This study presents a new fabrication method for touch screen sensors using inexpensive, flexible, and transparent polyethylene terephthalate (PET) films. In the proposed method, a transparent capacitive touch sensor array is implemented with two independent axes of invisible electrodes consisting of indium tin oxide (ITO) patterns and bridge electrodes. A low‐cost implementation of the bridge electrodes is achieved by using near‐field electrospinning (NFES) method for the conductive lines with linewidth of 3–5 µm. Then, the printed bridge electrode is sintered using green laser without damaging the PET film. It is demonstrated that two transparent electrodes deposited on a single sheet of PET film can detect the touch location by scanning the driving signal while simultaneously measuring the capacitances from the sensing lines connected via bridge electrodes.

Electrohydrodynamic (EHD) jet printing has drawn attention due to its capability to produce smaller dots and patterns with finer lines when compared to those obtained from using conventional inkjet printing. Previous studies have suggested that drop-on-demand EHD-patterning applications should be limited to very slow printing cases with speeds far less than 10 mm s ⁻¹ due to the small dot size and limited jetting frequency. In this study, a new EHD printing method is proposed to significantly increase the line-patterning printing speed by modifying the ink and thereby changing the relic shape. The proposed method has the additional advantage of reducing the line-pattern width. The results of the experiment show that the pattern width could be reduced from 20 µ m to 4 µ m by increasing the printing speed from 10 mm s ⁻¹ to 50 mm s ⁻¹ , respectively.

Copper oxide (CuO) nanoparticle (NP) ink is a potential candidate for low-cost alternatives to other metal-based nano-particle inks (e.g., Au, Ag.) in printed electronics. To obtain Cu patterns from CuO NP ink, CuO NP inks should be converted to Cu particles, and be fused to form a connected conductive line. For this purpose, photonic sintering methods have been widely used, which generate the heat required for sintering via the absorption of light. In this study, we used continuous wave (CW) green laser with 532 nm wavelength, since the laser has the advantage of selective sintering by irradiation of light only on the target place. We investigated the optimal sintering parameters, such as laser power and scanning speed, using the green laser, in order to obtain low resistivity. We also investigated the pre-treatment conditions, such as pre-baking, which is required to evaporate solvents in the ink. We found that over-baking of deposited film will adversely affect the sintering, because film can be easily damaged from laser irradiation. As a result of laser sintering, we obtained the resistivity of (9.5 and 71.6) μΩ·cm when the pre-baked thicknesses of CuO films were (546 and 889) nm, respectively. In such cases, the thicknesses were significantly reduced to (141 and 270) nm, respectively.

One of the advantages of inkjet printing in digital manufacturing is the ability to use multiple nozzles simultaneously to improve the productivity of the processes. However, the use of multiple nozzles makes inkjet status monitoring more difficult. The jetting nozzles must be carefully selected to ensure the quality of printed products, which is challenging for most inkjet processes that use multi-nozzles. In this article, we improved inkjet print head monitoring based on self-sensing signals by using machine learning algorithms. Specifically, supervised machine learning models were used to classify nozzle jetting conditions. For this purpose, the self-sensing signals were acquired, and the feature information was extracted for training. A vision algorithm was developed to label the nozzle status for classification. The trained models showed that the classification accuracy is higher than 99.6% when self-sensing signals are used for monitoring. We also proposed a so-called hybrid monitoring method using trained machine learning models, which divides the feature space into three regions based on predicted jetting probability: certain jetting, certain non-jetting, and doubt regions. Then, the nozzles with uncertain status in the doubt region can be verified by jet visualization to improve the accuracy and efficiency of the monitoring process.