Research On Key Technologies Of Tool Condition Monitoring And Prediction In Turning And Milling

Tool condition monitoring is one of the key technologies of the advanced manufacturing technology, key to enable uninterrupted production, realize automatic and unmanned machining process. It has great significance for ensuring machining efficiency and quality, protecting equipment, improving manufacturing standards and so on. Taking turning and milling tools as monitoring objects, cutting force and vibration signal as monitoring information, this paper takes some innovative studies on the key problems of tool condition monitoring field on the basis of discussing the review of developments in tool condition monitoring. The main research contents are as follows:(1) The wear feature, mechanism and influencing factor of cutting tool are briefly discussed. An indirect online monitor system based on cutting force and vibration signals was build, and a uniform experimental design method is used to arrange the experiments to collect tool condition monitoring data.(2) Research on feature extraction technology. First, using wavelet threshold denoising technology to eliminate high-frequency interference noise in monitoring signals, and then based on the statistical characteristics in time domain and frequency domain, wavelet packet analysis technology was studied, the wavelet packet energy and wavelet entropy features of cutting force and vibration signals were extracted. After getting the original feature set which reflect tool wear states from different perspectives, the correlation between tool wear and these eigenvalues was analyzed. Experimental results have shown:there is strong nonlinear relationship between tool wear and eigenvalues, tool wear condition can’t be got through a single feature.(3) Research on feature selection technology. There are many irrelevance and redundancy eigenvalues in original features set. It will be lead to sharp increase in learning samples and computational complexity of identification model if all the original features are used to monitor tool condition, which will reduce the speed and efficiency of monitoring system. Traditional feature selection algorithm of tool condition monitoring ignores the relationship of classifier, and need a large number of learning samples, is not suitable for feature selection under small samples. This paper proposed a tool condition monitoring feature selection technology based on one-versus-one support vector machine recursive feature elimination (SVM-RFE). The contribution of eigenvalues to classifier is used as the selection criteria to eliminate the irrelevance and redundancy features. Experiments show that the algorithm can effectively remove the irrelevant or redundant features, improve learning efficiency and identification accuracy of tool condition monitoring system.(4) Research on tool wear status assessment technology under incomplete prior knowledge. Aiming at lacking priori samples and difficult knowledge acquisition in the actual production process, which will make the applicability of monitoring system poor, a tool wear status assessment method based on factorial hidden markov model (FHMM) was proposed. First establish FHMM monitoring model using the priori knowledge of blunt and new tool. The performance indicators that can reflect tool wear state can be got from the similarity between monitoring sequence and trained FHMM. Meanwhile, self-improvement ability was introduced to the monitoring system so that the knowledge base of FHMM can continually improve in monitoring processes. The experimental results showed that:this method can achieve a preliminary estimate of tool wear using the priori knowledge of blunt and new tool, and this system has the ability of self-learning and self-improvement.(5) Research on tool wears recognition technology. There is a lack of the priori samples used to train recognition model, while the tool wear identification model based on traditional artificial neural network often requires a large number of training samples, and have some disadvantages such as running into local minimum value easily, slow convergence rate and so on. Against these issues, this paper proposed a tool wear recognition method based on least squares support vector machines (LS-S VM). Meanwhile, a self-adaptive weight particle swarm optimization (PSO) algorithm was proposed to search optimum value of the kernel function parameter and error penalty factor which affect the precision of the LS-SVM significantly. Experiments show that:PSO algorithm can efficiently search the optimal parameter of LS-SVM model under small sample; tool wear identification accuracy based on PSO-LS-SVM model is better than neural networks.(6) Research on optimization and prediction technology of tool condition monitoring results. By analyzing and testing results show that the system accuracy becomes poor because the interference signals existed in the intelligent condition monitoring results. Against this issue, this paper proposed an optimization technique for time sequence monitoring results based on kalman filter. According to the state space model of signal and noise, the present value of tool conditions can be estimated using the previous estimated value and the present observed value. There is certain regularity in the kalman filter optimized tool condition monitoring results. Based on history monitoring data, the future tool status can be predicted by certain prediction model, In this paper, a monitoring results prediction model based on autoregressive moving average (ARMA) method was proposed. Experimental results show:kalman filtering optimization algorithm can effectively eliminate interference noise in monitoring results, the relationship between status indicators and tool wear is stronger, the identification accuracy is higher, the ARMA method can effectively predict tool status.