the enhanced stiffness of mix (in terms of Marshall and flow characteristics). Moisture resistance of the SMA mix was improved up to 7.5% SBA replacement. Further, the drain-down test results revealed that, SBA can be effectively used as stabilizing agent. The mix ACS 1 and ACS2 shown a minimum rut depth in reference with ACS0. The mix with 5% SBA resists more number of repetitive loads than all the mix tested. © 2022 Elsevier Ltd