ABSTRACT In the present research work, a predictive model of material removal rate (MRR) for a novel ultrasonic turning (UST) process was developed and analyzed. Experimentation was performed to examine the effect of UST process variables viz. workpiece rotation speed, tool diameter, abrasive size, concentration and power rating on MRR, tool wear rate (TWR), and surface roughness (SR) while turning of glass rod. The experimentation was planned and executed as per Taguchi’s L27 orthogonal array. In addition, the UST process parameters were optimized to obtain higher MRR and lower TWR and SR using Taguchi grey relational analysis. The ANOVA results showed that tool diameter was the most significant parameter, whereas power rating was the least significant parameter affecting the UST process. The optimum parametric combination obtained by Taguchi grey relational analysis exhibited in an overall enhancement of 36% in UST process performance. Both the predicted and experimental results showed an acceptable agreement. Further, the statistical analysis revealed that the developed model has a R-value of 0.9984 and MAPE of 1.25%. Using this model, the MRR in UST process can be estimated for other brittle materials also.

中文翻译：

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超声车削加工材料去除率模型的建立及性能评价

摘要 在目前的研究工作中，开发并分析了一种用于新型超声车削 (UST) 工艺的材料去除率 (MRR) 预测模型。进行了实验以检查 UST 过程变量的影响，即。玻璃棒车削时的工件转速、刀具直径、磨料尺寸、MRR 的浓度和额定功率、刀具磨损率 (TWR) 和表面粗糙度 (SR)。根据田口的 L27 正交阵列计划和执行实验。此外，使用田口灰色关联分析优化 UST 工艺参数以获得更高的 MRR 和更低的 TWR 和 SR。方差分析结果表明，工具直径是最重要的参数，而额定功率是影响 UST 过程的最不重要的参数。通过田口灰色关联分析获得的最佳参数组合在 UST 工艺性能方面总体提高了 36%。预测结果和实验结果均显示出可接受的一致性。此外，统计分析显示，所开发模型的 R 值为 0.9984，MAPE 为 1.25%。使用该模型，也可以估计其他脆性材料在 UST 过程中的 MRR。