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During the unit event of material interaction 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 phenomenon 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 as the most sensitive monitoring process to investigate such miniscule material interactions. For this reason, two AE sensors were used to pick up energy signatures (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. Previous work has only partially displayed the link in terms of micro and macro phenomena (unit event to normal MG events). In the work presented here, the micro unit grit event will be linked to the macro phenomena such as normal grinding conditions extended to aggressive conditions—burn. This is significant to any safety critical manufacturing environment due to the fact that burn provides surfaces that cannot be accepted when scrutinised under quality considerations and therefore plays an integral part into abrasive machining process. This paper also looks at transparent classification (CART) to give regression capabilities in displaying the micro to macro phenomena in terms of signal intensities and frequency representation. The demarcation between each of the phenomena was identified from acoustic emission signals being converted to the frequency–time domains using short-time Fourier transforms.

the international journal of advanced manufacturing technology

Traceability of acoustic emission measurements for micro and macro grinding phenomena—characteristics and identification through classification of micro mechanics with regression to burn using signal analysis