Monitoring Gas Metal Arc Welding Process by Using Audible Sound Signal

Horvat, J.; Prezelj, Jurij; Polajnar, Ivan; Čudina, Mirko

doi:10.5545/sv-jme.2010.181

Cited by 40 publications

(24 citation statements)

References 4 publications

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“…Traditionally, in fusion welding, monitoring is made through an evaluation of current, voltage, shielding gas flow rate, travel speed, and wire feed speed. In addition, during WAAM, sensors can be used to monitor the temperature at different regions [151], measure the size and geometry of beads [152,153], determine the weld pool characteristics, monitor the acoustic signal of deposition [154], detect electrical conductivity variations [155], and measure oxygen levels [156]. Normally, in fusion-based welding, the process parameters are held constant, but in WAAM, due to differences of thermal behavior throughout parts fabrication, geometric variations and mechanical properties are established and adjustments are necessary.…”

Section: Defects and Non-destructive Testingmentioning

confidence: 99%

“…Xu et al [148] reviewed process monitoring and control of WAAM parts and proposed a multi-sensor device to monitor each variant and output, as schematically shown in Figure 32. It considers an acoustic sensor for measuring arc pulsation and intensity, since when an irregularity occurs it will be reflected on the acoustic signal and on the signal of the current [154]. Infrared camera, thermocouples, or pyrometers would be used to monitor the molten pool and thermal cycles.…”

Section: Defects and Non-destructive Testingmentioning

confidence: 99%

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Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM)

et al. 2019

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Additive manufacturing has revolutionized the manufacturing paradigm in recent years due to the possibility of creating complex shaped three-dimensional parts which can be difficult or impossible to obtain by conventional manufacturing processes. Among the different additive manufacturing techniques, wire and arc additive manufacturing (WAAM) is suitable to produce large metallic parts owing to the high deposition rates achieved, which are significantly larger than powder-bed techniques, for example. The interest in WAAM is steadily increasing, and consequently, significant research efforts are underway. This review paper aims to provide an overview of the most significant achievements in WAAM, highlighting process developments and variants to control the microstructure, mechanical properties, and defect generation in the as-built parts; the most relevant engineering materials used; the main deposition strategies adopted to minimize residual stresses and the effect of post-processing heat treatments to improve the mechanical properties of the parts. An important aspect that still hinders this technology is certification and nondestructive testing of the parts, and this is discussed. Finally, a general perspective of future advancements is presented.

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Section: Defects and Non-destructive Testingmentioning

confidence: 99%

Section: Defects and Non-destructive Testingmentioning

confidence: 99%

Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM)

et al. 2019

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“…Current advanced sensor technology provides accurate and comprehensive information about the welding process, and multi-variable parameter control has thus been approached with the use of AI decision-making systems. AI-based control systems have been integrated with various sensors such as laser sensors, thermal sensors, arc imaging, and acoustic sensors to address quality inconsistency in conventional automated welding [10,12,13,[18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Controlled GMAW System: Penetration and Quality Assurance in a Multi-Pass Butt Weld Application

Penttilä

Kah

Ratava

et al. 2019

Int J Adv Manuf Technol

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Intelligent welding parameter control is fast becoming a key instrument for attaining quality consistency in automated welding. Recent scientific breakthroughs in intelligent systems have turned the focus of adaptive welding control to artificial intelligencebased welding parameter control. The aim of this study is to combine artificial neural network (ANN) decision-making software and a machine vision system to develop an adaptive artificial intelligence (AI)-based gas metal arc welding (GMAW) parameter control system. The machine vision system uses a laser sensor to scan the upcoming seam and gather seam profile data. Based on further processing of the seam profile data, welding parameters are optimized by the decision-making system. In this work, the developed system is tested in a multivariable welding condition environment and its performance is evaluated. The quality of the welds was consistent and surpassed the required quality level. Additionally, the heat-affected zone (HAZ) was evaluated by microscopy, X-ray, and scanning electron microscope (SEM) imaging. It is concluded that the developed ANN system is suitable for implementation in automated applications, can improve quality consistency and cost efficiency, and reduce required workpiece preparation and handling.

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“…During processes of arc welding and laser welding, various types of sources can provide online information relevant to the weld quality, such as arc voltage [ 14 ], welding current [ 15 ], audible sound [ 16 ], acoustic emissions [ 17 , 18 , 19 ], as well as the optical or thermal radiation that is generated from electric arc, molten pool, plasma plume, and metallic vapor [ 20 , 21 , 22 , 23 ]. A promising approach is to use machine vision to the in-process weld pool monitoring, as this provides an access to abundant and direct-viewing information about the process dynamics that closely related to weld bead formation and some defects [ 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

In-Process Monitoring of Lack of Fusion in Ultra-Thin Sheets Edge Welding Using Machine Vision

Hong

Chang

Peng

et al. 2018

Sensors

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Lack of fusion can often occur during ultra-thin sheets edge welding process, severely destroying joint quality and leading to seal failure. This paper presents a vision-based weld pool monitoring method for detecting a lack of fusion during micro plasma arc welding (MPAW) of ultra-thin sheets edge welds. A passive micro-vision sensor is developed to acquire clear images of the mesoscale weld pool under MPAW conditions, continuously and stably. Then, an image processing algorithm has been proposed to extract the characteristics of weld pool geometry from the acquired images in real time. The relations between the presence of a lack of fusion in edge weld and dynamic changes in weld pool characteristic parameters are investigated. The experimental results indicate that the abrupt changes of extracted weld pool centroid position along the weld length are highly correlated with the occurrences of lack of fusion. By using such weld pool characteristic information, the lack of fusion in MPAW of ultra-thin sheets edge welds can be detected in real time. The proposed in-process monitoring method makes the early warning possible. It also can provide feedback for real-time control and can serve as a basis for intelligent defect identification.

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Monitoring Gas Metal Arc Welding Process by Using Audible Sound Signal

Cited by 40 publications

References 4 publications

Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM)

Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM)

Artificial Neural Network Controlled GMAW System: Penetration and Quality Assurance in a Multi-Pass Butt Weld Application

In-Process Monitoring of Lack of Fusion in Ultra-Thin Sheets Edge Welding Using Machine Vision

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