Abstract:

For the problem that the K-means algorithm requires manual determination of the cluster numbers and random selection of initial clustering centers, which can fall into local optima, an improved parameter-free K-means algorithm is proposed by combining the density peak-based clustering algorithm CFSFDP(Clustering by Fast Search and Find of Density Peaks). First, the local density and dispersion of the sample points are calculated, then a decision diagram is established, and a vector of two parameters is composed. The distance from each point to the surrounding 5 points is calculated, and those with a distance greater than 2 times the mean square error and a density greater than the average density are filtered out. The filtered point is used as the initial clustering center of the algorithm. The number of statistical clustering centers k is used as the number of clusters, and the initial number of clusters k and the initial clustering centers are used as the initial parameters of the K-means algorithm to cluster data. The algorithm is tested on different types of data sets, including artificially created Gaussian data sets, UCI(University of California, Irvine) data sets, and real tool vibration data sets. The results show that the proposed algorithm maintains the global optimality of the traditional algorithm and validates its effectiveness. Since K-means is an unsupervised clustering method, it can reduce the workload and computational cost of manual data calibration, supervised training, etc. , while obtaining better tool state recognition results, which is of high practical significance for accurate real-time extraction of the operating state of the tool for computerized numerical control machine tools.

Key words: K-means clustering algorithm, nonparametric, numerical control machine, tool wear identification