WELDING TECHNOLOGY

THIS PROJECT IS DEVELOPING METHODS TO USE ARTIFICIAL NEURAL NETWORKS FOR MODELING AND CONTROL OF ARC-WELDING PROCESSES. ARTIFICIAL NEURAL NETWORKS WILL BE COMPARED WITH MORE TRADITIONAL MODELS, SUCH AS THOSE DERIVED FROM THE PHYSICS OF HEAT CONDUCTION, AND TECHNIQUES FOR COMBINING THE USE OF BOTH IN MODELING AND CONTROL WILL BE EXPLORED. ARTIFICIAL NEURAL NETWORKS ARE ADVANTAGEOUS IN THAT ESSENTIALLY NO EXPLICIT MODEL DERIVATION OF THE PROCESS IS NECESSARY. RATHER, NEURAL NETWORKS ARE TRAINED WITH REAL WELDING DATA FROM THE PROCESS TO BE MODELED, AND THEY ADAPT THEMSELVES AUTONOMOUSLY TO "LEARN" OR CAPTURE THE GENERAL PROCESS CHARACTERISTICS. IT HAS BEEN SHOWN THAT NEURAL-NETWORK MODELS ARE GENERALLY NUMERICALLY MORE ACCURATE AND USUALLY FASTER IN EXECUTION THAN MOST OF THEIR TRADITIONAL, PHYSICALLY BASED COUNTERPARTS. TYPICAL ACCURACY IN PREDICTING GEOMETRICAL FEATURES OF WELDS IS ON THE ORDER OF 10 PERCENT OR BETTER. PHYSICALLY BASED MODELS, ON THE OTHERHAND, PROVIDE INTUITIVE INSIGHT INTO THE PROCESSES, AND THEYWILL BE USED HERE FOR GUIDING AND CHECKING THE OVERALL VALIDITY OF THE NEURAL-NETWORK SOLUTIONS. THIS PROJECT IS DEVELOPING METHODS TO USE ARTIFICIAL NEURAL NETWORKS FOR MODELING AND CONTROL OF ARC-WELDING PROCESSES. ARTIFICIAL NEURAL NETWORKS WILL BE COMPARED WITH MORE TRADITIONAL MODELS, SUCH AS THOSE DERIVED FROM THE PHYSICS OF HEAT CONDUCTION, AND TECHNIQUES FOR COMBINING THE USE OF BOTH IN MODELING AND CONTROL WILL BE EXPLORED. ARTIFICIAL NEURAL NETWORKS ARE ADVANTAGEOUS IN THAT ESSENTIALLY NO EXPLICIT MODEL DERIVATION OF THE PROCESS IS NECESSARY. RATHER, NEURAL NETWORKS ARE TRAINED WITH REAL WELDING DATA FROM THE PROCESS TO BE MODELED, AND THEY ADAPT THEMSELVES AUTONOMOUSLY TO "LEARN" OR CAPTURE THE GENERAL PROCESS CHARACTERISTICS. IT HAS BEEN SHOWN THAT NEURAL-NETWORK MODELS ARE GENERALLY NUMERICALLY MORE ACCURATE AND USUALLY FASTER IN EXECUTION THAN MOST OF THEIR TRADITIONAL, PHYSICALLY BASED COUNTERPARTS. TYPICAL ACCURACY IN PREDICTING GEOMETRICAL FEATURES OF WELDS IS ON THE ORDER OF 10 PERCENT OR BETTER. PHYSICALLY BASED MODELS, ON THE OTHERHAND, PROVIDE INTUITIVE INSIGHT INTO THE PROCESSES, AND THEYWILL BE USED HERE FOR GUIDING AND CHECKING THE OVERALL VALIDITY OF THE NEURAL-NETWORK SOLUTIONS.