Experimental Study on a Microwave Composite Forming Process Based on a SiC Mold for Manufacturing Fiber Metal Laminate.

The microwave composite forming (MCF) process can reduce manufacturing cost because the process time is reduced by the dielectric heating of the mold and the composite material. In a previous study, the MCF process using a commercial microwave oven with a polytetrafluoroethylene mold was applied. Disadvantages of the previous MCF process have been investigated. These included the difference in tensile properties according to the cutting location, absence of a method to measure temperature during the MCF process, and the fact that the input power cannot be controlled according to the temperature. To solve these problems, a microwave oven with a silicon carbide mold was proposed in this study. Uniaxial tensile tests were conducted to obtain the tensile properties of the fiber metal laminate (FML) specimen. In addition, a microscopic image was captured to investigate the non-adhesive area. The tensile properties and thickness distribution of the FML specimens manufactured by the proposed and previous MCF processes were compared according to the cutting location of the FML sheets. Furthermore, the non-adhesive area was quantified to compare the processes. The results revealed that the proposed MCF process improved the tensile properties of the FML specimen and reduced the non-adhesive area.

Improved Attitude and Heading Accuracy with Double Quaternion Parameters Estimation and Magnetic Disturbance Rejection.

The use of unmanned aerial vehicle (UAV) applications has grown rapidly over the past decade with the introduction of low-cost microelectromechanical system (MEMS)-based sensors that measure angular velocity, gravity, and magnetic field, which are important for an object orientation determination. However, the use of low-cost sensors has also been limited because their readings are easily distorted by unwanted internal and/or external noise signals such as environmental magnetic disturbance, which lead to errors in attitude and heading estimation results. In an extended Kalman filter (EKF) process, this study proposes a method for mitigating the effect of magnetic disturbance on attitude determination by using a double quaternion parameters for representation of orientation states, which decouples the magnetometer from attitude computation. Additionally, an online measurement error covariance matrix tuning system was implemented to reject the impact of magnetic disturbance on the heading estimation. Simulation and experimental tests were conducted to verify the performance of the proposed methods in resolving the magnetic noise effect on attitude and heading. The results showed that the proposed method performed better than complimentary, gradient descent, and single quaternion-based EKF.

Experimental Study on Microwave-Based Curing Process with Thermal Expansion Pressure of PTFE for Manufacturing Carbon Fiber/Epoxy Composites.

Conventional composite curing incur high production costs because of their long processing times. In contrast, microwave curing process (MCP) can reduce the production costs because both the mold and the composite parts can be heated directly. In this study, a mold consisting of polytetrafluoroethylene (PTFE), quartz glass, and stainless steel clamps was manufactured to cure composite specimens of carbon fiber and epoxy resin. Flame test was conducted prior to the MCP to confirm whether the spark occurred in the mold and the composite prepreg. Uniaxial tensile tests and three-point bending tests were then conducted to obtain the mechanical properties of the composite specimens according to the input power and the processing time. The mechanical properties of the composite specimens fabricated by MCP were compared with those of composite specimens manufactured by PCF. The results show that MCP can cure the composite prepreg more rapidly than PCF and can attain comparable mechanical properties.

Acquisition of Dynamic Material Properties in the Electrohydraulic Forming Process Using Artificial Neural Network.

Electrohydraulic forming is a high-velocity forming process that deforms sheet metals with velocities above 100 m/s and strain rates more than 100 s-1. This experiment was conducted in a closed space because of safety concerns related to the high-velocity conditions; therefore, we were not able to examine the deformation process of the sheet metal. To observe the electrohydraulic forming process in detail, we performed virtual numerical simulations using accurate material properties. Therefore, in this paper, we obtained the material property of a sheet metal from a numerical estimation by using a surrogate model based on the reduced order model and the artificial neural network. The Cowper-Symonds constitutive equation was selected for the Al 6061-T6 sheet metal, and two strain rate parameters were adopted as the unknown parameters. From the two sampling techniques, the training and test samples were extracted from the specific ranges of two unknown parameters, and a numerical simulation was performed for these samples by using the LS-DYNA program. The z-axis displacements of the deformed sheet metal were obtained from the results of the numerical simulation, and two basis vectors were extracted by using principal component analysis. In addition, to predict the weighting coefficients of the two basis vectors at the defined range of parameters, we used the artificial neural network technique as a surrogate model. By comparing the surrogate model and the experimental results and calculating the root mean square error value, we estimated the optimal parameter for Al 6061-T6. Finally, the reliability of the obtained material parameters was proved by comparing the experimental results, the surrogate model, and LS-DYNA.