摘要：A method for optimizing monitoring by usingArtificial Neural Network(ANN) technique was proposed based on instability of arc voltage signal and welding current signal ofsolid 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 intime domainby carrying out theMoving Average(M.A) andRoot Mean Square(RMS) based on the welding experiment parameters such astravel speed, thickness of specimen, feeding speed, and wire electrode diameterto detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets:train (70%),validation (15%), andtest (15%).Error back-propagation of Levenberg-Marquardt algorithmwas used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety theContact Tip to Work Distance(CTWD) throughMean Square Error(MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE0.0087).
