Study On Cutting Tool Wear Condition Monitoring Based On The Artificial Bee Colony Optimized Neural Network

Machinery manufacturing industry directly reflects the level of technology and economic of a country. It’s also the platform for new technology development and new product production of many other industries. And it plays an important role in national economic development. CNC tools are involved in manufacturing of machinery directly, and its operational status is essential for precision and continuity of machining. So the tool condition monitoring technology is the key to develop modern manufacturing technology.In this paper, tool wear condition monitoring use tool vibration and cutting force signals as the monitoring signal which based on advanced multi-sensor fusion technology. Collecting vibration and cutting force signals of cut tools through new knife to severe wear by continuous cutting and repeated tests, which is used for studying tool condition monitoring techniques.For the signals collected in the field will contain noise signal which come from interference. This paper introduces matching pursuit algorithm, we can use it to make the adaptive decomposition and reconstruction of original signals, which can realize the purpose of filtering and improve the signal to noise ratio; Using analysis methods including time domain, frequency domain and time-frequency domain which based on wavelet packet can extract the parameters of the monitoring signals. For the problem of input data sample which has big volume and high dimensionality, Kernel Principal Component Analysis (KPCA) method can transform multiple features into several intergrated characteristics with less information lost and uses few characteristic parameters to represent the major information and achieve dimensionality reduction of characteristic parameters.Artificial bee colony algorithm is introduced to optimize BP neural network performance for the BP neural network during training having problems like slow convergence speed, falling into local minimum easily, and even leading to the network can not complete the training and other issues. The algorithm introduced the concept of population, as well as the mechanism of evolution algorithm population, which greatly enhance the performance of BP neural network.The data of the research shows that optimized BP neural network has overcome the defect of original network for always easily falling into local minimum and improves the accuracy of tool condition monitoring. All of this is practical and significant to develop cutting tools condition monitoring techniques.