Journal Articles
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Item Methodological and parametric studies of machinability of carbon and alloy steels(2013) Lalbondre, R.; Krishna, P.; Mohan Kumar, G.C.The carbon and alloy steels form an increasingly diverse variety and range of steels in manufacturing industries. The assessment of the machinability of an engineering material becomes a matter of prime activity to improve productivity. The machinability of carbon and alloy steels is affected by many factors, such as the composition, microstructure, and strength level of the steel; the feeds, speeds, and depth of cut; and the choice of cutting fluid, cutting tool material and its geometry. Thus the machinability is an intrinsic technological property which is complex to understand and difficult to determine. This paper discusses different methodology of determining the machinability and its rating/index. One of the methodologies, the face turning method in particular, shall be used to determine the machinability of carbon and alloy steel. Further it deals with identifying the appropriate cutting parameters to test the machinability in an effective, simple and easy way. The research work findings here provide useful economic machining solution of knowing, in advance, the machinability of steels to gain and maintain a competitive advantage. © 2013 CAFET-INNOVA TECHNICAL SOCIETY.Item Modelling of squeeze casting process using design of experiments and response surface methodology(Maney Publishing maney@maney.co.uk, 2015) Gowdru Chandrashekarappa, M.; Krishna, P.; Parappagoudar, M.B.The present work makes an attempt to model and analyse squeeze casting process by utilising design of experiments and response surface methodology. The input–output data for developing regression models and test cases is obtained by conducting the experiments. Surface roughness, ultimate tensile strength and yield strength have been measured for different combinations of process variables, namely, squeeze pressure, pressure duration, pouring temperature and die temperature. Two non-linear regression models based on central composite design (CCD) and Box-Behnken design (BBD) of experiments have been developed to establish the input–output relationships. The effects of process variables on the measured responses have been studied using surface plots. The performances of the two non-linear models have been tested for their prediction accuracy with the help of 15 test cases. It is observed that, both CCD and BBD, the non-linear regression models are statistically adequate and capable of making accurate predictions. © 2015 W. S. Maney & Son Ltd.Item Squeeze casting process modeling by a conventional statistical regression analysis approach(Elsevier Inc. usjcs@elsevier.com, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.During the casting process, the alloy composition, melt treatment modification, processing method, and process variables change the microstructure, thereby affecting the mechanical properties. The hybrid squeeze casting method has been used to limit casting defects, refine the micro-structure, and enhance the mechanical properties. The process variables influence the mechanical and micro-structure properties during squeeze casting. In the present study, we established nonlinear input–output relationships and explored the physical behavior of this process based on the statistical design of experiments and using the response surface methodology. Experiments were conducted to measure the responses in terms of the density, hardness, and secondary dendrite arm spacing. Two nonlinear regression models, i.e., Box–Behnken design and central composite design, were used to conduct experiments, collect experimental data, identify significant process variables, analyze the collected data, and establish the complex input–output relationships. Surface plots were used to explore the effects of the squeeze pressure, pressure duration, pouring, and die temperature on the measured responses. Analysis of variance tests were conducted to evaluate the statistical suitability of the models developed. Furthermore, the accuracies of the predictions made by the models were investigated based on test cases. We found that both of the nonlinear models were statistically adequate and they provided complete insights into the complex nonlinear input–output relationships. Central composite design performed better for the secondary dendrite arm spacing and hardness responses, whereas its performance was the same as that of Box–Behnken design for the density response. The relationships between the responses (i.e., outputs) were established by generating large volumes of input–output data using the nonlinear regression models. We found that the density, hardness, and secondary dendrite arm spacing responses could be obtained by utilizing the nonlinear regression equations and the same set of process variables. Furthermore, the secondary dendrite arm spacing response could be expressed as third order nonlinear functions of density or hardness (structure to property relationship). The results showed that the secondary dendrite arm spacing had inverse relationships with density and hardness, whereas density and hardness had direct relationships. © 2016 Elsevier LtdItem Modelling and multi-objective optimisation of squeeze casting process using regression analysis and genetic algorithm(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.In the present work, an attempt has been made using statistical tools to develop a non-linear regression model and to identify the significant contribution of squeeze cast process parameters on surface roughness, hardness and tensile strength. Microstructure examination performed on the squeeze cast samples has revealed that a maximum of 100 MPa pressure is good enough to eliminate all possible casting defects. Accuracy of the developed models has been tested with the help of ten test cases. It is important to note that the developed models predict responses with a reasonably good accuracy and the developed mathematical input–output relationship helps the foundry-man to make better predictions. The present work comprises four objectives, which are conflicting in nature. Hence, mathematical formulation is used to convert four objective functions into a single objective function. The popular evolutionary algorithm, that is genetic algorithm has been utilised to determine the optimal process parameters. © 2015 Engineers Australia.Item Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization(De Gruyter Open Ltd peter.golla@degruyter.com, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Vundavilli, P.R.; Parappagoudar, M.B.The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO) and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time. © 2016 G.C.M. Patel et al., published by De Gruyter Open.Item An intelligent system for squeeze casting process—soft computing based approach(Springer London, 2016) Gowdru Chandrashekarappa, G.C.; Krishna, P.; Parappagoudar, M.B.The present work deals with the forward and reverse modelling of squeeze casting process by utilizing the neural network-based approaches. The important quality characteristics in squeeze casting, namely surface roughness and tensile strength, are significantly influenced by its process variables like pressure duration, squeeze pressure, and pouring and die temperatures. The process variables are considered as input and output to neural network in forward and reverse mapping, respectively. Forward and reverse mappings are carried out utilizing back propagation neural network and genetic algorithm neural network. For both supervised learning networks, batch training is employed using huge training data (input-output data). The input-output data required for training is generated artificially at random by varying process variables between their respective levels. Further, the developed model prediction performances are compared for 15 random test cases. Results have shown that both models are capable to make better predictions, and the models can be used by any novice user without knowing much about the mechanics of materials and the process. However, the genetic algorithm tuned neural network (GA-NN) model prediction performance is found marginally better in forward mapping, whereas BPNN produced better results in reverse mapping. © 2016, Springer-Verlag London.Item Synthesis of high hardness IR optical coating using diamond-like carbon by PECVD at room temperature(Elsevier Ltd, 2017) Krishna, K.; Varade, A.; Niranjan Reddy, K.; Dhan, S.; Chellamalai, M.; Balashanmugam, N.; Krishna, P.Diamond-like Carbon (DLC) for IR antireflective properties is currently being used in the coating of germanium based IR optics. These DLC coatings offer better wear resistance as compared to traditional anti-reflective (AR) coatings. The current work emphasizes the development of IR optics using germanium substrate coated with DLC which typically covers IR transmission in wavelength regions like 3–7 ?m and 9–15 ?m. In order to study IR transmission, an optimum film thickness of DLC was calculated and coated on a double sided polished germanium substrate. DLC was coated on a single side of a germanium substrate, as well as on both sides of germanium. DLC has been deposited using Radio Frequency Plasma Enhanced Chemical Vapour Deposition (RF-PECVD) at room temperature without the use of any intermediary buffer layers required for adhesion and high hardness values were achieved at room temperature as compared to existing literature. The transmission of IR through DLC coated germanium windows was measured using Fourier Transform Infra-Red (FTIR) spectroscopy. A comparison between transmission through a single side and double sided DLC coating on germanium has been demonstrated. The hardness of the film was measured using nanoindentation. Scratch test was also performed using nanoindentation. Adhesion and salt spray tests were performed as per MIL standards. With double sided DLC coating, a peak transmission value of 93% is achieved in 3–7 ?m and the average hardness of DLC is measured to be 32.74 GPa. © 2017 Elsevier B.V.Item Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process(Elsevier Ltd, 2017) Gowdru Chandrashekarappa, M.; Shettigar, A.K.; Krishna, P.; Parappagoudar, M.B.Today, in competitive manufacturing environment reducing casting defects with improved mechanical properties is of industrial relevance. This led the present work to deal with developing the input-output relationship in squeeze casting process utilizing the neural network based forward and reverse mapping. Forward mapping is aimed to predict the casting quality (such as density, hardness and secondary dendrite arm spacing) for the known combination of casting variables (that is, squeeze pressure, pressure duration, die and pouring temperature). Conversely, attempt is also made to determine the appropriate set of casting variables for the required casting quality (that is, reverse mapping). Forward and reverse mapping tasks are carried out utilizing back propagation, recurrent and genetic algorithm tuned neural networks. Parameter study has been conducted to adjust and optimize the neural network parameters utilizing the batch mode of training. Since, batch mode of training requires huge data, the training data is generated artificially using response equations. Furthermore, neural network prediction performances are compared among themselves (reverse mapping) and with those of statistical regression models (forward mapping) with the help of test cases. The results shown all developed neural network models in both forward and reverse mappings are capable of making effective predictions. The results obtained will help the foundry personnel to automate and précised control of squeeze casting process. © 2017Item Synthesis of high hardness, low COF diamond-like carbon using RF-PECVD at room temperature and evaluating its structure using electron microscopy(Elsevier Ltd, 2017) Krishna, K.; Varade, A.; Reddy, N.; Dhan, S.; Chellamalai, M.; Krishna, P.; Balashanmugam, N.Diamond-like carbon (DLC) coatings have been deposited on Silicon wafers using a Radio Frequency based Plasma Enhanced Chemical Vapor Deposition (RF-PECVD) at room temperature. Experiments were carried out using a flow rate of 100 sccm and 300 sccm of acetylene (C2H2) gas and the bias voltage was varied from 300 to 450 V for DLC deposition. Scanning electron microscope (SEM) and transmission electron microscope (TEM) has been used to study the structure and morphology of the DLC coating. TEM results of DLC coatings deposited at 100 sccm C2H2 flow suggest that some crystalline features of diamond are present in the disordered matrix of DLC. Mechanical properties of DLC coatings were studied using a nanoindenter. The results indicate that the hardest DLC film is obtained at 100 sccm flow rate of C2H2 deposited at 450 V bias voltage of about 32.25 GPa. The results also indicate that the lowest coefficient of friction (COF) of about 0.04 in DLC film is obtained at 300 sccm flow rate of C2H2 deposited at 400 V bias voltage. COF is found to be lower in high C2H2 flow rate, wherever relatively softer DLC was deposited. © 2017 Elsevier B.V.Item Laser surface melting of ?-TiAl alloy: An experimental and numerical modeling study(Institute of Physics Publishing helen.craven@iop.org, 2019) Mallikarjuna, M.; Bontha, S.; Krishna, P.; Balla, V.K.The objective of present work is to study the evolution of thermal stresses during laser surface melting (LSM) of ?-TiAl alloy using experimental and numerical modeling approaches. LSM of ?-TiAl alloy samples were carried out at different processing conditions in a controlled atmosphere. Material characterization of the melted region was investigated using scanning electron microscope. It was found that fully lamellar microstructure was transformed into predominantly ?-TiAl with little amount of ?2-Ti3Al. A maximum improvement in hardness of over 72% was noticed in the melted region compared to that of the substrate. Three-dimensional thermomechanical finite element analysis of LSM of ?-TiAl alloy was carried out. Melt pool dimensions, temperature history, and residual stresses were predicted from the finite element models. Measured and predicted values of melt pool depth were in good agreement with a maximum error of 13.6% at P=400Wand V=10mms-1. Predicted residual stress in the melted region exceeded the yield strength of ?-TiAl alloy and resulted in cracking of the melted region at all process conditions. ©2019 IOP Publishing Ltd.