Intelligent cutting tool condition monitoring based on a hybrid pattern recognition architecture

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 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.