Welding Quality Detection for Variable Groove Weldments Based on Infrared Sensor and Artificial Neural Network

Abstract

Connecting a variable groove weldment is always challenging, and it is necessary to monitor the course of the work and optimize the welding process parameters in real time to ensure the final welding forming quality. Welding penetration is an important index to appraise the welding forming quality; the visual sensing method for molten pool is the main method for detecting the weld penetration, but its detection accuracy is affected by the arc light. In this paper, a welding penetration sensing method for variable groove weldments based on the welding temperature field distribution is proposed. Firstly, a set of temperature field measurement system for a weldment is developed by means of an infrared sensor. Secondly, in the direction perpendicular to the welding direction, a linear temperature distribution feature extraction algorithm based on Gaussian fitting is studied; in the direction parallel to the welding direction, the linear temperature distribution feature extraction algorithm based on the thermal cycle parameters is studied, and the feasibility of using the extracted linear temperature distribution features to identify the weld penetration of a variable groove weldment is analyzed. Finally, taking the extracted linear temperature distribution features as input, using an artificial neural network, the prediction model for the welding penetration of a variable groove weldment is established. The experimental results showed that the weld penetration sensing method put forward in this paper can realize high-precision weld penetration sensing and has high reliability, which solves the problem that weld penetration sensing is affected by arc light to a great extent.