Moheimani Reza, Gonzalez Marcial, Dalir Hamid
Ray W. Herrick Laboratories, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Department of Mechanical and Energy Engineering, Indiana University-Purdue University, Indianapolis, IN 46202, USA.
Nanomaterials (Basel). 2022 Apr 8;12(8):1269. doi: 10.3390/nano12081269.
This paper utilizes multi-objective optimization for efficient fabrication of a novel Carbon Nanotube (CNT) based nanocomposite proximity sensor. A previously developed model is utilized to generate a large data set required for optimization which included dimensions of the film sensor, applied excitation frequency, medium permittivity, and resistivity of sensor dielectric, to maximize sensor sensitivity and minimize the cost of the material used. To decrease the runtime of the original model, an artificial neural network (ANN) is implemented by generating a one-thousand samples data set to create and train a black-box model. This model is used as the fitness function of a genetic algorithm (GA) model for dual-objective optimization. We also represented the 2D Pareto Frontier of optimum solutions and scatters of distribution. A parametric study is also performed to discern the effects of the various device parameters. The results provide a wide range of geometrical data leading to the maximum sensitivity at the minimum cost of conductive nanoparticles. The innovative contribution of this research is the combination of GA and ANN, which results in a fast and accurate optimization scheme.
本文利用多目标优化方法高效制造一种新型的基于碳纳米管（CNT）的纳米复合接近传感器。利用先前开发的模型生成优化所需的大数据集，该数据集包括薄膜传感器的尺寸、施加的激励频率、介质介电常数和传感器电介质的电阻率，以最大化传感器灵敏度并最小化所用材料的成本。为了减少原始模型的运行时间，通过生成一个包含一千个样本的数据集来实现人工神经网络（ANN），以创建和训练一个黑箱模型。该模型用作遗传算法（GA）模型进行双目标优化的适应度函数。我们还展示了最优解的二维帕累托前沿和分布散点图。还进行了参数研究以识别各种器件参数的影响。结果提供了广泛的几何数据，从而在导电纳米颗粒成本最低的情况下实现最大灵敏度。本研究的创新贡献在于GA和ANN的结合，这产生了一种快速且准确的优化方案。
