Feed based trajectory addition and elimination algorithm combined with stochastic meshless modelling to simulate roughness in surface grinding

Abstract

Surface grinding is observed to be an inevitable machining event in many of the functional engineering surfaces after pre-processing stages such as milling. Prediction of roughness profile after grinding on such initial surfaces, for selected operating conditions and grinding wheel specification, is always an interesting topic of investigation. Though a number of models have been reported in prior art, value additions through hybrid modelling strategies have been always contributing positively to this field of research due to the stochastic nature of abrasive interactions. Since feed rate of work piece is identified as a key grinding variable influencing the surface roughness, present paper introduces grain trajectory addition and elimination algorithm driven by feed based time scale to predict the ground surface topography. A realistic initial surface modelling is considered using meshless moving least square approximation incorporating both roughness as well as geometric deviation, expected to be imparted during pre-grinding stages. The algorithm is configured to compute two dimensional roughness parameters from the simulated surface profiles and these results are compared with experimental outcomes at various depths of grinding to validate the proposed model.