| Summary: | 博士 === 國立交通大學 === 機械工程系所 === 95 === Many parameters affect the automatic welding quality. In practice, the desired welding parameters are usually determined based on experience or handbook values. It does not insure that the selected welding parameters result in optimal or near optimal welding quality characteristics for that particular welding system and environmental conditions. To solve such problems, engineers conventionally apply the Taguchi method. However, the Taguchi method has some limitations in practice. Many benefits can arise from using the Taguchi method for neural network design. A proposed approach that combine the Taguchi method and a neural network to determine optimal welding conditions for improving the effectiveness of the optimization of parameter design is presented. The proposed approach includes two phases. Phase 1 executes initial optimization via Taguchi method to construct a database for the neural network. Phase 2 applies a neural network with the Levenberg-Marquardt back-propagation (LMBP) algorithm to search for the optimal parameter combination. Three examples involving the gas tungsten arc (GTA) welding, the pulsed Nd:YAG laser micro-weld process, and the resistance spot welding (RSW) process in automotive industry demonstrate the effectiveness of the proposed approach. The experimental results show that the proposed procedures excel the Taguchi method in this dissertation. It has demonstrated the practicability of the proposed procedures.
|
|---|
