Vibration signal acquisition and extraction for cutter wear monitoring

Abstract

The cutter wear state is key in manufacturing. During the machining, the vibration signal of the tool spindle will change with the increase of cutter wear, which provides an effective method for cutter wear monitoring. However, due to the popularity of machining centers, the machining trajectory of a cutter is extremely complex in practical production. The complexity includes a single process containing multiple work steps, a step containing random and long non-cutting time, etc., making it difficult to acquire and extract the specific step signal as the effective signal for cutter wear monitoring. Therefore, this paper proposes a system that can synchronously acquire machine parameters and tool spindle vibration signals. Then, two algorithms—the cutting signal recognition algorithm and the step signal similarity detection algorithm—are proposed. The former algorithm is based on sliding window energy, which can accurately identify the instant the cutter begins to cut the workpiece. The step signal similarity detection algorithm based on the Edit Distance on Real (EDR) sequence ensures the correctness of the step extraction. The experimental results show that the system has efficient acquisition of vibration signals and correct step signal extraction, providing a data basis for cutter wear monitoring.