Real-time fuzzy-clustering and CART rules classification of the characteristics of emitted acoustic emission during horizontal single-grit scratch tests

Abstract

During the unit event of material iteraction in grinding three phenomena are involved, namely: rubbing, ploughing and cutting. Where ploughing and rubbing essentially mean the energy is being applied less efficiently in terms of material removal. Such phenomena usually occurs before or after cutting. Based on this distinction, it is important to identify the effects of these different phenomena experienced during grinding. Acoustic emission (AE) of the material grit interaction is considered the most sensitive monitoring process to investigate such miniscule material change. For this reason, two AE sensors were used to pick up energy information (one verifying the other) correlated to material measurements of the horizontal scratch groove profiles. Such material measurements would display both the material plastic deformation and material removal mechanisms. Accurate material surface profile measurements of the cut groove were made using the Fogale Photomap Profiler which enables the comparison between the corresponding AE signal scratch data. By using short-time Fourier transforms (STFT) and filtration, the salient features for identifying and classifying the phenomena were more distinct between the three different levels of single-grit (SG) phenomena. Given such close data segregation between the phenomenon data sets, fuzzy clustering/genetic algorithm (GA) classification techniques were used to classify and verify the demarcation of SG phenomena. After the cutting, ploughing and rubbing gave a high confidence in terms of classification accuracy, the results from the unit/micro-event to the multi/macro-event, both 1-μm and 0.1-mm grinding test data, were applied to the named classifier for classification. Interesting output results correlated for the classifier signifying a distinction that there is more cutting utilisation than both ploughing and rubbing as the interaction between grit and workpiece become more involved (measured depth of cut increases). With the said classifier technique it is possible to get a percentage utilisation of the grit and material interaction phenomena. In addition, optimised fuzzy clustering was verified against a classification and regression tree (CART) rule-based system giving transparent rule classification. Such findings were then realised into a Simulink model as a potential control system for a micro-grinding simulation or, for real-time industrial control purposes.