Comparison of Three Control Methods in Penetration Control of Pulsed Gas Tungsten Arc Welding

An artificial neural network model for backside bead width was established and three control meth-ods——PID. fuzzy and neuron were designed, simulated and tested. The test results of bead-on-plate weld of GTAW indicate that the artificial neural network (ANN) modeling and learning control method have more advan-tages than the conventional method. They show that the ANN modeling and learning control method is an effective approach to real time control of welding dynamics and ideal quality.     

Current Density Distribution in Gas Tungsten Arc Welding Process

IntroductionWeldingisacriticaltechnologyinbuildingnuclearreactors,shins,piPelinesandinatomobilemanufaCtuing.BatuPtil1noWtheearemanyproblemsinPractice,understandingthedeVlopmentoftheweldpoolduringweldingisofconsiderablePracticalsignificance.BecauseofthecomPlexityoftheprocessandtl1ePresenceofthebrightarc,directexPerimentalinvestigationsareextremelyexPensiveandoftenimPossibleorimPracticable,thensomemathematicalmethodsareadoptedtosett1PthetfansiellttemPerattirefieldandthefluidflowfieldofthew…     

A Generalized Rough Set Modeling Method for Welding Process

Modeling is essential, significant and difficult for the quality and shaping control of arc welding process. A generalized rough set based modeling method was brought forward and a dynamic predictive model for pulsed gas tungsten arc welding (GTAW) was obtained by this modeling method. The results show that this modeling method can well acquire knowledge in welding and satisfy the real life application. In addition, the results of comparison between classic rough set model and back-propagation neural network model respectively are also satisfying.