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Arun Kumar Samantaray - One of the best experts on this subject based on the ideXlab platform.

  • Determination of Optimal Pulse Metal Inert Gas Welding Parameters with a Neuro-GA Technique
    Materials and Manufacturing Processes, 2010
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    Optimization of a manufacturing process is a rigorous task because it has to take into account all the factors that influence the product quality and productivity. Welding is a multi-variable process, which is influenced by a lot of process uncertainties. Therefore, the optimization of Welding process parameters is considerably complex. Advancement in computational methods, evolutionary algorithms, and multiobjective optimization methods create ever-more effective solutions to this problem. This work concerns the selection of optimal parameters setting of pulsed metal Inert Gas Welding (PMIGW) process for any desired output parameters setting. Six process parameters, namely pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate and table feed rate were used as input variables, and the strength of the welded plate, weld bead geometry, transverse shrinkage, angular distortion and deposition efficiency were considered as the output variables. Artificial neural network (ANN) mod...

  • Prediction of the quality of pulsed metal Inert Gas Welding using statistical parameters of arc signals in artificial neural network
    International Journal of Computer Integrated Manufacturing, 2010
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    One of the big challenges in Welding is the prediction of weld-quality without destructive test. This work introduces an intelligent system for weld-quality prediction in a pulsed metal Inert Gas Welding process based on the statistical parameters of the acquired current and voltage signals. Six process parameters and 10 statistical parameters of arc signals are used to describe various Welding conditions. These process features obtained from a set of experiments are employed as input patterns to back propagation neural network and radial basis function network models to predict the corresponding weld qualities. The prediction errors show that the neural network model, which has been trained with the statistical parameters of arc signals along with the process parameters, gives superior prediction of weld quality as compared to that from a model developed with only the process parameters as its inputs.

  • Optimization of quality characteristics parameters in a pulsed metal Inert Gas Welding process using grey-based Taguchi method
    The International Journal of Advanced Manufacturing Technology, 2009
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Santosh K. Malviya, Arun Kumar Samantaray
    Abstract:

    Optimization of a manufacturing process has to take into accounts all of the factors that influence the product quality and productivity. Optimization of Welding process parameters is considerably complex because Welding is a multi-variable process, which is influenced by a lot of process uncertainties. In this paper, a grey-based Taguchi method has been adopted to optimize the pulsed metal Inert Gas Welding process parameters. Many quality characteristic parameters are combined into one integrated quality parameter by using grey relational grade or rank. The Welding process parameters considered in this analysis are pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate, and table feed rate. The quality parameters considered are the tensile strength, bead geometry, transverse shrinkage, angular distortion, and deposition efficiency. Analysis of variance has been performed to find out the impact of individual process parameter on the quality parameters. If the tensile strength as the most important quality parameter is assigned a higher weight, then the pulse voltage was found to be the most influential process parameter. Experiments with the optimized parameter settings, which have been obtained from the analysis, are given to validate the results.

  • Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal Inert Gas Welding process
    Science and Technology of Welding and Joining, 2008
    Co-Authors: Surjya K. Pal, Sukhomay Pal, Arun Kumar Samantaray
    Abstract:

    AbstractThe monitoring of Welding process is crucial for the development of a real time quality control system for the pulsed metal Inert Gas Welding (PMIGW) process. This work introduces an intelligent system for weld joint strength prediction in a PMIGW process based on the analysis of acquired current signal by wavelet packet transform. A thirteen-dimensional array of process features, i.e. six process parameters and seven wavelet packet features, are used to describe various Welding conditions. These process features obtained from a set of experiments are employed as input vectors of an artificial neural network model to predict the corresponding weld joint strengths. The results, i.e. the prediction errors, show that the use of wavelet packet features gives much accurate prediction as compared to the use of the purely time domain features.

  • artificial neural network modeling of weld joint strength prediction of a pulsed metal Inert Gas Welding process using arc signals
    Journal of Materials Processing Technology, 2008
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    This paper addresses the weld joint strength monitoring in pulsed metal Inert Gas Welding (PMIGW) process. Response surface methodology is applied to perform Welding experiments. A multilayer neural network model has been developed to predict the ultimate tensile stress (UTS) of welded plates. Six process parameters, namely pulse voltage, back-ground voltage, pulse duration, pulse frequency, wire feed rate and the Welding speed, and the two measurements, namely root mean square (RMS) values of Welding current and voltage, are used as input variables of the model and the UTS of the welded plate is considered as the output variable. Furthermore, output obtained through multiple regression analysis is used to compare with the developed artificial neural network (ANN) model output. It was found that the Welding strength predicted by the developed ANN model is better than that based on multiple regression analysis.

Sukhomay Pal - One of the best experts on this subject based on the ideXlab platform.

  • Determination of Optimal Pulse Metal Inert Gas Welding Parameters with a Neuro-GA Technique
    Materials and Manufacturing Processes, 2010
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    Optimization of a manufacturing process is a rigorous task because it has to take into account all the factors that influence the product quality and productivity. Welding is a multi-variable process, which is influenced by a lot of process uncertainties. Therefore, the optimization of Welding process parameters is considerably complex. Advancement in computational methods, evolutionary algorithms, and multiobjective optimization methods create ever-more effective solutions to this problem. This work concerns the selection of optimal parameters setting of pulsed metal Inert Gas Welding (PMIGW) process for any desired output parameters setting. Six process parameters, namely pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate and table feed rate were used as input variables, and the strength of the welded plate, weld bead geometry, transverse shrinkage, angular distortion and deposition efficiency were considered as the output variables. Artificial neural network (ANN) mod...

  • Prediction of the quality of pulsed metal Inert Gas Welding using statistical parameters of arc signals in artificial neural network
    International Journal of Computer Integrated Manufacturing, 2010
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    One of the big challenges in Welding is the prediction of weld-quality without destructive test. This work introduces an intelligent system for weld-quality prediction in a pulsed metal Inert Gas Welding process based on the statistical parameters of the acquired current and voltage signals. Six process parameters and 10 statistical parameters of arc signals are used to describe various Welding conditions. These process features obtained from a set of experiments are employed as input patterns to back propagation neural network and radial basis function network models to predict the corresponding weld qualities. The prediction errors show that the neural network model, which has been trained with the statistical parameters of arc signals along with the process parameters, gives superior prediction of weld quality as compared to that from a model developed with only the process parameters as its inputs.

  • Optimization of quality characteristics parameters in a pulsed metal Inert Gas Welding process using grey-based Taguchi method
    The International Journal of Advanced Manufacturing Technology, 2009
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Santosh K. Malviya, Arun Kumar Samantaray
    Abstract:

    Optimization of a manufacturing process has to take into accounts all of the factors that influence the product quality and productivity. Optimization of Welding process parameters is considerably complex because Welding is a multi-variable process, which is influenced by a lot of process uncertainties. In this paper, a grey-based Taguchi method has been adopted to optimize the pulsed metal Inert Gas Welding process parameters. Many quality characteristic parameters are combined into one integrated quality parameter by using grey relational grade or rank. The Welding process parameters considered in this analysis are pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate, and table feed rate. The quality parameters considered are the tensile strength, bead geometry, transverse shrinkage, angular distortion, and deposition efficiency. Analysis of variance has been performed to find out the impact of individual process parameter on the quality parameters. If the tensile strength as the most important quality parameter is assigned a higher weight, then the pulse voltage was found to be the most influential process parameter. Experiments with the optimized parameter settings, which have been obtained from the analysis, are given to validate the results.

  • Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal Inert Gas Welding process
    Science and Technology of Welding and Joining, 2008
    Co-Authors: Surjya K. Pal, Sukhomay Pal, Arun Kumar Samantaray
    Abstract:

    AbstractThe monitoring of Welding process is crucial for the development of a real time quality control system for the pulsed metal Inert Gas Welding (PMIGW) process. This work introduces an intelligent system for weld joint strength prediction in a PMIGW process based on the analysis of acquired current signal by wavelet packet transform. A thirteen-dimensional array of process features, i.e. six process parameters and seven wavelet packet features, are used to describe various Welding conditions. These process features obtained from a set of experiments are employed as input vectors of an artificial neural network model to predict the corresponding weld joint strengths. The results, i.e. the prediction errors, show that the use of wavelet packet features gives much accurate prediction as compared to the use of the purely time domain features.

  • artificial neural network modeling of weld joint strength prediction of a pulsed metal Inert Gas Welding process using arc signals
    Journal of Materials Processing Technology, 2008
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    This paper addresses the weld joint strength monitoring in pulsed metal Inert Gas Welding (PMIGW) process. Response surface methodology is applied to perform Welding experiments. A multilayer neural network model has been developed to predict the ultimate tensile stress (UTS) of welded plates. Six process parameters, namely pulse voltage, back-ground voltage, pulse duration, pulse frequency, wire feed rate and the Welding speed, and the two measurements, namely root mean square (RMS) values of Welding current and voltage, are used as input variables of the model and the UTS of the welded plate is considered as the output variable. Furthermore, output obtained through multiple regression analysis is used to compare with the developed artificial neural network (ANN) model output. It was found that the Welding strength predicted by the developed ANN model is better than that based on multiple regression analysis.

Marcelo J. Dapino - One of the best experts on this subject based on the ideXlab platform.

  • fusion Welding of nickel titanium and 304 stainless steel tubes part ii tungsten Inert Gas Welding
    Journal of Intelligent Material Systems and Structures, 2013
    Co-Authors: Gordon Fox, Ryan Hahnlen, Marcelo J. Dapino
    Abstract:

    Shape memory nickel–titanium is attractive for lightweight actuators as it can generate large blocking stresses and high recovery strains through solid-state operation. A key challenge is the integration of the nickel–titanium components into systems; this alloy is difficult and expensive to machine and challenging to weld to itself and other materials. In this research, we join nickel–titanium and 304 stainless steel tubes of 9.53 mm (0.375 in) in diameter through tungsten Inert Gas Welding. By joining nickel–titanium to a common structural material that is easily machined and readily welded to other materials, the system integration challenges are greatly reduced. The joints prepared in this study were subjected to optical microscopic inspection, hardness mapping, energy dispersive X-ray spectroscopy, mechanical testing, and failure surface analysis via scanning electron microscopy. The affected zone from Welding is approximately 125 µm (0.005 in) wide including partially mixed zones with a maximum hard...

  • Fusion Welding of nickel–titanium and 304 stainless steel tubes: Part II: tungsten Inert Gas Welding
    Journal of Intelligent Material Systems and Structures, 2012
    Co-Authors: Gordon Fox, Ryan Hahnlen, Marcelo J. Dapino
    Abstract:

    Shape memory nickel–titanium is attractive for lightweight actuators as it can generate large blocking stresses and high recovery strains through solid-state operation. A key challenge is the integration of the nickel–titanium components into systems; this alloy is difficult and expensive to machine and challenging to weld to itself and other materials. In this research, we join nickel–titanium and 304 stainless steel tubes of 9.53 mm (0.375 in) in diameter through tungsten Inert Gas Welding. By joining nickel–titanium to a common structural material that is easily machined and readily welded to other materials, the system integration challenges are greatly reduced. The joints prepared in this study were subjected to optical microscopic inspection, hardness mapping, energy dispersive X-ray spectroscopy, mechanical testing, and failure surface analysis via scanning electron microscopy. The affected zone from Welding is approximately 125 µm (0.005 in) wide including partially mixed zones with a maximum hard...

Xinjian Yuan - One of the best experts on this subject based on the ideXlab platform.

  • Improvement of Al/Steel Tungsten Inert Gas Welding–Brazing Joint by High-Energy Shot Peening
    Journal of Materials Engineering and Performance, 2019
    Co-Authors: Xinjian Yuan, Haodong Wang, Jun Luo
    Abstract:

    High-energy shot peening treatment was applied to improve the Al/steel tungsten Inert Gas Welding–brazing lap joint. The mechanical properties and microstructure evolution of the joint were investigated. Results showed that the mechanical properties of weld were evidently reinforced due to microstructure transformation of joint surface layer. The grain structure in the joint surface layer was distorted and refined. Large compressive residual stress was introduced into the joint surface, and its hardness obviously increased due to strain hardening. When the peening pressure was 0.15 MPa, the tensile shear strength of the joint reached the maximum value (199.6 MPa), which was 16.5% higher than that of the as-welded joint.

  • reinforcement of mg ti joints using ultrasonic assisted tungsten Inert Gas Welding brazing technology
    Science and Technology of Welding and Joining, 2014
    Co-Authors: Guangmin Sheng, Haodong Wang, Xinjian Yuan
    Abstract:

    Ultrasonic assisted tungsten Inert Gas Welding–brazing technology was developed to refine coarsening columnar α-Mg grains of Mg/Ti joints. In this study, ultrasonic vibration was introduced into molten pool of Mg/Ti joints with frequency of 20 kHz and maximum power of 1·6 kW. The results showed that, with ultrasonic power of 1·2 kW, the morphology of columnar α-Mg grains was refined to approximately equiaxed grains and the average grain size of columnar grains decreased from 200 to ∼50 μm. Moreover, the maximum joint strength of joints increased ∼18·1% to 228 N mm−1 over the joints welded without ultrasonic vibration (193 N mm−1). Furthermore, the optimised Mg/Ti joint fractured at base metal zone rather than fusion zone upon tensile–shear loading, indicating that efficient grain refinement was attained. However, Welding voids occurred with the ultrasonic power further increased to 1·6 kW, which resulted in the decrease in mechanical properties.

  • Reinforcement of Mg/Ti joints using ultrasonic assisted tungsten Inert Gas Welding–brazing technology
    Science and Technology of Welding and Joining, 2014
    Co-Authors: Guangmin Sheng, Haodong Wang, Xinjian Yuan
    Abstract:

    Ultrasonic assisted tungsten Inert Gas Welding–brazing technology was developed to refine coarsening columnar α-Mg grains of Mg/Ti joints. In this study, ultrasonic vibration was introduced into molten pool of Mg/Ti joints with frequency of 20 kHz and maximum power of 1·6 kW. The results showed that, with ultrasonic power of 1·2 kW, the morphology of columnar α-Mg grains was refined to approximately equiaxed grains and the average grain size of columnar grains decreased from 200 to ∼50 μm. Moreover, the maximum joint strength of joints increased ∼18·1% to 228 N mm−1 over the joints welded without ultrasonic vibration (193 N mm−1). Furthermore, the optimised Mg/Ti joint fractured at base metal zone rather than fusion zone upon tensile–shear loading, indicating that efficient grain refinement was attained. However, Welding voids occurred with the ultrasonic power further increased to 1·6 kW, which resulted in the decrease in mechanical properties.

Surjya K. Pal - One of the best experts on this subject based on the ideXlab platform.

  • Determination of Optimal Pulse Metal Inert Gas Welding Parameters with a Neuro-GA Technique
    Materials and Manufacturing Processes, 2010
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    Optimization of a manufacturing process is a rigorous task because it has to take into account all the factors that influence the product quality and productivity. Welding is a multi-variable process, which is influenced by a lot of process uncertainties. Therefore, the optimization of Welding process parameters is considerably complex. Advancement in computational methods, evolutionary algorithms, and multiobjective optimization methods create ever-more effective solutions to this problem. This work concerns the selection of optimal parameters setting of pulsed metal Inert Gas Welding (PMIGW) process for any desired output parameters setting. Six process parameters, namely pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate and table feed rate were used as input variables, and the strength of the welded plate, weld bead geometry, transverse shrinkage, angular distortion and deposition efficiency were considered as the output variables. Artificial neural network (ANN) mod...

  • Prediction of the quality of pulsed metal Inert Gas Welding using statistical parameters of arc signals in artificial neural network
    International Journal of Computer Integrated Manufacturing, 2010
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    One of the big challenges in Welding is the prediction of weld-quality without destructive test. This work introduces an intelligent system for weld-quality prediction in a pulsed metal Inert Gas Welding process based on the statistical parameters of the acquired current and voltage signals. Six process parameters and 10 statistical parameters of arc signals are used to describe various Welding conditions. These process features obtained from a set of experiments are employed as input patterns to back propagation neural network and radial basis function network models to predict the corresponding weld qualities. The prediction errors show that the neural network model, which has been trained with the statistical parameters of arc signals along with the process parameters, gives superior prediction of weld quality as compared to that from a model developed with only the process parameters as its inputs.

  • Optimization of quality characteristics parameters in a pulsed metal Inert Gas Welding process using grey-based Taguchi method
    The International Journal of Advanced Manufacturing Technology, 2009
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Santosh K. Malviya, Arun Kumar Samantaray
    Abstract:

    Optimization of a manufacturing process has to take into accounts all of the factors that influence the product quality and productivity. Optimization of Welding process parameters is considerably complex because Welding is a multi-variable process, which is influenced by a lot of process uncertainties. In this paper, a grey-based Taguchi method has been adopted to optimize the pulsed metal Inert Gas Welding process parameters. Many quality characteristic parameters are combined into one integrated quality parameter by using grey relational grade or rank. The Welding process parameters considered in this analysis are pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate, and table feed rate. The quality parameters considered are the tensile strength, bead geometry, transverse shrinkage, angular distortion, and deposition efficiency. Analysis of variance has been performed to find out the impact of individual process parameter on the quality parameters. If the tensile strength as the most important quality parameter is assigned a higher weight, then the pulse voltage was found to be the most influential process parameter. Experiments with the optimized parameter settings, which have been obtained from the analysis, are given to validate the results.

  • Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal Inert Gas Welding process
    Science and Technology of Welding and Joining, 2008
    Co-Authors: Surjya K. Pal, Sukhomay Pal, Arun Kumar Samantaray
    Abstract:

    AbstractThe monitoring of Welding process is crucial for the development of a real time quality control system for the pulsed metal Inert Gas Welding (PMIGW) process. This work introduces an intelligent system for weld joint strength prediction in a PMIGW process based on the analysis of acquired current signal by wavelet packet transform. A thirteen-dimensional array of process features, i.e. six process parameters and seven wavelet packet features, are used to describe various Welding conditions. These process features obtained from a set of experiments are employed as input vectors of an artificial neural network model to predict the corresponding weld joint strengths. The results, i.e. the prediction errors, show that the use of wavelet packet features gives much accurate prediction as compared to the use of the purely time domain features.

  • artificial neural network modeling of weld joint strength prediction of a pulsed metal Inert Gas Welding process using arc signals
    Journal of Materials Processing Technology, 2008
    Co-Authors: Sukhomay Pal, Surjya K. Pal, Arun Kumar Samantaray
    Abstract:

    This paper addresses the weld joint strength monitoring in pulsed metal Inert Gas Welding (PMIGW) process. Response surface methodology is applied to perform Welding experiments. A multilayer neural network model has been developed to predict the ultimate tensile stress (UTS) of welded plates. Six process parameters, namely pulse voltage, back-ground voltage, pulse duration, pulse frequency, wire feed rate and the Welding speed, and the two measurements, namely root mean square (RMS) values of Welding current and voltage, are used as input variables of the model and the UTS of the welded plate is considered as the output variable. Furthermore, output obtained through multiple regression analysis is used to compare with the developed artificial neural network (ANN) model output. It was found that the Welding strength predicted by the developed ANN model is better than that based on multiple regression analysis.