Boring chatter identification by multi-sensor feature fusion and manifold learning

Abstract

In the boring process, chatter is easy to occur because of the large overhang of the boring bar and the poor structural stiffness of the system. The key technique to reduce the chatter effect in boring process is to identify the chatter state accurately. In this paper, a method of chatter identification based on multi-sensor feature fusion and manifold learning is proposed. Displacement sensor, acceleration sensor, and acoustic pressure sensor are used to acquire processing signals synchronously, and then triple signals are decomposed by empirical mode decomposition (EMD). The multi-indicators are used to construct high-dimensional space, and then different manifold learning algorithms are used to reduce feature dimensionality. Support vector machine chatter identification models are obtained to verify the effect of boring chatter identification. Multi-sensor feature fusion realizes the complementary of different features and achieves better recognition results. The results show that the proposed method can identify boring chatter effectively. And the best result is obtained by the combination of the displacement sensor and acceleration sensor.