摘要
An intelligent fuzzy c-means system for process monitoring and recognition of process disturbances during short- circuiting gas metal arc welding （GMAW） is introduced in this paper. The raw measured and statistically test data of probability density distribution （ PDD ） and class frequency distribution （ CFD ） of welding electrical parameters are further processed into a 7-dimensional array which is designed to describe various welding conditions, and is employed as input vector of the intelligent fuzzy c-means system. The fuzzy c-means system is used to conduct process monitoring and automatic recognition. The correct recognition rate of 24 test data under 8 kinds of welding condition is 92%.
An intelligent fuzzy c-means system for process monitoring and recognition of process disturbances during short- circuiting gas metal arc welding （GMAW） is introduced in this paper. The raw measured and statistically test data of probability density distribution （ PDD ） and class frequency distribution （ CFD ） of welding electrical parameters are further processed into a 7-dimensional array which is designed to describe various welding conditions, and is employed as input vector of the intelligent fuzzy c-means system. The fuzzy c-means system is used to conduct process monitoring and automatic recognition. The correct recognition rate of 24 test data under 8 kinds of welding condition is 92%.
基金
The authors are grateful to the financial support provided by the National Natural Science Foundation of China under grant No. 51005106, Research Fund for the Doctoral Program of Jiangsu Uni- versity of Science and Technology under grant No. 35060902, A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
