Hot-wire GTAW cladding: inconel 625 on 347 stainless steel

Silwal, Bishal; Walker, James S.; West, Daniel W. D.

doi:10.1007/s00170-019-03448-0

Cited by 30 publications

(8 citation statements)

References 22 publications

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“…Hot-wire GTAW [104] is an improved process ( Fig. 18) where the wire and work-piece are separately connected to the two terminals of an added power sources in series.…”

Section: Filling Processesmentioning

confidence: 99%

Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Zhang

Yang

Wei

et al. 2020

Journal of Manufacturing Science and Engineering

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Welding is a major manufacturing process that joins two or more pieces of materials together through heating/mixing them followed by cooling/solidification. The goal of welding manufacturing is to join materials together to meet service requirements at lowest costs. Advanced welding manufacturing is to use scientific methods to realize this goal. This paper views advanced welding manufacturing as a three step approach: (1) pre-design that selects process and joint design based on available processes (properties, capabilities, and costs); (2) design that uses models to predict the result from a given set of welding parameters and minimizes a cost function for optimizing the welding parameters; (3) real-time sensing and control that overcome the deviations of welding conditions from their nominal ones used in optimizing the welding parameters by adjusting the welding parameters based on such real-time sensing and feedback control. The paper analyzes how these three steps depend on process properties/capabilities, process innovations, predictive models, numerical models for fluid dynamics, numerical models for structures, real-time sensing, and dynamic control. The paper also identifies the challenges in obtaining ideal solutions and reviews/analyzes the existing efforts toward better solutions. Special attention and analysis have been given to (1) gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW) as benchmark processes for penetration and materials filling; (2) keyhole plasma arc welding (PAW), keyhole-tungsten inert gas (K-TIG), and keyhole laser welding as improved/capable penetrative processes; (3) friction stir welding (FSW) ......

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“…Hot-wire GTAW [104] is an improved process ( Fig. 18) where the wire and work-piece are separately connected to the two terminals of an added power sources in series.…”

Section: Filling Processesmentioning

confidence: 99%

Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Zhang

Yang

Wei

et al. 2020

Journal of Manufacturing Science and Engineering

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“…Hence, the applications of wire-feed DED can be found in the aerospace industry, e.g., mount rings for entire fan cases [3] and features of compressor casings [4] of turbo jet engines as well as space rocket nozzles [5]. Thus, an increased interest in the use of the wire-feed DED for processing of high temperature and corrosion resistant metals, like stainless steels [6][7][8], Nickel [8][9][10][11] and Titanium-based [12,13] alloys, is being observed.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the excessive heat input, the high temperature gradients and the heat accumulation related to the multiple heating cycles, can lead to high residual stresses [11], shape distortions [14] and cracking [2] as well as undesirable effects on the resulting microstructure [15][16][17] and the phase distribution [18]. On the other hand, too low heat input can cause insufficient melting, leading to an insufficient penetration [19] and dilution [8] or lack of fusion (LoF) [19] as well as promote pore formation [20], impairing the mechanical properties [19]. Therefore, the ability to better adjust the heat input is a necessary step for further development of the technology.…”

Section: Introductionmentioning

confidence: 99%

Hot-Wire Laser-Directed Energy Deposition: Process Characteristics and Benefits of Resistive Pre-Heating of the Feedstock Wire

Kisielewicz

Pandian

Sthen

et al. 2021

Metals

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This study investigates the influence of resistive pre-heating of the feedstock wire (here called hot-wire) on the stability of laser-directed energy deposition of Duplex stainless steel. Data acquired online during depositions as well as metallographic investigations revealed the process characteristic and its stability window. The online data, such as electrical signals in the pre-heating circuit and images captured from side-view of the process interaction zone gave insight on the metal transfer between the molten wire and the melt pool. The results show that the characteristics of the process, like laser-wire and wire-melt pool interaction, vary depending on the level of the wire pre-heating. In addition, application of two independent energy sources, laser beam and electrical power, allows fine-tuning of the heat input and increases penetration depth, with little influence on the height and width of the beads. This allows for better process stability as well as elimination of lack of fusion defects. Electrical signals measured in the hot-wire circuit indicate the process stability such that the resistive pre-heating can be used for in-process monitoring. The conclusion is that the resistive pre-heating gives additional means for controlling the stability and the heat input of the laser-directed energy deposition.

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“…Zhang et al [9] deposited hardfacing using a fusion technique obtained by the arc between the tungsten electrode and the additional metal, successfully obtaining a coating with low dilution. Another successful technique for obtaining low dilution was tried by Silwal et al [10] using hot-wire with resistive arc heat to preheat the wire. Wang et al [11] presented an innovation in the process of depositing hot-wire GTAW coatings where a and c scheme of the welding torch and wire feeders secondary bypass arc energy was used to heat the wire.…”

Section: Introductionmentioning

confidence: 99%

Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm

Tomaz

Colaço

Sarfraz

et al. 2021

Int J Adv Manuf Technol

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Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R2) and RMSE value of all response parameters are satisfactory, and the R2 of all the data remained higher than 0.65.

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Hot-wire GTAW cladding: inconel 625 on 347 stainless steel

Cited by 30 publications

References 22 publications

Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Hot-Wire Laser-Directed Energy Deposition: Process Characteristics and Benefits of Resistive Pre-Heating of the Feedstock Wire

Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm

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