Online Monitoring of Functional Electrical Properties in Aerosol Jet Printing Additive Manufacturing Process Using Shape-From-Shading Image Analysis
Online Monitoring of Functional Electrical Properties in Aerosol Jet Printing Additive Manufacturing Process Using Shape-From-Shading Image Analysis
复制标题
DOI：
10.1115/1.4036660
复制
发表时间：
2017-10-01
影响因子：
4
通讯作者：
Poliks, Mark D.
中科院分区：
文献类型：
--
作者：
Salary, Roozbeh (Ross);Lombardi, Jack P.;Poliks, Mark D.
本研究的目标是在线监测功能性电特性，例如，电阻，使用气溶胶喷射印刷（AJP）增材制造（AM）工艺制造的电子器件。为了实现这一目标，我们的目标是通过对在线图像的阴影形状（SfS）分析来恢复AJP沉积的电子迹线（称为线）的横截面轮廓。其目的是使用Sfs导出的横截面轮廓来预测线路的电阻。横截面的准确表征对于监测器件电阻和其他功能特性至关重要。例如，根据欧姆定律，导体的电阻与其横截面积（CSA）成反比。中心假设是，AJP沉积线的电阻估计在线和原位从其Sfs衍生的横截面积是在其离线测量的20%以内。为了测试该假设，使用Optomec AJ-300打印机在不同的鞘气流速（ShGFR）条件下沉积银纳米颗粒线。四点探针法，称为开尔文传感，用于离线测量印刷结构的电阻。使用同轴安装到打印机的沉积喷嘴的电荷耦合器件（CCD）相机在线获取线的图像。为了从在线图像中恢复横截面轮廓，测试了三种不同的SfS技术：Horn方法、Pentland方法和Shah方法。使用光学轮廓术来验证SfS截面估计值。Shah的方法被发现在三种SfS方法中具有最高的保真度。根据使用Shah方法的线横截面的Sfs估计值，预测线抗性作为ShGFR的函数。发现在线Sfs衍生的线电阻在使用开尔文感测技术进行的离线电阻测量的20%以内。
The goal of this research is online monitoring of functional electrical properties, e.g., resistance, of electronic devices made using aerosol jet printing (AJP) additive manufacturing (AM) process. In pursuit of this goal, the objective is to recover the cross-sectional profile of AJP-deposited electronic traces (called lines) through shape-from-shading (SfS) analysis of their online images. The aim is to use the SfS-derived cross-sectional profiles to predict the electrical resistance of the lines. An accurate characterization of the cross section is essential for monitoring the device resistance and other functional properties. For instance, as per Ohm's law, the electrical resistance of a conductor is inversely proportional to its cross-sectional area (CSA). The central hypothesis is that the electrical resistance of an AJP-deposited line estimated online and in situ from its SfS-derived cross-sectional area is within 20% of its offline measurement. To test this hypothesis, silver nanoparticle lines were deposited using an Optomec AJ-300 printer at varying sheath gas flow rate (ShGFR) conditions. The four-point probes method, known as Kelvin sensing, was used to measure the resistance of the printed structures offline. Images of the lines were acquired online using a charge-coupled device (CCD) camera mounted coaxial to the deposition nozzle of the printer. To recover the cross-sectional profiles from the online images, three different SfS techniques were tested: Horn's method, Pentland's method, and Shah's method. Optical profilometry was used to validate the SfS cross section estimates. Shah's method was found to have the highest fidelity among the three SfS approaches tested. Line resistance was predicted as a function of ShGFR based on the SfS-estimates of line cross section using Shah's method. The online SfS-derived line resistance was found to be within 20% of offline resistance measurements done using the Kelvin sensing technique.