Monitoring of Grinding Burn by Acoustic Emission

Abstract

The grinding process is of high dimensional accuracy and provides a high degree of finish. Damages on the workpiece are of high cost, since all previous process besides the grinding itself is lost. In the case of metals, the most common cause of damage is excessive thermal input on the ground surface. One of the critical problems in implementing an intelligent grinding process is the automatic detection of workpiece surface burn. This work aimed to develop two models using fuzzy logic to predict the various levels of burning surface of the workpiece in the grinding process. Most engineering applications of fuzzy logic belong to “Linguistic Mathematics”. It deals with engineering problem where variables cannot be assigned crisp numeric values or can be assigned context-dependent linguistic values. This is exactly applicable to a grinding process. For example, in the context of grinding, grain size cannot be specified by, say, diameter in micron. In practice, words like “coarse”, “medium”, and “fine” are found very vague but yet very expressive of the main characteristics. The workpieces for the grinding tests consisted of SAE 1020 laminated steel bars with dimensions of 150mm length, 10mm width and 60mm height. The grinding was performed along the length of the workpiece. The samples were inspected visually registering any change of color of the ground surface. Based on acoustic emission signals, cutting power, and the mean-value deviance (MVD), linguistic rules were established for the various burn situations (slight, intermediate, severe) by applying fuzzy logic using the Matlab Toolbox. Two practical fuzzy system models were developed. The first model with two inputs (root mean square of acoustic emission signal and the power signal from the electric motor that drives the wheel) resulted in a simple analysis process. The second model has an additional statistic input (MVD), associating information and precision. The two models differ by the number of association rules and the MVD input in the second model. The two developed models presented valid responses, proving effective, accurate, reliable and easy to use for the determination of ground workpiece burn. In this analysis, fuzzy logic translates the operator’s human experience associated with powerful computational methods. The models may be attractive to the practicing engineer who would like to get quick answers for on-line intelligent control and/or optimization. In its current state, the models …