2019
DOI: 10.1051/matecconf/201926904004
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Estimation of Contact Tip to Work Distance (CTWD) using Artificial Neural Network (ANN) in GMAW

Abstract: A method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parame… Show more

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Cited by 3 publications
(2 citation statements)
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References 9 publications
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“…An artificial neutral network (ANN) is used in [11] to detect the CTWD in GMAW. Specifically, the travel speed, thickness of specimen, feeding speed, wire electrode diameter, the current, and voltage filtered by a moving mean filter as well as the root mean square of the current and voltage are passed to the ANN.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…An artificial neutral network (ANN) is used in [11] to detect the CTWD in GMAW. Specifically, the travel speed, thickness of specimen, feeding speed, wire electrode diameter, the current, and voltage filtered by a moving mean filter as well as the root mean square of the current and voltage are passed to the ANN.…”
Section: Introductionmentioning
confidence: 99%
“…The ANN is reported to detect changes in the CTWD within 0.147 s. A statement about the accuracy of the CTWD cannot be made, as the defuzzification step is missing. As the thickness of the specimen plays an important role during the evaluation, it is not clear, if this method is applicable for WAAM [11].…”
Section: Introductionmentioning
confidence: 99%