In this paper, for the first time, the role of manufacturing parameters of fused deposition modeling (FDM) on the shape memory effect (SME) is investigated by design of experiments. PLA-TPU blend with a weight composition of 30:70% is processed by melt mixing and then extruded into 1.75 mm filaments for 3D printing via FDM. SEM images reveal that TPU droplets are distributed in the PLA matrix, and the immiscible matrix-droplet morphology is evident. Box-Behnken design (BBD), as an experimental design of the response surface method (RSM), is implemented to fit the model between variables and responses. The shell, infill density, and nozzle temperature are selected as variables, and their effects on loading stress, recovery stress, shape fixity, and shape recovery ratio are studied in detail. An analysis of variance (ANOVA) is applied to estimate the importance of each printing parameter on the output response and assess the fitness of the presented model. The ANOVA results reveal the high accuracy of the model and the importance of the parameters. Infill density and nozzle temperature had the greatest and least roles on shape memory properties, respectively. Also, the values of shape fixity and shape recovery were obtained in the ranges of 58–100% and 53–91%, respectively. Despite many researches on 4D printing of PLA, low ductility at room temperature and high stress relaxation rate are its weakness, which are covered by adding TPU in this research. Due to the lack of similar outcomes in the specialized literature, this paper is likely to fill the gap in the state-of-the-art problem and supply pertinent data that are instrumental for FDM 3D printing of functional shape memory polymers with less material consumption.
In this study, two types of lumped parameter models (LPMs) are introduced for a vehicle to study frontal crash. A fourdegree-of-freedom (DOF) LPM Hybrid (LH) model is proposed and analysed as a full car model during crash status, which is compared with a serial 4-DOF model. In addition, a 5-DOF LH model is proposed and compared with a serial 5-DOF model. In this investigation, two parameters, including spring and damper coefficients, have been determined so that the proposed models have similar deceleration with the experimental data. These determined parameters have been calculated on the basis of difference minimisation between simulated and experimental results as occurred for occupants in the frontal crash. A genetic algorithm (GA) is implemented for defining a value function to optimise parameters. In other words, the GA is used for system identification to determine the spring and damper parameters for LPMs. Absolute decelerations along with their frequencies due to external load excitation are also studied here.
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