目的：获得声波辅助剪切增稠抛光方法抛光轴承钢圆柱表面的最佳工艺参数。方法应用田口法，对声波辅助剪切增稠抛光过程中影响工件材料去除率，以及表面粗糙度的声波频率、声波功率、声波波形等参数进行实验与优化分析，以材料去除率、表面粗糙度为评价条件，得到最优抛光参数，并在最优参数条件下做多组重复性实验以验证结果的可靠性。利用金相显微镜、光学轮廓仪等测试手段对加工后的工件进行表面形貌检测。结果以材料去除率为评价指标，声波频率影响最为显著，声波功率影响次之，声波波形影响最小；以表面粗糙度为评价指标时，声波波形影响最为显著，声波频率影响次之，声波功率影响最小。结论在声波频率为20 Hz、声波功率为25 W、正弦波形条件下，工件材料去除率最高，材料去除率达到了11.32μm/h；在声波频率为60 Hz、声波功率25 W、正弦波形条件下，工件表面质量最佳，抛光1 h后工件平均表面粗糙度Ra由100 nm下降至7 nm以内，最低达到了4.48 nm。
Objective To obtain the optimal parameters in the acoustic assisted shear thickening polishing ( ASTP) process of bearing steelcylindrical surface. Methods The Taguchi method was used to analyze and optimize the parameters such as acoustic frequency, acoustic power and acoustic waveform which would influence the material removed rate ( MRR) and the surface rough-ness ( Ra) . The optimized parameters were gained taking MRR and Ra as the evaluation indexes, and multi-group experiments were carried out to verify the reliability of the results under the optimized ASTP conditions and the surface morphology of the processed workpieces was investigated by the test methods including metallurgical microscopy and optical profilometry. Results When the MRR was used as the evaluation index, acoustic frequency was the most significant influencing factor, followed by acoustic power and then acoustic waveform. While Ra was used as the index, acoustic waveform was the most significant influencing factor, followed by acous-tic frequency and acoustic power. Conclusion Based on the analysis results, MRR reached the highest value of 11. 32 μm/h under the conditions of 20 Hz acoustic frequency, 25 W acoustic power and sine wave acoustic waveform. The surface quality of the work-piece was the best under the conditions of 60 Hz acoustic frequency, 25 W acoustic power and sine wave acoustic waveform, and the surface roughness Ra was reduced rapidly from 100 nm to 4. 48 nm after 1 hour processing and the lowest roughness was 4. 48 nm.
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