원문정보
초록
영어
The successful application of short electric arc machining (SEAM) technology can solve the long-standing technical problem of hard-facing materials processing which machinery manufacturing industry generally faces. In the article, we trained high power supply neural network model by using the simulation data. And on this basis, we optimize parameters of power supply combined with genetic algorithm based on objective weighting method to guide parameters changes to meet the requirements. The results, by analyzing startup test and load-mutation test, show that the power supply have the advantages of stable output voltage and fast response speed, which meet the expectant targets and machining requirements. At last, through cutting experiment of SEAM on nickel-based superalloy, the power supply of SEAM designed by this new method is verified that its electric properties meet processing requirements of SEAM.
목차
1. Introduction
2. Simulation Model of Power Supply Based on Matlab Software
3. Power Supply’s Mapping Relationship Building of BP Neural Network
3.1. Learning Samples Design of BP Neural Network
3.2. Structure and Algorithm of BP Neural Network
3.3. BP Neural Network Training
4. Genetic Algorithm Based on Objective Weighting Method
5. Simulation and Test
5.1. Startup Test
5.2. Load Mutation Test
6. Processing Experiment of SEAM Based on Nickel-Base Superalloy
7. Conclusion
Acknowledgements
Reference