A Smart System to Determine and Control for the Process Parameters in Pipeline Welding
Determination of the optimal welding parameters to achieve specific weldments on a new material is usually an expensive and time consuming. To determine the welding parameters using Artificial Intelligence (AI) technologies, one must consider many factors including productivity, thermal input, defect formation, and process robustness. Determination of the welding parameters for pipeline welding is based on a skilled welders long-term experience rather than on a theoretical and analytical technique. In this paper, a smart system develops which determines welding parameters and position for each weld pass in pipeline welding based on one database and FEM model, two BP neural network models and a C-NN model. The preliminary test of the system has indicated that the system could determine the welding parameters for pipeline welding quickly, from which good weldments can be produced without experienced welding personnel. Experiments using the predicted welding parameters from the developed system proved the feasibility of interface standards and intelligent control technology to increase productivity, improve quality, and reduce the cost of system integration.
A. Kiet Tieu, Hongtao Zhu and Qiang Zhu
Y. J. Jeong et al., "A Smart System to Determine and Control for the Process Parameters in Pipeline Welding", Materials Science Forum, Vols. 773-774, pp. 750-758, 2014