In the current work, the optimal factors are selected to achieve the improvements in the energy consumption (EB), power factor (PB), decreased roughness (DR) and improved surface hardness (IH) for the roller burnishing operation. The process inputs are the burnishing speed (V), the feed (f), and the depth (d). A hybrid approach comprising the principal component analysis and Technique for Order of Preference by Similarity to Ideal Solution was used to explore the weight values of burnishing performances and select the optimum parameters. Moreover, another optimization technique employing the response surface method and archive-based micro-genetic algorithm was adopted to identify the optimal outcomes in the continuous domain. The main findings showed the performances measured are primarily affected by the burnishing feed, depth and speed, respectively. The energy consumption and roughness are approximately decreased by 31.46% and 7.41%, while the power factor and hardness are improved by 17.47% and 43.09%, respectively, as compared to the general process. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for burnishing applications in industrial practices.

在当前的工作中，选择最佳因素以实现滚筒磨光操作的能耗（EB），功率因数（PB），降低的粗糙度（DR）和改善的表面硬度（IH）的改善。过程输入为抛光速度（V），进给量（f）和深度（d）。一种混合方法，包括主成分分析和“与理想溶液相似的优先顺序技术”，用于研究抛光性能的权重值并选择最佳参数。此外，采用了另一种采用响应面法和基于存档的微遗传算法的优化技术来确定连续域中的最佳结果。主要发现表明，所测量的性能主要受抛光饲料，深度和速度的影响。与一般工艺相比，能耗和粗糙度大约分别降低了31.46％和7.41％，而功率因数和硬度分别提高了17.47％和43.09％。