Abstract
On-line tool condition monitoring is important to prevent workpieces and tools from damage, and to increase the effective machining time of a machine tool. It is necessary to define tool-life criteria clearly, for indirect methods of on-line tool condition monitoring. There are many tool life criteria that depend on wear manner, economic considerations, workpiece dimensional tolerance and surface roughness. However, the signal measured by a sensor (e.g. cutting force) usually represents the tool wear condition contributed from a different wear zone. This implies that it is difficult to extract a single wear criterion from a convoluted sensor signal. When multiple signal features are used, the response of the features to the tool life cannot be clearly seen, and the tool life prediction may not be reliable. This paper presents an investigation into tool life criteria in raw turning. A new tool-life criterion depending on a pattern-recognition technique is proposed. The neural network and wavelet techniques are used to realize the new criterion. The experimental results show that this criterion is applicable to tool condition monitoring in a wide range of cutting conditions.
Published Version
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