Sensoring Fusion Data from the Optic and Acoustic Emissions of Electric Arcs in the GMAW-S Process for Welding Quality Assessment

Alfaro, Sadek Crisóstomo Absi; Cayo, Eber Huanca

doi:10.3390/s120606953

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…The acoustic signal was used to detect defects in [33,94,101,[118][119][120] or to identify the transfer mode in [121]. Figure 12 shows the variation of the waveform of the acoustic signal for different penetration states.…”

Section: Estimation Of the Weld Bead Depthmentioning

confidence: 99%

Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

Bestard

Alfaro

2018

J Braz. Soc. Mech. Sci. Eng.

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The arc welding process is widely used in industry, but the automatic control is limited by the difficulty in the process for measuring the principal magnitudes and to close the control loop. Adverse environmental conditions make use of conventional measurement systems difficult for obtaining information of the weld bead geometry. Under these conditions, indirect sensing techniques are a good option. Different sensing and estimation techniques are used, but few researchers are focusing on the flat welding position. The theory and practice prove that the dynamic models are the best representation to control the welding process, but most studies are performed with static models. This work is a review of the algorithms and sensing techniques used for collecting values of the arc welding process that allow the measurement or estimation of the weld bead geometry. Special attention is given to sensor fusion techniques due to its promising future in the welding process. Discussed in this text are the papers, patents, thesis and other documents found on the theme. It shows a summary of their evolution over the last 50 years.

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Section: Estimation Of the Weld Bead Depthmentioning

confidence: 99%

Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

Bestard

Alfaro

2018

J Braz. Soc. Mech. Sci. Eng.

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“…As an example, the weld pool vision and infrared information can be combined to obtain the weld pool dimensions with more accuracy, or the combination of the weld bead width (from a vision system) and the back bead thermographic information (from a pyrometer) to estimate the bead penetration [ 22 , 23 ]. Nowadays, because of these and other advantages, sensor fusion systems were studied to obtain an effective weld pool and weld bead estimations [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes

Bestard

Sampaio

Vargas

et al. 2018

Sensors

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The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments.

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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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Sensoring Fusion Data from the Optic and Acoustic Emissions of Electric Arcs in the GMAW-S Process for Welding Quality Assessment

Cited by 16 publications

References 9 publications

Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

Measurement and estimation of the weld bead geometry in arc welding processes: the last 50 years of development

Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes

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

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