Context sensitive recognition of abrupt changes in cutting process

摘要

This paper presents a new generic approach to real-time monitoring of abrupt changes in cutting process. Proposed method is based on hierarchical fuzzy clustering of patterns obtained from discrete wavelet transform (DWT) of acquired signals correlated with cutting force variation in time. Cutting process is naturally highly dynamical and normally consists of mixture of various dynamic phenomena related to the chip formation process and dynamical responses of machining system, workpiece and tool itself. These phenomena are characterized by different time duration. The class of phenomena related to abrupt changes during short time interval is of special importance since they correspond to the most dramatic changes in cutting process, such as various kinds of tool failure or workpiece damage or even breakage. Due to their short time duration, discovery and recognition of these phenomena is extremely difficult. To solve given problem we have chosen DWT, fuzzy clustering and finite state automata as a formal platform for its analysis. Beside its good time localization properties, DWT is, due to asymmetric and irregular shapes of wavelets, especially suitable for analysis of signals having sharp changes or even discontinuities. Given properties make DWT an efficient means for extraction of representative and reliable information contents, thus making good basis for extraction of discriminative and representative features (as DWT coefficients combinations) for classification that will follow. Robustness of specific pattern recognition and learning may be achieved only by taking into consideration wider context. Therefore, in tool condition pattern recognition we have considered the entire context of changes in cutting process state space that precedes and appears after the phenomenon which should be recognized. The cutting process behavior and its evolution in time are considered rather then momentary state which is represented as a point in adopted feature hyperspace of classification machine. Efficiency and practical applicability of developed method is evaluated by extensive experiments in laboratory conditions.