Abstract：
Pressure sensors urgently need high-performance sensing materials in order to be developed further. Sensitivity and creep are regarded as two key indices for assessing a sensor\'s performance. For the design and optimization of sensing materials, an accurate estimation of the impact of several parameters on sensitivity and creep is essential. In this study, sensitivity and creep were predicted using the response surface methodology (RSM) and support vector regression (SVR), respectively. The input parameters were the concentrations of nickel (Ni) particles, multiwalled carbon nanotubes (MWCNTs), and multilayer graphene (MLG), as well as the magnetic field intensity (B). According to statistical measures, the SVR model exhibited a greater level of predictability and accuracy. The non-dominated sorting genetic-II algorithm (NSGA-II) was used to generate the Pareto-optimal fronts, and decision-making was used to determine the final optimal solution. With these conditions, the optimized results revealed an improved performance compared to the earlier study, with an average sensitivity of 0.059 kPa-1 in the pressure range of 0-16 kPa and a creep of 0.0325, which showed better sensitivity in a wider range compared to previous work. The theoretical sensitivity and creep were relatively similar to the actual values, with relative deviations of 0.317% and 0.307% after simulation and experimental verification. Future research for transducer performance optimization can make use of the provided methodology because it is representative.
摘要：
压力传感器迫切需要高性能传感材料的进一步发展。灵敏度和蠕变被认为是评估传感器性能的两个关键指标。对于传感材料的设计和优化，准确估计几个参数对灵敏度和蠕变的影响是至关重要的。在这项研究中,使用响应面方法(RSM)和支持向量回归(SVR)预测灵敏度和蠕变，分别。输入参数是镍（Ni）颗粒的浓度，多壁碳纳米管(MWCNT),和多层石墨烯（MLG），以及磁场强度（B）。根据统计数据，SVR模型表现出更高水平的可预测性和准确性.非支配排序遗传-II算法(NSGA-II)用于生成帕累托最优前沿，并通过决策来确定最终的最优解。在这些条件下，优化结果表明，与早期研究相比，性能有所改善，在0-16kPa的压力范围内，平均灵敏度为0.059kPa-1，蠕变为0.0325，与以前的工作相比，在更宽的范围内显示出更好的灵敏度。理论灵敏度和蠕变与实际值相对相似，经模拟和实验验证,相对偏差分别为0.317%和0.307%。换能器性能优化的未来研究可以利用所提供的方法，因为它是代表性的。
