نوع مقاله : مقاله پژوهشی
1 گروه مهندسی مکانیک، دانشگاه آزاد اسلامی، واحد گلپایگان
2 دانشکده مهندسی مکانیک، دانشگاه صنعتی اصفهان
3 دانشکدهی مهندسی مواد، دانشگاه صنعتی اصفهان
عنوان مقاله [English]
Grinding is a surface finishing process, and surface roughness is one of the most important factors in evaluating the performance of the finished component. The development of a comprehensive model that can predict surface roughness over a wide range of operating conditions is still a key issue for the grinding process. In this paper, a new predictive surface roughness model for the surface grinding process is developed, based on maximum undeformed chip thickness modeling. By considering the random nature of grit distribution and grit geometry and, thus, variations in the depth of grain penetration, the concept of a probability density function (PDF) has been utilized. Gamma PDF has been determined to be the best function, by comparing the main distribution functions in a histogram graph of chip thickness, and, based on this PDF, maximum undeformed chip thickness modeling has been carried out. The representation of chip thickness in the proposed model is a function of grinding parameters, the wheel microstructure and process kinematic conditions. The developed model for surface roughness prediction is based on the geometrical analysis of the grooves left on the surface due to the grit-workpiece interaction, and has been resulted using maximum undeformed chip thickness modeling. The surface roughness model has been validated by the experimental results of the surface grinding of a thermally sprayed WC-10Co-4Cr coating. Reasonable agreement has been observed between predictions from the proposed model and the experimentally measured surface roughness. This is also supported by the values of the average percentage of error between predicted and experimental results. The average value of relative error between predicted and measured values of surface roughness was 8.53\%. According to these results, it can be concluded that the proposed surface roughness model is an effective prediction technique.