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Four-dimensional (4D) printing adds the dimension of time to 3D-printed specimens, causing movement when external stimuli are applied. This movement enables applications across various fields, including the soft robotics, aerospace, apparel, and automotive industries. Traditionally, 4D printing has utilized special materials such as shape-memory polymers (SMPs) or shape-memory alloys (SMAs) to achieve this movement. This study explores a novel approach to 4D printing by applying microcellular foaming processes (MCPs) to 3D printing. This study primarily aims to design and fabricate patterned specimens using common materials, such as PLA, through 3D printing and to analyze their dynamic behavior under various foaming conditions. To demonstrate the potential applications of this technology, the degree of bending was measured by controlling the patterning and foaming conditions. The 3D-printed specimens with microcellular foaming exhibited predictable deformations owing to the asymmetric expansion caused by differential gas saturation. The results confirm that 4D printing can be realized using conventional materials without the need for smart materials and can introduce foaming processes as a new external stimulus. This study highlights the potential of combining 3D printing with microcellular foaming for advanced 4D printing applications. [ABSTRACT FROM AUTHOR]

In previous studies, difficulties were encountered in measuring changes within high-pressure vessels owing to limitations such as sensor connectors and sensor failures under high-pressure conditions. In addition, polymer–gas mixtures experience instantaneous gas desorption upon exiting high-pressure vessels owing to pressure differentials, leading to measurement errors. In this study, a device using magnetic sensors was developed to measure the real-time changes in gas-saturated polymers inside pressure vessels. Experiments on polymethyl methacrylate gas adsorption were conducted with parameters including pressure at 5 MPa and temperatures ranging from −20 to 40 °C for 60 and 180 min. It was observed that at −20 °C, the maximum magnetic field force density and deflection were 391.53 μT and 5.83 mm, respectively, whereas at 40 °C, deflection did not occur, with a value of 321.79 μT. Based on gas saturation experiments, a new model for deflection in high-pressure atmospheres is proposed. Additionally, an ANSYS analysis was conducted to predict the changes in Young's modulus based on gas saturation. In previous studies, mechanical properties were measured outside the pressure vessel, resulting in an error due to a pressure difference, while the proposed method is characterized by the ability to directly measure polymer behavior according to gas saturation in high-pressure vessels using a magnetic sensor in real time. Therefore, it is possible to predict polymer behavior, making it easy to control variables in high-pressure polymer processes. [ABSTRACT FROM AUTHOR]

The well-known conventional Kalman filter requires an accurate system model and exact stochastic information. But in a number of situations, the system model has an unknown bias, which may degrade the performance of the Kalman filter or may cause the filter to diverge. The effect of the unknown bias may be more pronounced on the extended Kalman filter (EKF), which is a nonlinear filter. The two-stage extended Kalman filter (TEKF) with respect to this problem has been receiving considerable attention for a long time. Recently, the optimal two-stage Kalman filter (TKF) for linear stochastic systems with a constant bias or a random bias has been proposed by several researchers. A TEKF can also be similarly derived as the optimal TKF. In the case of a random bias, the TEKF assumes that the information of a random bias is known. But the information of a random bias is unknown or partially known in general. To solve this problem, this paper proposes an adaptive two-stage extended Kalman filter (ATEKF) using an adaptive fading EKF. To verify the performance of the proposed ATEKF, the ATEKF is applied to the INS-GPS (inertial navigation system-Global Positioning System) loosely coupled system with an unknown fault bias. The proposed ATEKF tracked/estimated the unknown bias effectively although the information about the random bias was unknown.