EXPERT SYSTEM TRAINING TECHNIQUE TO EVALUATE WELDER’S JOB STABILITY - Vitaliy Fedorovich Lukianov; Igor Vladimirovich Kuzmenko (Advanced Engineering Research)
The design and training scheme for the artificial neural network is considered. An expert system of evaluating a craftsman’s motor skills stability while working on the welder simulator is based on this technique. It is assumed that the weld joint quality depends directly on the welding behavior stability. While the stability of the manual arc and mechanized welding depends on the welder’s motor skills. It is proposed to use an expert system to determine the stability criterion of the welding process. A step by step design of the artificial neural network architecture considering the specific weld formation is described. It is shown that the application of artificial neural networks based on the expert system allows evaluating the welder’s job stability. A training technique which shortens the time and reduces the number of experiments without loss of the data adequacy and the expert system training quality is described. When creating a database, the experimental results presented as "Quality domain" that connects the welder’s motor actions and the fillet joints defects are used.