scholarly journals Hot Wire-Assisted Gas Metal Arc Welding of Ni-Based Hardfacing

2018 ◽  
Vol 97 (4) ◽  
pp. 99-107 ◽  
Author(s):  
Keyword(s):  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Hot Wire ◽  
Metal Arc Welding

scholarly journals Metal Transfer Mechanisms in Hot-Wire Gas Metal Arc Welding

2020 ◽  
Vol 99 (11) ◽  
pp. 281s-294s
Author(s):  
P. P. G. RIBEIRO ◽  
◽  
P. D. C. ASSUNÇÃO ◽  
E. M. BRAGA ◽  
R. A. RIBEIRO ◽  
...  
Keyword(s):  
Cross Sections ◽  
Arc Welding ◽  
High Speed ◽  
Gas Metal Arc Welding ◽  
Melting Rate ◽  
Bead Geometry ◽  
Hot Wire ◽  
High Speed Imaging ◽  
Metal Arc Welding ◽  
Full Factorial

The hot-wire gas metal arc welding (HW-GMAW) process is widely used to increase the melting rate of a secondary wire through Joule heating without significantly increasing the total heat input to the substrate. Because there is limit-ed knowledge regarding the associated arc dynamics and its influence on bead geometry, the present study considers how these are affected by the hot-wire polarity (negative or positive), hot-wire feed rate, and hot-wire orientation using a two-factor full factorial experiment with three replicates. During welding, high-speed imaging synchronized with current and voltage acquisition to study the arc dynamics. After this, each replicated weld was cut into three cross sections, which were examined by standard metallography. The preliminary results suggest that the arc was stable within the range of process parameters studied. The arc polarity played a role on arc position relative to the hot wire, with a decrease in penetration depth observed when the arc was attracted to the hot wire.

scholarly journals Advanced Gas Tungsten Arc Weld Surfacing Current Status and Application

2015 ◽  
Vol 20 (3) ◽  
pp. 300-314 ◽  
Author(s):  
Stephan Egerland ◽  
Johannes Zimmer ◽  
Roland Brunmaier ◽  
Roland Nussbaumer ◽  
Gerhard Posch ◽  
...  
Keyword(s):  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Current Status ◽  
Filler Materials ◽  
Hot Wire ◽  
Gas Tungsten Arc ◽  
Metal Arc Welding ◽  
Welding Automation ◽  
Increase Productivity ◽  
Nickel Base

Abstract Gas Shielded Tungsten Arc Welding (GTAW) – a process well-known providing highest quality weld results joined though by lower performance. Gas Metal Arc Welding (GMAW) is frequently chosen to increase productivity along with broadly accepted quality. Those industry segments, especially required to produce high quality corrosion resistant weld surfacing e.g. applying nickel base filler materials, are regularly in consistent demand to comply with "zero defect" criteria. In this conjunction weld performance limitations are overcome employing advanced 'hot-wire' GTAW systems. This paper, from a Welding Automation perspective, describes the technology of such devices and deals with the current status is this field – namely the application of dual-cathode hot-wire electrode GTAW cladding; considerably broadening achievable limits.

scholarly journals Two gas metal arc welding process dataset of arc parameters and input parameters

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106790
Author(s):  
Rogfel Thompson Martinez ◽  
Guillermo Alvarez Bestard ◽  
Sadek C. Absi Alfaro
Keyword(s):  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Metal Arc Welding ◽  
Input Parameters ◽  
Arc Welding Process

scholarly journals Out-of-Control Multivariate Patterns Recognition Using D2 and SVM: A Study Case for GMAW

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 467
Author(s):  
Pamela Chiñas-Sanchez ◽  
Ismael Lopez-Juarez ◽  
Jose Antonio Vazquez-Lopez ◽  
Jose Luis Navarro-Gonzalez ◽  
Aidee Hernandez-Lopez
Keyword(s):  
Arc Welding ◽  
New Technologies ◽  
Gas Metal Arc Welding ◽  
Industrial Processes ◽  
Support Vector ◽  
Innovative Method ◽  
Metal Arc Welding ◽  
Vector Machines ◽  
Study Case ◽  
Multivariate Pattern

Industrial processes seek to improve their quality control, including new technologies and satisfying requirements for globalised markets. In this paper, we present an innovative method based on Multivariate Pattern Recognition (MVPR) and process monitoring in a real-world study case. By identifying a distinctive out-of-control multivariate pattern using the Support Vector Machines (SVM) and the Mahalanobis Distance D2 it is possible to infer the variables that disturbed the process; hence, possible faults can be predicted knowing the state of the process. The method is based on our previous work, and in this paper we present the method application for an automated process, namely, the robotic Gas Metal Arc Welding (GMAW). Results from the application indicate an overall accuracy up to 88.8%, which demonstrates the effectiveness of the method, which can also be used in other MVPR tasks.

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