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This paper proposes a topology modification method of generating gear grinding based on multi-axis linkage parameter optimization. In order to improve the accuracy of tooth flank modification in generating gear grinding and to reduce the transmission error of modified gear pair, a multi-objective optimization genetic algorithm is adopted to adjust machine movement. Firstly, according to the position and motion relationship among diamond wheel, worm wheel, and workpiece gear, the mathematical models of worm wheel dressing and generating gear grinding are established. The linkage relationships between motion axes are redefined to achieve topology modification. The radial feed F X 1 , tangential feed F Y 1 of worm wheel, and gear rotational angle φ g are defined as the fourth-order polynomial function of axial feed F Z 1 . Secondly, the transmission error model of modified gear is developed. And the polynomial coefficients of each axis are optimized with the objective of minimizing total tooth flank deviation and transmission error. Finally, the effectiveness of this method is verified by three numerical examples with different parameter groups. In addition, the diamond wheel and worm wheel used in the study are standard profile, which saves the cost of gear machining and improves the efficiency of production cycle. The research results could provide a theoretical reference for the improvement of generating gear grinding process. [ABSTRACT FROM AUTHOR]

Modelling and optimisation are necessary for the control of any process to achieve improved product quality, high productivity and low cost. The grinding of silicon carbide is difficult because of its low fracture toughness, making it very sensitive to cracking. The efficient grinding of high performance ceramics involves the selection of operating parameters to maximise the MRR while maintaining the required surface finish and limiting surface damage. In the present work, experimental studies have been carried out to obtain optimum conditions for silicon carbide grinding. The effect of wheel grit size and grinding parameters such as wheel depth of cut and work feed rate on the surface roughness and damage are investigated. The significance of these parameters, on the surface roughness and the number of flaws, has been established using the analysis of variance. Mathematical models have also been developed for estimating the surface roughness and the number of flaws on the basis of experimental results. The optimisation of silicon carbide grinding has been carried out using genetic algorithms to obtain a maximum MRR with reference to surface finish and damage. [ABSTRACT FROM AUTHOR]