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Mahesh B. Parappagoudar - One of the best experts on this subject based on the ideXlab platform.
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back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process
Applied Soft Computing, 2017Co-Authors: Manjunath Patel G C, Arun Kumar Shettigar, Prasad Krishna, Mahesh B. ParappagoudarAbstract:Abstract 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 precised control of squeeze casting process.
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an intelligent system for squeeze casting process soft computing based approach
The International Journal of Advanced Manufacturing Technology, 2016Co-Authors: G Manjunath C Patel, Prasad Krishna, Mahesh B. ParappagoudarAbstract: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.
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An intelligent system for squeeze casting process—soft computing based approach
The International Journal of Advanced Manufacturing Technology, 2016Co-Authors: G C Manjunath Patel, Prasad Krishna, Mahesh B. ParappagoudarAbstract: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.
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Forward and reverse process models for the squeeze casting process using neural network based approaches
Soft Computing, 2014Co-Authors: Manjunath Patel Gowdru Chandrashekarappa, Prasad Krishna, Mahesh B. ParappagoudarAbstract:The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing Forward and reverse Mappings of artificial neural networks. Forward Mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.
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Forward and reverse Mappings of the cement-bonded sand mould system using fuzzy logic
The International Journal of Advanced Manufacturing Technology, 2012Co-Authors: B. Surekha, Pandu R. Vundavilli, Mahesh B. ParappagoudarAbstract:Ferrous metals can be cast economically in cement-bonded sand moulds with a good dimensional accuracy. In the present work, Forward and reverse Mappings were carried out in cement-bonded sand moulding system by utilizing three different fuzzy logic (FL)-based approaches. The mould properties, namely compression strength and mould hardness were predicted in Forward Mapping for a set of input parameters. The amount of cement, accelerator, water and testing time were considered as the input parameters. The reverse Mapping is a convenient and effective tool to control the process, as it can be used to determine a set of input process parameters that will produce the desired output (also known as responses). Manually constructed Mamdani-based FL system is used in the first approach. The rule base and data base of the FL system constructed in approach 1 has been optimized in the second approach. Finally, GA was used in the third approach, for the automatic evolution of the FL system. The performances of the developed approaches were tested with the help of twenty test cases and found to be satisfactory for the cement-bonded sand mould system.
Prasad Krishna - One of the best experts on this subject based on the ideXlab platform.
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back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process
Applied Soft Computing, 2017Co-Authors: Manjunath Patel G C, Arun Kumar Shettigar, Prasad Krishna, Mahesh B. ParappagoudarAbstract:Abstract 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 precised control of squeeze casting process.
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an intelligent system for squeeze casting process soft computing based approach
The International Journal of Advanced Manufacturing Technology, 2016Co-Authors: G Manjunath C Patel, Prasad Krishna, Mahesh B. ParappagoudarAbstract: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.
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An intelligent system for squeeze casting process—soft computing based approach
The International Journal of Advanced Manufacturing Technology, 2016Co-Authors: G C Manjunath Patel, Prasad Krishna, Mahesh B. ParappagoudarAbstract: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.
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Forward and reverse process models for the squeeze casting process using neural network based approaches
Soft Computing, 2014Co-Authors: Manjunath Patel Gowdru Chandrashekarappa, Prasad Krishna, Mahesh B. ParappagoudarAbstract:The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing Forward and reverse Mappings of artificial neural networks. Forward Mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN) and genetic algorithm neural network (GA-NN). The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.
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Prediction and Optimization of Dimensional Shrinkage Variations in Injection Molded Parts Using Forward and Reverse Mapping of Artificial Neural Networks
Advanced Materials Research, 2012Co-Authors: Patel G.c. Manjunath, Prasad KrishnaAbstract:The most significant process parameters affecting dimensional shrinkage in transverse and longitudinal directions of molded parts in Plastic Injection Molding (PIM) process are injection velocity, mold temperature, melt temperature and packing pressure. In the present work, ANN model was developed for Forward and reverse Mapping prediction. In Forward Mapping PIM process parameters are expressed as the input parameters to predict dimensional shrinkage, whereas in reverse Mapping, attempts were made to predict an appropriate set of process parameters required for arriving at the required dimensional shrinkage. The trained network with one thousand input-output data randomly generated from regression equations reported by earlier researchers resulted in minimum mean squared error. The performance of developed model was compared with experimental values for ten different test cases. The results show that ANN model with both Forward and reverse Mapping is capable of prediction with an error level of less than ten percent.
Bradley S. Peterson - One of the best experts on this subject based on the ideXlab platform.
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BMEI (2) - Improved Warping of Diffusion Tensor Fields Free of Artifacts
2008 International Conference on BioMedical Engineering and Informatics, 2008Co-Authors: Bradley S. PetersonAbstract:Warping diffusion tensor (DT) fields accurately is much more complicated than that of conventional scalar images. It requires tensors be reoriented in the space to which the tensors are warped based on both the local deformation field and the orientation of the underlying fibers in the original image. Because DT images contain high dimensional information of both spatial orientation and magnitude, standard warping using backward Mapping for regular intensity-based images cannot be applied to warp DT images. Therefore, all existing algorithms for warping tensors typically use Forward Mapping deformations; Forward Mapping, however, can also create artifacts by failing to define accurately the voxels in the template space where the local deformation is expanding. To overcome this disadvantage, we propose a novel method for the spatial normalization of DT fields that uses a bijection to warp DT datasets from one imaging space to another, without generating artifacts.
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Seamless Warping of Diffusion Tensor Fields
IEEE Transactions on Medical Imaging, 2008Co-Authors: Dongrong Xu, Ravi Bansal, Kerstin J. Plessen, Bradley S. PetersonAbstract:To warp diffusion tensor fields accurately, tensors must be reoriented in the space to which the tensors are warped based on both the local deformation field and the orientation of the underlying fibers in the original image. Existing algorithms for warping tensors typically use Forward Mapping deformations in an attempt to ensure that the local deformations in the warped image remains true to the orientation of the underlying fibers; Forward Mapping, however, can also create ldquoseamsrdquo or gaps and consequently artifacts in the warped image by failing to define accurately the voxels in the template space where the magnitude of the deformation is large (e.g., |Jacobian| > 1). Backward Mapping, in contrast, defines voxels in the template space by Mapping them back to locations in the original imaging space. Backward Mapping allows every voxel in the template space to be defined without the creation of seams, including voxels in which the deformation is extensive. Backward Mapping, however, cannot reorient tensors in the template space because information about the directional orientation of fiber tracts is contained in the original, unwarped imaging space only, and backward Mapping alone cannot transfer that information to the template space. To combine the advantages of Forward and backward Mapping, we propose a novel method for the spatial normalization of diffusion tensor (DT) fields that uses a bijection (a bidirectional Mapping with one-to-one correspondences between image spaces) to warp DT datasets seamlessly from one imaging space to another. Once the bijection has been achieved and tensors have been correctly relocated to the template space, we can appropriately reorient tensors in the template space using a warping method based on Procrustean estimation.
Manjunath Patel G C - One of the best experts on this subject based on the ideXlab platform.
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back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process
Applied Soft Computing, 2017Co-Authors: Manjunath Patel G C, Arun Kumar Shettigar, Prasad Krishna, Mahesh B. ParappagoudarAbstract:Abstract 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 precised control of squeeze casting process.
Dilip Kumar Pratihar - One of the best experts on this subject based on the ideXlab platform.
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Recurrent neural networks to model input-output relationships of metal inert gas (MIG) welding process
International Journal of Data Analysis Techniques and Strategies, 2017Co-Authors: Geet Lahoti, Dilip Kumar PratiharAbstract:The mechanical strength of weld-bead is dependent on its geometric parameters like bead height, width and penetration, which depend on input process parameters, namely welding speed, arc voltage, wire feed rate, gas flow rate, nozzle-to-plate distance, torch angle etc. Recurrent neural networks were used for conducting both Forward and reverse Mappings using three approaches. The first approach dealt with the training of Elman network through updating its connecting weights using a back-propagation algorithm. In second approach, a real-coded genetic algorithm was used along with the back-propagation algorithm to tune the network. The third approach utilised a real-coded genetic algorithm only to optimise the network. In Forward Mapping, third approach was found to outperform the others, but in reverse Mapping, first and second approaches were seen to perform better than the third one. The performances of these approaches were found to be data dependent.
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Study on electron beam butt welding of austenitic stainless steel 304 plates and its input–output modelling using neural networks
Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 2011Co-Authors: M. N. Jha, Dilip Kumar Pratihar, Vidyut Dey, T. K. Saha, A. V. BapatAbstract:Butt welding of austenitic stainless steel 304 plates was carried out using an electron beam. Experiments were conducted for various combinations of input process parameters determined according to central composite design. Three input parameters, namely accelerating voltage, beam current, and welding speed were considered during the experiments. The weld-bead parameters, namely bead width and its depth of penetration, and weld strength in terms of yield strength and ultimate tensile strength, were measured as the responses of the process. Input–output modelling of this process was carried out in the Forwards direction using regression analysis, a back-propagation neural network (BPNN), and a genetic algorithm-tuned neural network (GANN). Reverse Mapping of this process was also attempted using the BPNN and GANN-based approaches, although the same could not be done from the obtained regression equations. The GANN was found to outperform the other two approaches in Forward Mapping. In reverse Mapping also,...
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Design and Development of Knowledge Bases for Forward and Reverse Mappings of TIG Welding Process
Intelligent Data Analysis, 2009Co-Authors: J. P. Ganjigatti, Dilip Kumar PratiharAbstract:In this chapter, an attempt has been made to design suitable knowledge bases (KBs) for carrying out Forward and reverse Mappings of a Tungsten inert gas (TIG) welding process. In Forward Mapping, the outputs (also known as the responses) are expressed as the functions of the input variables (also called the factors), whereas in reverse Mapping, the factors are represented as the functions of the responses. Both the Forward as well as reverse Mappings are required to conduct, for an effective online control of a process. Conventional statistical regression analysis is able to carry out the Forward Mapping efficiently but it may not be always able to solve the problem of reverse Mapping. It is a novel attempt to conduct the Forward and reverse Mappings of a TIG welding process using fuzzy logic (FL)-based approaches and these are found to solve the said problem efficiently.
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Modelling of input–output relationships in cement bonded moulding sand system using neural networks
International Journal of Cast Metals Research, 2007Co-Authors: Mahesh B. Parappagoudar, Dilip Kumar Pratihar, G. L. DattaAbstract:Cement bonded sand moulds can be used to cast ferrous metals with a good dimensional control. To determine input–output relationships in the cement bonded moulding sand system, both Forward and reverse Mappings were carried out using feed Forward neural networks trained with the help of a back propagation algorithm and a genetic algorithm, separately. In the Forward Mapping, mould properties, namely compression strength and hardness, were predicted for different combinations of process parameters, such as percentages of cement, of accelerator and of water and testing time. In the reverse Mapping, the process parameters were determined as the functions of mould properties. A batch mode of training had been provided to the neural networks with the help of one thousand training data generated artificially using the conventional statistical regression equations derived earlier by the authors. The performances of the developed models were compared among themselves and with those of the statistical regression model, for twenty randomly generated test cases. Neural network based approaches had proved their ability to carry out both the Mappings. In Forward Mapping, the results of the neural network based approaches were found to be comparable with those of conventional regression analysis. Moreover, the genetic algorithm trained neural network was seen to perform better than the back propagation trained neural network for both the Forward and reverse Mappings.