This study investigated effects of energy levels, pulse durations, and pulse frequencies during an IPL (Intense Pulsed Light) sintering process on surface morphology and resistance of screen-printed Ag patterns on PET substrates. Surface characteristics, including primary profile (Pa), roughness (Ra), thickness, and sheet resistance, were measured before and after sintering. At fixed energy levels (13.18, 32.96, and 46.14 kW), increasing pulse counts (2, 5, and 7) at 6 ms durations significantly increased Pa and thickness, while Ra was not changed. In contrast, higher pulse counts (4, 10, and 14) at 3 ms durations improved surface roughness by reducing Ra. Statistical analysis (Paired T-test) confirmed these results. Sheet resistance analysis showed that lower pulse counts at 6 ms caused greater variability in resistance, stabilizing with higher counts. At 3 ms, surface resistance decreased with higher pulse counts, showing reduced variability. These results suggest that adjusting pulse conditions and counts during the sintering process can optimize both electrical properties and uniformity. Additionally, morphological changes before and after sintering indicated that these adjustments might influence upper-layer printability in multilayer printing. The study highlights the importance of considering both functional and morphological characteristics during sintering for optimized production of printed electronic devices.

Microfluidics allows for precise manipulation of small volumes of analytical solutions in diverse applications, including disease diagnostics, drug efficacy testing, chemical analysis, and water quality monitoring. Among these diverse applications, one of the most critical aspects is the precise and programmable control of flow within microfluidic control devices. However, microfluidic experiments that employ pressure control via a gas tank may encounter restricted mobility. To address these challenges, we developed an air pump feedback control system utilizing artificial intelligence image analysis and devised a method to enhance portability. In this paper, we utilized a commercially available portable pump to achieve the desired pressure and subsequently cease operation. In addressing the challenge of sustaining prolonged pressure, we implemented a strategy wherein the dimensions of the pressure vessel were modified, accompanied by iterative pump activations, thereby ensuring the sustained maintenance of pressure over time. The evaluation of the flow controller developed in this study involves conducting a comparative flow analysis with established pneumatic flow controllers. Furthermore, we employed artificial intelligence image analysis methods to automate the operation of iterative pumps. In conclusion, we anticipate that the developed portable microfluidic control device will lead to innovative advancements in modern technology and healthcare through its potential applications.

The Forming Limit Diagram (FLD) is a criterion used to assess the formability of sheet metal during a manufacturing process. Traditionally, FLDs are obtained through manual measurements using Mylar tape or through the use of automatic deformation measurement systems such as ARMIS and ARGUS. However, the use of Mylar tape is not user-friendly and can result in errors. Additionally, the cost of using automatic measuring equipment is high. To address these challenges, we propose a method that utilizes a low-cost USB digital microscope and the Python-based open-source library, OpenCV, to obtain forming limit diagrams. This approach allows for the measurement of deformation on specimens by analyzing circles printed on them. To evaluate the performance of this method, a circular grid was printed on a sus430 0.3 t specimen and a nakajima test was conducted. The strain data obtained using this system was then compared to the FLD obtained with the ARGUS system. The results confirmed that the formability of sheet metal can be assessed at a lower cost using our proposed method.

In order to monitor the machining status of a machine tool, it is necessary to measure the signal of the machine tool and establish the relationship between the machining status and the signal. One effective approach is to utilize an AIbased analysis model. To improve the accuracy and reliability of AI models, it is crucial to identify the features of the model through signal analysis. However, when dealing with time series data, it has been challenging to identify these features. Therefore, instead of directly applying time series data, a method was used to extract the best features by processing the data using techniques such as RMS and FFT. Recently, there have been numerous reported cases of designing AI models with high accuracy and reliability by directly applying time series data to find the best features, particularly in the case of AI models combining CNN and LSTM. In this paper, time series data obtained through a gap sensor are directly applied to an AI model that combines CNN, LSTM, and MLP (Multi-Layer Perceptron) to determine tool wear. The machine tool and tool status were monitored and evaluated through an AI model trained using time series data from the machining process.

People with hemiplegia require ongoing rehabilitation exercises to regain function in their upper limbs. However, due to the increasing number of elderly and disabled people, the number of rehabilitation professionals is insufficient. As a solution to this problem, researchers have been exploring various upper limb rehabilitation exercise robots. Unfortunately, these robots are often large and heavy, making them cumbersome to wear and use. The proposed exoskeleton rehabilitation robot consists of two robotic modules: an elbow module (1 DOF) and a wrist module (1 DOF). In order to analyze the robot"s workspace, the kinematics were calculated using the D-H parameters. To generate the trajectories, five able-bodied individuals wore the robot and performed the hand-wash motion, resulting in a total of 10 trajectory data sets. The reference trajectories were then generated by polynomial regression based on the collected data. Lastly, a passive mode control was experimented with in the rehabilitation process, and the results demonstrated the promising effectiveness of the proposed robot.

The key components of smart manufacturing, a central concept in the era of the 4th Industrial Revolution, consist of digital twin technology, AI, and computer vision technology. In this study, these technologies were utilized to govern the Poppy robot, a humanoid robot designed for educational and research purposes. The digital twin creates a virtual environment capable of real-time simulation, analysis, and control of the robot’s motions. The digital twin of the robot was constructed using Unity, a 3D development program. Motion data was captured while simulating the physical structure and movements of the virtual robot. This data was then fed into a Tensorflow-based deep neural network to generate a regression modelthat predicts motor rotation based on the position of the robot’s hand. By integrating this model with a Python-based robot control program, the robot’s movements could be effectively managed. Additionally, the robot was controlled using Openpose, a computer vision algorithm that predicts characteristic points on a human body. Position data for human joint points was collected from 2D images, and the motor angle was calculated based on this data. By implementing this approach on an actual robot, it became possible to enable the robot to replicate human movements.

In this study, a new method of bonding CFRP and Al6061-T6 with epoxy adhesive after shot-peening treatment on the surface of Al6061-T6 specimens was proposed to improve bonding strength of a single lap joint between CFRP and Al6061-T6. More specifically, correlation between shot peening coverage on the Al6061-T6 surface and bonding strength with CFRP was experimentally analyzed. Experimental results showed that the surface roughness and the bonding strength increased as the peening time on the surface of Al6061-T6 increased up to a specific peening time (or coverage). However, the surface roughness and bonding strength decreased again under an over-peening condition of 480 seconds (300% coverage) or more. Therefore, it is necessary to search for the optimal peening time that can maximize bonding strength as well as the fatigue life of parts at a peening time between 320 (200%) and 480 s (300%) through additional experiments in future studies.

In this study, we present the fabrication of dual-morphing vascular stents using an additive-lathe printing method and two different shape-memory polymers. Traditional additive manufacturing techniques confront significant challenges in producing vascular stents with complex, hollow, mesh-like structures due to limitations such as a flat printing bed and the placement of supports. To overcome these obstacles, we employed a lathe-type additive manufacturing system with a rotatable base substrate, enabling precise fabrication of cylindrical-shaped stents. To achieve shape transformability, we used shapememory polymers as the stent materials, offering the advantage of minimally invasive surgery. Two distinct shape-memory polymers, with different transition temperatures (35 and 55oC), were printed using the additive-lathe method. The printed stents consisted of two distinct parts that underwent dual-stage morphological changes at the different temperatures. By manipulating the printing paths, the dual-morphing properties of the stents could be adjusted in both longitudinal and circumferential directions. This innovative approach could be a solution to several limitations associated with the application of stents in diseased vascular tissues with complex shapes, facilitating minimal invasion during surgical procedures.

With recent development of 3D printing technology, its applications to the bio-industry are increasing. Many research studies are being done for manufacturing personalized tablets through this technology in the pharmaceutical process. In this study, to control the dissolution rate of tablets, a lattice structure was inserted into the tablet and the dissolution rate was compared. The tablet proposed in this study can be manufactured by the FDM method, adopting a lattice structure with a large surface area-to-volume ratio. Tablets containing various lattice structures were fabricated using water-soluble PVA filaments and dissolution experiments were conducted in water at 37oC. As a result, it was confirmed that the specific surface area and the mass loss rate were proportional to both the 3D lattice structure and the monolith structure. Among different structures, the diamond structure had the most active dissolution.

Factors such as weight reduction and improved fuel efficiency of vehicles interfere with the efficiency of roller bearings in automobiles under harsh conditions. In particular, studies are ongoing to increase the load capacity and rigidity under highspeed conditions. The development of tapered roller bearings that can be used under high-speed conditions is accelerating. In the case of high-speed bearings, factors such as centrifugal force, gyroscopic moment, and slippage have a greater influence on the performance of the bearing, unlike the traditional operating mechanisms. The resulting lubrication characteristics have a profound impact on the failure mode of the bearing. In particular, unlike traditional roller bearings, system failure due to damage to the retainer frequently occurs, suggesting the need for prompt investigation. In this study, the rotational characteristics and strength of three models, a steel cage and two plastic cages for tapered roller bearings with the same internal structure, were examined. A comparative analysis of retainers with different shapes and materials can reveal the factors contributing to optimal performance under high-speed operating conditions and the optimal design of bearings.

Advances in cell culture technology have improved the understanding of the physiological principles of cells. Recently, the development of microfluidic chips has made it possible to observe single cells in a massively parallelized and accurate manner. However, in order to maximize the availability of the microfluidic cell chip, it is essential to use an incubator that can isolate the cell culture chip from the outside while minimizing contamination and maintaining the temperature and humidity required for cell culture for a long time period. Here, we developed a thermo-hygrostat incubator consisting of an Arduino-based feedback control module for controlling a temperature and humidity complex sensor, a humidifier, and a heater. The temperature and humidity of the incubator could be actively changed according to the needs and application by simple editing control variables of Arduino coding. To demonstrate the efficiency of the device, we conducted an experiment comparing the growth of bacterial cells and obtained optimal conditions necessary for culture. In conclusion, it is expected that the newly developed thermo-hygrostat incubator can be used for a variety of purposes that require active control of temperature and humidity, as well as for long-term cultivation of bacterial cells inside a microfluidic chip.

Recently, industrial manufacturing has developed into additive manufacturing, benefiting from multi-item small-sized production and effective manufacturing. Importantly, Wire Arc Additive Manufacturing, which uses metal wires, is attracting worldwide attention for its high-quality metal product technology. Technological innovation that combines virtual physics with reality through big data communication, such as process variables along with Wire Arc Additive Manufacturing, is an essential task for implementing smart manufacturing technology. Due to the characteristic of Wire Arc Additive Manufacturing, numerous variable conditions exist, making it difficult to standardize robot"s process path data generation algorithms and data application methods, and this data generation method is being studied as a core element technology. The present study generated foundation process implementation, simulation, and generated path data for robots in virtual space using RoboDK, which provides robot libraries from multiple manufacturers, and Python, which is a universal programming language. To implement the experimental data in practice, ABB"s industrial six-axis robots IRB-6700 and Fronius TPS500i were used to control the arcing plasma heat source, and the process path worked the same as simulation. Based on the underlying experimental results, this process can be applied to generation of additive manufacturing in the Wire Arc Additive Manufacturing process for 3D models.

This paper introduces PongBot, a quadruped robot developed for preparation in the Dronebot Challenge held in Jangseonggun, Jeollanam-Do, South Korea in November 2020. The Dronebot Challenge, hosted by the Army Headquarters, is a competition to demonstrate that drones and robots can be useful for military purposes. In 2020, this competition consisted of a total of 8 events and we participated in the ‘Traveling on rough terrain’ event, which consisted of various terrains, such as, slopes, unpaved roads, and streams. PongBot is a quadruped robot that uses an electric motor and can walk for more than an hour on various terrains. Also, according to the rules of the competition, the robot had a system which could be remotely controlled from a ground control station. In addition, by applying the SLAM algorithm, the robot operator received information about its surrounding environment, thereby deriving records to facilitate the operation. The performance of this robot system and SLAM algorithm was verified through this competition.

Nature-inspired architected materials have been widely used to achieve efficient structural materials by harnessing their cellular and hierarchical structures. For example, biological materials observed in bone, shell, nacre, and wood contain constituents, ranging from nanometers to centimeters, arranged in an ordered hierarchy. Because of their composited structures that contain micro and nanoscale building blocks arranged in an ordered hierarchy and the material size effect in the mechanical strength of nano-sized solids, bioceramic materials are mechanically robust and lightweight. The design principles offered by hard biological materials of multiscale composite structures can assist in the creation of advanced ceramic architectures. In addition, the evolution of additive manufacturing technologies has enabled the fabrication of materials with intricate cellular architected materials. In this review, we discussed advanced additive manufacturing for the fabrication of nature-inspired multiscale ceramic structures by combining conformal thin-film coating technique with conventional additive manufacturing methods.