Experimental study of tool wear monitoring based on neural networks

The influence of experimental design on modeling of tool condition monitoring system based on different neural networks was investigated. The orthogonal experiments and complete parameter combination experiments were carried out on a Vertical Machining Centre, and BPNN and CSGFFNN were adopted to model the mapping relations between tool condition and features extracted from different sensor signals by using experimental data. The research results show the orthogonal experiments canpsilat meet the need of modeling of TCMS based on different NN and the classifying error is high above 87%, complete parameter combination experiments can provide enough data for modeling of NN for TCMS and realize reliable monitoring of tool condition or tool wear.