A Detailed Forecast of the Technologies Based on Lifecycle Analysis of GMAW and CMT Welding Processes

Oliveira, André Souza; Santos, Raphael Oliveira dos; Silva, Bruno Caetano dos Santos; Guarieiro, Lílian Lefol Nani; Angerhausen, Matthias; Reisgen, Uwe; Sampaio, Renelson Ribeiro; Machado, Bruna Aparecida Souza; Droguett, Enrique López; Silva, Paulo Henrique Ferreira da; Coelho, Rodrigo Santiago

doi:10.3390/su13073766

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…They described the relationship between the arc energy and the emitted sound in a formula that was verified by Cayo et al [15,16]. The formula is shown in (1) with S a representing the emitted sound pressure of the arc in dependence of time. Within this equation, k is used as a proportional factor, which considers a geometrical factor α, the adiabatic air expansion coefficient γ and the speed of sound in the air c to take the transmission in air into account.…”

Section: Introductionmentioning

confidence: 93%

“…Gas metal arc welding (GMAW) is one of the most used welding processes in the world and provides high productivity besides low equipment costs. The process is highly automatable and is used in different industry sectors like the automotive, structural engineering or shipbuilding sectors [1]. Despite the high level of automation of the welding process, most test procedures are off-line.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

Rohe

Stoll

Hildebrand

et al. 2021

JMMP

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Today, the quality of welded seams is often examined off-line with either destructive or non-destructive testing. These test procedures are time-consuming and therefore costly. This is especially true if the welds are not welded accurately due to process anomalies. In manual welding, experienced welders are able to detect process anomalies by listening to the sound of the welding process. In this paper, an approach to transfer the “hearing” of an experienced welder into an automated testing process is presented. An acoustic measuring device for recording audible sound is installed for this purpose on a fully automated welding fixture. The processing of the sound information by means of machine learning methods enables in-line process control. Existing research results until now show that the arc is the main sound source. However, both the outflow of the shielding gas and the wire feed emit sound information. Other investigations describe welding irregularities by evaluating and assessing existing sound recordings. Descriptive analysis was performed to find a connection between certain sound patterns and welding irregularities. Recent contributions have used machine learning to identify the degree of welding penetration. The basic assumption of the presented investigations is that process anomalies are the cause of welding irregularities. The focus was on detecting deviating shielding gas flow rates based on audio recordings, processed by a convolutional neural network (CNN). After adjusting the hyperparameters of the CNN it was capable of distinguishing between different flow rates of shielding gas.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

Rohe

Stoll

Hildebrand

et al. 2021

JMMP

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“…Oliveira et al. [ 76 ] also used laboratory data and the S-curve technology life cycle to analyse welding processes. Lin et al.…”

Section: Literature Reviewmentioning

confidence: 99%

The technology life cycle of Persian lime. A patent based analysis

Hugo

Angie

Pilar

et al. 2022

Heliyon

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“…Low heat input results in inadequate melting time for enough materials, which causes a low volume weld pool with quick freezing as a result. This affects the dendritic structure and regulates the development of intermetallic layers [4]. It is proposed that a further decrease in weld heat input can be achieved by combining welding with a magnetic field for joining dissimilar metals [5].…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation and process parameter optimization in cold metal transfer welding for SS304L using response surface method

2023

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Cold Metal Transfer welding (CMT) was used to join SS304L plates by means of SS308L MIG wire. The effects of current, distance between nozzle and tip, welding speed on strength, hardness and surface roughness of welded zone in the CMT process by means of response surface methodology were studied in this work. The outcomes show that Current is having significant effect on the outputs. Also current interacting with weld speed is showing it’s mark on the output variables like tensile strength, hardness and surface roughness. Finally optimal results are found and the experimental values are in coincidence with the predicted values.

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A Detailed Forecast of the Technologies Based on Lifecycle Analysis of GMAW and CMT Welding Processes

Cited by 10 publications

References 39 publications

Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

The technology life cycle of Persian lime. A patent based analysis

Experimental investigation and process parameter optimization in cold metal transfer welding for SS304L using response surface method

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