Dissimilar metal welding of P91 steel-AISI 316L SS with Incoloy 800 and Inconel 600 interlayers by using activated TIG welding process and its effect on the microstructure and mechanical properties

Kulkarni, Anup; Dwivedi, Dheerendra Kumar; Vasudevan, M.

doi:10.1016/j.jmatprotec.2019.116280

Cited by 100 publications

(21 citation statements)

References 27 publications

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“…Apart from different merits introduced for GTAW process, shallow penetration has been considered as a drawback especially for joining thick parts [1−3]. To cope with the mentioned drawback of poor (low) penetration, different procedures have been introduced among which using a paste like coating of different activating fluxes on the weld surface, known as activated GTAW (A-GTAW) process is the most important ones [4,5]. In this process a layer of activating flux/fluxes (including oxide, fluoride, and chloride) on the specimens' surface has been coated prior the process begins.…”

Section: Introductionmentioning

confidence: 99%

Activated Gas Tungsten Arc Welding Process Optimization Using Artificial Neural Network and Heuristic Algorithms

Moghaddam¹,

Kolahan²

2021

Preprint

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Apart from different merits of using conventional gas tungsten arc welding (C-GTAW) process, shallow penetration has been considered as the most important drawback of the process. Recently, in order to cope with the low penetration, using a paste like coating of activating flux during welding process known as activated GTAW (A-GTAW) has been proposed. In this paper, effect of A-GTAW process input parameters (welding speed (S), welding current (C)) and percentage of activating fluxes (TiO2 and SiO2) combination (F)) on the most important quality characteristics (weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR)) for AISI316L parts have been considered. The data needed for the modeling and optimization objectives, box-behnken design (BBD) of experiments, back propagation neural network (BPNN), simulated annealing (SA), and particle swarm optimization (PSO) algorithms have been employed. Moreover, PSO algorithm has been used to determine the proper ANN architecture (hidden layers number and their corresponding neurons/nodes) and optimize the proper ANN model to obtain the desired aspect ratio, maximum depth of penetration, and minimum weld bead width. Next, SA algorithm has been used to avoid getting trapped in local minima. Finally, confirmation experimental tests have been carried out to evaluate the performance of the proposed method. Due to the obtained results, the suggested method for modeling and optimization of A-GTAW process is quite efficient (with less than 4% error).

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Section: Introductionmentioning

confidence: 99%

Activated Gas Tungsten Arc Welding Process Optimization Using Artificial Neural Network and Heuristic Algorithms

Moghaddam¹,

Kolahan²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Apart from different advantages introduced for GTAW process, shallow penetration has been considered as a prominent drawback, coping with which different procedures have been introduced [1−3]. Recently, using a paste like coating of activating fluxes on the weld surface, known as activated GTAW (A-GTAW) process is the most important approach to enhance the TIG welding process through increasing the depth of penetration [4,5]. In this process a layer of activating flux/fluxes (including oxide, fluoride, and chloride) on the specimens' surface has been coated prior the process begins.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Enhanced TIG Welding Process Using Artificial Neural Network and Heuristic Algorithms

Moghaddam

Kolahan

2021

Preprint

View full text Add to dashboard Cite

Using conventional gas tungsten arc welding (C-GTAW) process includes some demerits, shallow penetration has been considered as the most important ones. Recently, in order to cope with the mentioned disadvantage (low penetration), using a paste like coating of activating flux during welding process known as activated GTAW (AGTAW) has been proposed. In this paper, effect of A-GTAW process input adjusting parameters including welding speed (S), welding current (C) and percentage of activating fluxes (TiO2 and SiO2) combination (F) on weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR) (the most important quality characteristics) in welding of AISI316L parts have been studied. Box-behnken design (BBD) of experiments has been used to prepare the required experimental matrix for modeling and optimization objectives. Back propagation neural network (BPNN), architecture (hidden layers number and their corresponding neurons/nodes) of which has been determined using heuristic algorithm employed to model the process outputs, the most fitted ones have been optimized using simulated annealing (SA), and particle swarm optimization (PSO) algorithms in order to obtain the desired aspect ratio, maximum depth of penetration, and minimum weld bead width. Finally, confirmation experimental tests have been carried out to evaluate the performance of the proposed method. Due to the obtained results, the suggested method for modeling and optimization of A-GTAW process is quite efficient (with less than 4% error).

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“…Among the proposed procedures activated GTAW (A-GTAW) is the most important ones. In A-GTAW process a coating of activating uxes is prepared and applied on the surface of the weldment before the welding process begins [4,5]. As the welding process proceeds, the coated layer is melted and vaporized.…”

Section: Introductionmentioning

confidence: 99%

Using a back propagation neutral network based modeling and heuristic algorithms based optimization technique in activated gas tungsten arc welding process

Moghaddam¹,

Kolahan²

2021

Preprint

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In this study, a modeling method based on an artificial neural networks model combined with a back propagation algorithm (BPNN) and an optimization procedure based on heuristic algorithms (particle swarm optimization (PSO) and simulated annealing (SA) algorithms) have been proposed for modeling and optimization of activated gas tungsten arc welding (A-GTAW) process in order to tackle the poor penetration drawback occurs during GTAW process. in this study effect of the most important process adjusting variables including welding current (C), welding speed (S)) and percentage of activating fluxes (TiO2 and SiO2) combination (F) on the most important quality characteristics (weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR)) in welding of AISI316L austenite stainless steel parts have been investigated. Box-behnken and central composite designs (BBD and CCD) based on response surface methodology (RSM) in design of experiments (DOE) method have been employed to gather the required data for modeling and optimization purposes. Then, BPNN has been used to determine the relations between A-GTAW process input variables and output responses. To determine the proper BPNN model architecture (the proper hidden layers’ number and their corresponding neurons/nodes in each layer) PSO algorithm has been used. Next, PSO and SA algorithms have been used to optimize the proposed BPNN model in such a way that desired AR, minimum WBW, and maximum DOP achieved. Finally, confirmation experimental tests have been conducted to evaluate the proposed procedure performance. Based on the results, the proposed method is efficient in modeling and optimization (less than 7% error) of A-GTAW process.

show abstract

Dissimilar metal welding of P91 steel-AISI 316L SS with Incoloy 800 and Inconel 600 interlayers by using activated TIG welding process and its effect on the microstructure and mechanical properties

Cited by 100 publications

References 27 publications

Activated Gas Tungsten Arc Welding Process Optimization Using Artificial Neural Network and Heuristic Algorithms

Activated Gas Tungsten Arc Welding Process Optimization Using Artificial Neural Network and Heuristic Algorithms

Optimization of Enhanced TIG Welding Process Using Artificial Neural Network and Heuristic Algorithms

Using a back propagation neutral network based modeling and heuristic algorithms based optimization technique in activated gas tungsten arc welding process

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