Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation.

The article presents a new methodology for modelling the influence of parameters and conditions of surface grinding process on the value of roughness and grinding forces. Grinding processes are characterised by numerous factors influencing results of the process with a complex mechanism of the cumulative effects of their interactions. Therefore, authors for the development of the model used an artificial neural network. The input parameters of the model, apart from the processing parameters, were the properties of the workpiece and features of the grinding wheel. In the selection process of the neural model structure, an evaluation criterion was proposed which included the character of the influence of processing parameters on the resultant values of the grinding process for the given pair workpiece-tool. A high ability to data generalisation by the developed neural model was demonstrated. [ABSTRACT FROM AUTHOR]