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.

A method for lightweight strength optimization when using dissimilar materials is proposed by applying topology optimization and a design-of-experiment (DOE) to a seat-cushion frame with typical steel. Static analysis of the seat-cushion frame was performed by applying the appropriate safety standards for each passenger. The authors performed topology optimization of the seat-cushion, which was subjected to static analysis and confirmed that the method can be applied to dissimilar materials according to their density ratio as well as the strength of the cushion frame, which was made with dissimilar materials. The strength of the cushion frame made of dissimilar materials is improved compared to the seat-cushion frame made of typical steel. It was also confirmed that the application ratio of the dissimilar materials and the optimization of the application ratio of a dissimilar ratio and the thickness of each material using the DOE can improve the strength of the cushion frame and reduce the weight.

In this paper, the authors proposed an optimal design method for the strength design of infant pop-up seat frame combined with rear seats for infants, children, and adults, not removable booster seats or car seats. Frame strength design was performed using discrete material and thickness optimization (DMTO) method considering high strength steel (HSS) and advanced high strength steel (AHSS). Structural design using the Section 4 link mechanism was performed, and the weakness of the seat frame due to static load was confirmed through finite element analysis. An optimal design criterion was established by carrying out a case study to derive the limiting conditions according to static and dynamic loads. In consideration of these criteria, the optimal design according to d-optimal and discrete Latin-hypercube (DLH) was performed among the design of experiments (DOE). And the strength of the pop-up seat frame for infants according to each DOE was checked, and the strength optimization method was suggested by comparing the lightweight ratio.