Welding process parameters are indispensable to program are welding robot. To simplify off-line programming (OLP) for robotic are welding, we develop an are welding expert system which can generate welding process parameters automatically. Its input data come from the feature database of welding part, which is set up by our feature modeling system The expert system has become an important module of our RAWTOLPS (Robotic Are Welding Task-level Off-Line System). It combines case-based reasoning with heuristic rule-based reasoning methods to deal with the welding process design. Moreover, artificial neural networks are introduced to the systems for reasoning and machine learning, and several network modules are developed to learn from welding process database, based on backpropagation neural networks. After some groups of actual welding process data were used to train the network models, several network models are established both to design the welding process and to predict the weld bead shape. Besides the ANN-based learning, cased-based learning are used in the expert system. These two methods have respectively their own characteristics, and can meet qualifications of different users. The experimental data show that the system can accomplish re-learning and expanding of welding process knowledge, and satisfy the command of the off-line programming system.
