A fused deposition modeling (FDM) printing machine and a paste extrusion system were integrated, and supercapacitor samples were fabricated using a combination of two three-dimensional (3D) printing techniques. The FDM provided a simple method for creating a frame of electric double layer capacitor (EDLC) samples. The paste extrusion system offered the possibility of depositing different materials to complete the functions of the EDLC samples. A combination of these two 3D printing methods offered one continuous manufacturing process with a high accuracy of manufacturing. Different materials were used to build current collectors and electrodes. Silver and carbon conductive paints were used as current collector materials. Different electrode materials based on activated carbon (AC), carbon conductive paint, and their combination were prepared as three different slurries and deposited to form the electrodes of EDLC samples. The results showed that silver conductive paint was a suitable material for constructing current collectors, and carbon conductive paint mixed with AC was highly effective for use as an electrode material for supercapacitors.

The evaporation rate of a droplet was explained in relation to the thickness of the boundary layer and the condition near the droplet’s surface. However, the number of results obtained from experiments is very limited. This study aims to investigate the thickness of the boundary layer of an ethanol–water mixture droplet and its effect on the evaporation rate by Z-type Schlieren visualization. Single and double droplets are tested and compared to identify the effect of the second droplet on the average and instantaneous evaporation rate. The double droplet’s lifetime is found to be longer than the single droplet’s lifetime. The formation of a larger vapor region on the top of the droplet indicates a higher instantaneous evaporation rate. The thickness of the boundary layer is found to increase with increase in ethanol concentration. Furthermore, a larger vapor distribution area is found in the case of higher ethanol concentration, which explains the faster evaporation rate at higher ethanol concentration.

Continuous inkjet printing relies on steering charged droplets accurately to the surface by using electric fields. A vital component is the set of deflecting electrodes within the printhead, which create these fields. Unwanted deposition of ink on the electrodes, known as build-up, is a concern for operators because this modifies the applied electric field, affects long-term reliability, and requires manual intervention. However, this has not been widely reported or explored. Here, the authors report a laser-based high-speed visualization technique to observe build-up and show that it stems from small satellite droplets that break off from the main printed drops. They characterize the material build-up and reveal its nanoscale particulate nature. Combining the tracking with characterization allows quantifying the charge-to-mass ratio of these droplets. This study provides a route to understanding the build-up phenomenon, and it will enable optimization of printing conditions and printing reliability.

To explore the effects of geometric features on the color similarity perception of displayed three-dimensional (3D) tablets designed by color 3D modeling techniques or printed by color 3D printing techniques, two subjective similarity scaling tasks were conducted for color tablets with four shape features (circular, oval, triangular-columnar, and rounded-cuboid shapes) and four notch features (straight V, straight U, crisscross V, and crisscross U shapes) displayed on a calibrated monitor using the nine-level category judgement method. Invited observers were asked to assort all displayed samples into tablet groups using six surface colors (aqua blue, bright green, pink, orange yellow, bright red, and silvery white), and all perceived similarity values were recorded and compared to original samples successively. The results showed that the similarity perception of tested tablets was inapparently affected by the given shape features and notch features, and it should be judged by a flexible interval rather than by a fixed color difference. This research provides practical insight into the visualization of color similarity perception for displayed personalized tablets to advance precision medicine by 3D printing.

Recently, the three-dimensional (3D) printing technique has attracted much attention for creating objects of arbitrary shape and manufacturing. For the first time, in this work, we present the fabrication of an inkjet printed low-cost 3D temperature sensor on a 3D-shaped thermoplastic substrate suitable for packaging, flexible electronics, and other printed applications. The design, fabrication, and testing of a 3D printed temperature sensor are presented. The sensor pattern is designed using a computer-aided design program and fabricated by drop-on-demand inkjet printing using a magnetostrictive inkjet printhead at room temperature. The sensor pattern is printed using commercially available conductive silver nanoparticle ink. A moving speed of 90 mm∕min is chosen to print the sensor pattern. The inkjet printed temperature sensor is demonstrated, and it is characterized by good electrical properties, exhibiting good sensitivity and linearity. The results indicate that 3D inkjet printing technology may have great potential for applications in sensor fabrication.

For mass production, multiple color halftoning screen printing (MCHSP) can be considered as the alternative textile printing technology when vivid color gradation is needed and the cost of digital printing is a concern. Essentially, MCHSP utilizes the same equipment as traditional screen printing to print overlapping multiple color gradation under halftoning patterns by applying dedicated treatments on color separation and calibration. To ensure color quality, equipment calibration and tone curve compensation are required to compensate for the variables arising from equipment setup and heterogeneous fabrics. In this research, the authors present a procedure of tone curve compensation to eliminate the discrepancy from heterogeneous fabrics. The experimental results based on 55 samples of 44 different fabrics show the effectiveness of compensation and reveal the distribution of average compensation percentage across fabrics.

We learn the color of objects and scenes through our experience in everyday life. The colors of things that we see more frequently are defined as memory colors. These help us communicate, identify objects, detect crop ripeness or disease, evaluate the weather, and recognize emotions. Color quality has become a priority for the smartphone and camera industry. Color quality assessment (CQA) provides insight into user preference and can be put to use to improve cameras and display pipelines. The memory color of important content like human skin, food, etc. drives perceived color quality. Understanding memory color preference is critical to understanding perceived color quality. In this study, grass, sky, beach sand, green pepper, and skin were used to perform memory color assessment. Observers were asked to adjust patches with four different textures, including computed textures and real image content, according to their memory. The results show that observers adjust the image patch most consistently. In cases where the artificially generated textures closely resembled the real image content, particularly for the sky stimulus, which resembled a flat color patch, participants were able to adjust each sample more consistently to their memory color. To understand the relation between memory color and the color quality preference for camera images, a second experiment was performed. A paired comparison for familiar objects was performed with five different color quality images per object. Two of these five images were rendered from the results of the memory color assessment experiment. Additional images included were the three most preferred color quality images from a rank order CQA. This experiment was performed by naïve observers and a validation experiment was also performed by Munsell Color Science Laboratory observers. The results for color image rendering preference for each memory image content vary. The results show that for most of the colors, people prefer the top three camera color quality images used from the rank order CQA. For grass, however, the color quality preference is highest for one of the memory color assessment results. In this experiment, images rendered to reflect memory color do not match observer preference.

The authors discuss the spectral estimation of multiple light sources from image data in a complex illumination environment. An approach is proposed to effectively estimate illuminant spectra and the corresponding light sources based on highlight areas that appear on dielectric object surfaces. First, the authors develop a highlight detection method using two types of convolution filters with Gaussian distributions, center-surround and low-pass filters. This method is available even for white surfaces, and it is independent of object color and of viewing and incidence angles. Second, they present an algorithm for estimating the illuminant spectra from extracted highlight areas. Each specular highlight area has a spectral composition corresponding to only one light source among multiple light sources. The spectral image data are projected onto a two-dimensional subspace, where a linear cluster in pixel distribution is detected for each highlight area. Third, the relative positional relationship between highlight areas among different object surfaces is used to identify the light sources on each surface. The authors develop an algorithm based on probabilistic relaxation labeling. The light source for each highlight and the corresponding spectral-power distribution are determined from the iterative labeling process. Finally, the feasibility of the proposed approach is examined in an experiment using a real complex environment, where dielectric objects are illuminated by multiple light sources of light-emitting diode, fluorescence, and incandescence.

Digital halftoning is a technique for converting a continuous-tone image into a quantized image to reproduce it on a digital printing device. Error diffusion (ED) is an algorithm that has proven to be effective for the halftoning process, and it has been widely applied to digital printing tasks. However, in images reproduced using conventional ED algorithms based on the signal processing theory, the texture of objects is often lost. In this study, we propose a texture-aware ED algorithm for multi-level digital halftoning. First, we generate multiple mapped images with different brightness levels through nonlinear transformation. For each mapped image, we adopt a texture-aware binary error diffusion method to obtain multiple halftone images. Finally, we generate a multi-level halftone image from the multiple halftone images. We test the algorithm on an actual printer, compare the results with those of the current raster image processor software and classical ED algorithms, and observe that our algorithm outputs better results.