A study on tool condition monitoring and diagnosis of micro-grinding process based on feature extraction from force data

In this article, a new algorithm for diagnosing tool conditions in micro-scale grinding process is proposed using features extracted from measured tangential grinding force data with the aid of wavelet packet decomposition and back-propagation neural network methods. The tangential grinding forces are measured in a series of micro-grinding experiments by varying depth of cut and feed rate, and those measured profiles are analyzed to define the tool conditions - sharp, middle and dull. From each tangential grinding force signal, 32 node energies are extracted by applying a wavelet packet decomposition method, and a total of 34 features including 32 node energies, depth of cut and feed rate are used to build the micro-grinding tool condition diagnosis model based on a back-propagation neural network approach. In this model, the grinding tool condition can be represented as a numerical confidence value. The experimental verification is conducted and it is demonstrated that the developed model is applicable for effectively diagnosing the micro-grinding tool conditions.