Design, Fabrication, and Testing of a Modular Magnetic Field Microsensor on a Flexible Polymer Foil
In contrast to existing microelectromechanical systems (MEMS), this paper describes the development, fabrication, and testing of an anisotropic magnetoresistance (AMR) microsensor using a 7 μm thick polymer foil as substrate material. The modular magnetic field micro sensors are fabricated on a standard 4 in Si Wafer due to handling purposes during the sensor fabrication process. To enable a release of the micro sensors at the end of the fabrication process, initial investigations concentrated on the proof of principle applying a deep reactive-ion etching (DRIE) process to structure the Si wafer. The DRIE process was used to structure Si frames, which serve as carriers for the modular magnetic field micro sensors. For the evaluation of the fabricated modular magnetic field micro sensors, electrical resistance measurements were accomplished. The aim of these investigations was the characterization of the magnetic field dependence of the electrical resistance. The electrical output signal of the AMR sensors were subsequently compared to electrical resistance measurements of AMR sensors fabricated on a Si substrate and served as reference. The measurement results show a marginal effect of the substrate material on the characteristics of the AMR sensors.
Static Modeling of Bending Characteristics on V-Shaped Beam Actuator Based on Flexible Substrate
Due to the unique properties of flexible electronic devices, various microelectromechanical systems (MEMS) devices based on flexible substrate have been proposed for miniaturization, lightweight, and intelligent applications. Obviously, the deformation of flexible substrate inevitably affects the performance of flexible MEMS devices. This article first investigates the static modeling of bending characteristics on the flexible V-shaped beam actuator, which is the most representative MEMS thermal actuator. This article focused on a comprehensive static analysis of bending mechanical property based on the flexible substrate. The innovation of static modeling is obtaining variation regularity of bending characteristics by establishing the deformation coupling model of V-beam structure and flexible substrate. The V-shaped beam thermal actuators are designed and fabricated on flexible liquid crystal polymer (LCP) substrate, and the influence of bending substrate on the actuation performance of the device is revealed by calculation, simulation, and experiment. The experimental results demonstrate that the actuation current of the V-shaped beam actuator rises up proportionally with the increase of angle and the decrease of beam length. When flexible substrate bends gradually from curvature 0 to 33.3 (1/m), the rates of measured actuation current change are 17.05%, 13.70%, and 10.24%, corresponding to different angles of 13°, 20°, and 27° at $600~\mu \text{m}$ length of V-shaped beams. Meanwhile, the rates of measured actuation current change are 9.22%, 13.22%, and 13.70%, corresponding to different lengths of 400, 500, and $600~\mu \text{m}$ at 20° angle of V-shaped beams. The experimental results are consistent with the theoretical calculation results of static modeling, and the error is less than 3.7%.
Reinforcement Learning Approaches to Biological Manufacturing Systems
Abstract The concept of Biological Manufacturing System (BMS) aims at dealing with unpredictable changes in the external and internal environments during whole product life cycle, based on biologically-inspired ideas such as self-organization, learning and evolution. We previously developed evolution-based and self-organization models of the floor level that are able to cope with environmental changes such as system reconfiguration, machine breakdown and unforeseen production requests. This paper describes reinforcement learning approaches to the modeling of BMS, in order to deal with the various kinds of complex global objectives. The effectiveness of the proposed model is also discussed by simulation with global objectives.