This study investigates the effect of annealing on the mechanical properties of fused deposition modeling (FDM) 3D-printed recycled carbon fiber (rCF)-reinforced composites. In this study, filaments for FDM 3D printers are self-fabricated from pure acrylonitrile butadiene styrene (ABS) and ABS reinforced with fiber content of 10 wt% and 20 wt% rCF. This study explores the tensile and flexural properties as a function of the annealing temperature and time for the three different fiber content values. In addition, dimensional measurements of the shape changes are performed to determine the suitability of applying annealing in practical manufacturing processes. The results show that annealing improves the mechanical properties by narrowing the voids between the beads, which occur during the FDM process, and by reducing the gaps between the fibers and polymer. Following annealing, the largest tensile and flexural strength improvements are 12.64% and 42.33%, respectively, for the 20 wt% rCF content samples. Moreover, compared with the pure ABS samples, the annealing effect improves the mechanical properties of the rCF-reinforced samples more effectively, and they have higher dimensional stability, indicating their suitability for annealing. These results are expected to expand the application fields of rCF and greatly increase the potential use of FDM-printed parts.

Among the composite manufacturing methods, injection molding has higher time efficiency and improved processability. The production of composites via injection molding requires a pre-process to mix and pelletize the matrix polymer and reinforcement material. Herein, we studied the effect of extrusion process conditions for making pellets on the mechanical and thermal properties provided by injection molding. Polyamide 6 (PA6) was used as the base, and composites were produced by blending carbon fibers and Al₂O₃ as the filler. To determine the optimum blending ratio, the mechanical properties, thermal conductivity, and melt flow index (MI) were measured at various blending ratios. With this optimum blending ratio, pellets were produced by changing the temperature and RPM conditions, which are major process variables during compounding. Samples were fabricated by applying the same injection conditions, and the mechanical strength, MI values, and thermal properties were measured. The mechanical strength increased slightly as the temperature and RPM increased, and the MI and thermal conductivity also increased. The results of this study can be used as a basis for specifying the conditions of the mixing and compounding process such that the desired mechanical and thermal properties are obtained.

A slurry pump is a device used to transport slurry, which is a mixture of solids and liquids. The slurry pump is subjected to physical erosion, generally resulting from erosion by friction between the solid and liquid particles. This study aimed to analyze the effects of process parameters on the erosion wear of a throat bush, which is the main component of a slurry pump. The erosion rate density (E) was analyzed based on the process parameters, that is, the slurry particle diameter, rotation speed of the impeller, and gap between the impeller and the throat bush. The discrete phase model (DPM) of the slurry pump was simulated using the process parameters. These parameters were optimized to minimize the erosion rate density. The optimization method using design of experiments (DOE) to derive a specific location with the greatest influence of the design variables through the one-way layout method and optimize the response value at that location is a method that efficiently analyzes a large number of cases. The optimization results confirmed that the erosion rate density was reduced in the optimization model compared with the earlier model.

Defining a passenger’s thermal comfort in a car cabin is difficult because of the narrow environment and various parameters. Although passenger comfort is predicted using a thermal-comfort scale in the overall cabin or a local area, the scale’s range of passenger comfort may differ owing to psychological factors and individual preferences. Among the many factors affecting such comfort levels, the temperature of the seat is one of the direct and significant environmental factors. Therefore, it is necessary to predict the cabin environment and seat-related personal thermal comfort. Accordingly, machine learning is used in this research to predict whether a passenger’s seat-heating-operation pattern can be predicted in a winter environment. The experiment measures the ambient factor and collects data on passenger heating-operation patterns using a device in an actual winter environment. The temperature is set as the input parameter in the measured data and the operation pattern is used as the output parameter. Based on the parameters, the predictive accuracy of the heating-operation pattern is investigated using machine learning. The algorithms used in the machine-learning train are Tree, SVM, and kNN. In addition, the predictive accuracy is tested using SVM and kNN, which shows a high validation accuracy based on the prediction results of the algorithm. In this research, the parameters predicting the personal thermal comfort of three passengers are investigated as a combination of input parameters, according to the passengers. As a result, the predictive accuracy of the operation pattern according to the tested input parameter is 0.96, showing the highest accuracy. Considering each passenger, the predictive accuracy has a maximum deviation of 30%. However, we verify that it indicates the level of accuracy in predicting a passenger’s heating-operation pattern. Accordingly, the possibility of operating a heating seat without a switch operation is confirmed through machine learning. The primary-stage research result reveals whether it is possible to predict objective personal thermal comfort using the passenger seat’s heating-operation pattern. Based on the results of this research, it is expected to be utilized for system construction based on the AI prediction of operation patterns according to the passenger through machine learning.

Ethylene propylene diene monomer (EPDM) has superior mechanical properties, water resistance, heat resistance, and ozone resistance. It can be applied to various products owing to its low hardness and high slip resistance properties. A grommet is one of the various products made using EPDM rubber. It is a main component of automobiles, in which it protects wires throughout the inside and outside of a vehicle body. The grommet, made of EPDM, has different mounting performance depending on the process parameters and the shape of the grommet. This study conducted optimization to improve the mounting performance of a grommet using EPDM materials. The physical properties of the main molding materials were investigated according to process parameters. A grommet was fabricated according to the process parameters of fabrication. Insertion force and separation force were examined through experiments. Nonlinear material constants were determined through uniaxial and biaxial tensile tests. The nonlinear analysis of the grommet was conducted, and a compound design that incorporated the shape parameters for the minimum load of each part was derived. Then, additional nonlinear analysis was performed. This was followed by a comparative analysis of the actual model through experimental evaluation.