FUZZY NEURAL HYBRID SYSTEM FOR CUTTING TOOL CONDITION MONITORING
In manufacturing processes it is very important that the condition of the cutting tool, particularly the indications when it should be changed, can be monitored. Cutting tool condition monitoring is a very complex process and thus sensor fusion techniques and artificial intelligence signal processing algorithms are employed in this study. The multi-sensor signals reflect the tool condition comprehensively. A unique fuzzy neural hybrid pattern recognition algorithm has been developed. The weighted approaching degree can measure the difference of signal features accurately and the neurofuzzy network combines the transparent representation of fuzzy system with the learning ability of neural networks. The algorithm has strong modeling and noise suppression ability. These leads to successful tool wear classification under a range of machining conditions.
Sensor fusion feature extraction pattern recognition condition monitoring hybrid system
PAN FU A.D.HOPE
Mechanical Engineering Faculty, Southwest JiaoTong University, Chengdu 610031, China Systems Engineering Faculty, Southampton Institute, Southampton SO14 OYN, U.K
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
3026-3031
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）