The Taguchi method (TM) and TM-combined methods [e.g., TM fuzzy method (TM-Fuzzy), TM data envelopment analysis (TM-DEA), TM coupled with grey relational analysis (TM-GRA), etc.] have been proven to be effective to achieve robust design performance. However, the efficient strategy to identify the significant design parameters, and the link between different quality characteristics and design parameters have not been fully studied. To fill the gaps, a combined method, i.e., TM–response surface methodology (RSM)–desirability function (DF), (TM–RSM–DF), was proposed. The significance of the TM–RSM–DF method is able to address the relationship between design parameters and quality characteristics, which facilitated to sort out the most significant design parameters precisely; besides, it also provided a robust strategy to optimize the multiple quality characteristics of the precision product. A design process of a precision amplification (PA) was taken as an example to prove the effectiveness of the TM–RSM–DF. The results showed that the TM–RSM–DF method improved the estimation performance of displacement amplification ratio (DAR) and natural frequency (NF) by 6.148% and 0.537% compared with the initial desired design. Besides, compared with others, the TM–RSM–DF method reduced the stress amount about 16% and had the lowest DAR and NF errors of 2.882% and 1.305%, respectively. Overall, the proposed TM–RSM–DF method outperformed the TM-DEA, TM-GRA, and TM-Fuzzy in the robustness of the PA design.

田口方法（TM）和TM组合方法[例如，TM模糊方法（TM-Fuzzy），TM数据包络分析（TM-DEA），TM与灰色关联分析（TM-GRA）等]被证明可有效实现强大的设计性能。但是，尚未有效研究识别重要设计参数的有效策略，以及不同质量特征和设计参数之间的联系。为了弥补这一空白，提出了一种组合方法，即TM-响应面方法（RSM）-合意函数（DF），（TM-RSM-DF）。TM–RSM–DF方法的重要性在于能够解决设计参数与质量特性之间的关系，从而有助于精确地挑选出最重要的设计参数。除了，它还提供了一种鲁棒的策略来优化精密产品的多种质量特征。以精密放大（PA）的设计过程为例，以证明TM–RSM–DF的有效性。结果表明，与初始设计相比，TM–RSM–DF方法将位移放大率（DAR）和固有频率（NF）的估计性能提高了6.148％和0.537％。此外，与其他方法相比，TM–RSM–DF方法减少了约16％的应力，并且DAR和NF误差最低，分别为2.882％和1.305％。总体而言，所提出的TM–RSM–DF方法在PA设计的鲁棒性方面优于TM-DEA，TM-GRA和TM-Fuzzy。以精密放大（PA）的设计过程为例，以证明TM–RSM–DF的有效性。结果表明，与初始设计相比，TM–RSM–DF方法将位移放大率（DAR）和固有频率（NF）的估计性能提高了6.148％和0.537％。此外，与其他方法相比，TM–RSM–DF方法减少了约16％的应力，并且DAR和NF误差最低，分别为2.882％和1.305％。总体而言，所提出的TM–RSM–DF方法在PA设计的鲁棒性方面优于TM-DEA，TM-GRA和TM-Fuzzy。以精密放大（PA）的设计过程为例，以证明TM–RSM–DF的有效性。结果表明，与初始设计相比，TM–RSM–DF方法将位移放大率（DAR）和固有频率（NF）的估计性能提高了6.148％和0.537％。此外，与其他方法相比，TM–RSM–DF方法减少了约16％的应力，并且DAR和NF误差最低，分别为2.882％和1.305％。总体而言，所提出的TM–RSM–DF方法在PA设计的鲁棒性方面优于TM-DEA，TM-GRA和TM-Fuzzy。此外，与其他方法相比，TM–RSM–DF方法减少了约16％的应力，并且DAR和NF误差最低，分别为2.882％和1.305％。总体而言，所提出的TM–RSM–DF方法在PA设计的鲁棒性方面优于TM-DEA，TM-GRA和TM-Fuzzy。此外，与其他方法相比，TM–RSM–DF方法减少了约16％的应力，并且DAR和NF误差最低，分别为2.882％和1.305％。总体而言，所提出的TM–RSM–DF方法在PA设计的鲁棒性方面优于TM-DEA，TM-GRA和TM-Fuzzy。