A quality monitoring system in butt welding process is proposed to estimate weld pool sizes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to assess the integrity of the weld quality. The monitoring variables used are the surface temperatures measured at three points on the top surface of the weldment which are strongly related to the formation of the weld pool. The surface temperatures are measured using IR temperature sensing system. The temperature profile is assumed that it has a gaussian distribution in vertical direction of torch movement and verify this assumption through temperature analysis.
A neural network estimator is designed to estimate weld pool size from temperature informations. Butt welding experments were performed to assess the performance of the neural network estimator. The experimental results show that the proposed neural network estimator which used gaussian distribution as temperature information can estimate the weld pool sizes accurately than used three point temperatures as temperature information. Considering the change of gap size in butt welding, the experiment were performed on various gap size. And using fuzzy logic neural network estimator is desigined. This fuzzy multilayer perceptron show an improved estimation performance.