Search Results

Abstract The paper presents a complete description of the design and manufacturing of a Carbon Fiber/epoxy mold with an embedded Carbon Fiber resistor heater, and the mold performances in terms of its surface temperature distribution and thermal deformations resulting from the heating. The mold was designed for manufacturing aileron skins from Vacuum Bag Only prepreg cured at 135°C. The glass transition temperature of the used resin-hardener system was about 175°C. To ensure homogenous temperature of the mold working surface in the course of curing, the Carbon Fiber heater was embedded in a layer of a highly heat-conductive cristobalite/epoxy composite, forming the core of the mold shell. Because the cristobalite/epoxy composite displayed much higher thermal expansion than CF/epoxy did, thermal stresses could arise due to this discrepancy in the course of heating.

Abstract A large amount of heat generated in the engineering compartment in a hovering helicopter may lead to premature degradation of inner skin of its engine cowling and cause serious failure on the engine cowling. This study proposes a solution of improving heat resistance of the helicopter engine cowlings by replacing the currently used intumescent coating with a ceramic coating material, Cerakote C-7700Q. Oven and flame tests were designed and conducted to evaluate the heat resistance of Cerakote C-7700Q. The test results show that the currently used painting scheme of the engine cowlings failed the 220°C oven test while after replacing the epoxy seal coat with the Cerakote, the new painting system passed the 220°C test in regards to painting bubbling. Based on that, a new painting scheme with C-7700Q implemented was recommended.

Abstract Fiber-reinforced composites are widely used in injection molding processes because of their high strength and high elastic modulus. However, the addition of reinforcing agents such as glass fibers has a significant impact on their injection molding quality. The difference in shrinkage and hardness between the plastic and the reinforcement will bring about warpage and deformation in the injection molding of the product. At the same time, the glass fibers will be oriented in the flow direction during the injection molding process. This will enhance the mechanical properties in the flow direction and increase the shrinkage in the vertical direction, reducing the molding quality of the product. In this study, a test program was developed based on the Box-Behnken test design in the Design-Expert software, using a plastic part as an example.

Abstract Compared with traditional plastics, glass fiber-reinforced plastic (GFRP) has more outstanding performance advantages, which is more and more widely used. To improve the quality of the products manufactured by the GFRP injection molding, the injection parameters are optimized in two stages. In the first stage, the range of optimization parameters including the glass fiber content and six molding parameters is selected by the Moldflow recommendation. The warpage and shrinkage of each orthogonal experiment are obtained by the Moldflow simulation. Then, a comprehensive evaluation method called GRA-TOPSIS and the range analysis method are utilized to identify the optimal level values of all optimization parameters. According to the order of influence of each parameter, the range of these parameters is adjusted for the second stage.

Abstract Halogen detector is an important halogen gas leakage detection instrument. In order to ensure that the upper and lower shells have the same quality, it is necessary to use one mold and two pieces in production. Compared with the conventional one-mold two-cavity process, it is easier to produce warpage and volume shrinkage. To solve this problem, a multi-objective injection molding process optimization method based on deep neural network (DNN) model based on stochastic weight average (SWA) method and multi-objective evolutionary algorithm based on decomposition (MOEA/D) was proposed. Melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time are the six parameters and important structure parameters (gate diameter) as design variables, warpage, and volume shrinkage rate as the optimization goal. The neural network model between variable and goal was established, and the MOEA/D algorithm was used for global optimization.

Abstract In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN).