• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.945-951
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• 2012

In this study, we analyze the static and dynamic characteristics of a bulk-cement trailer with a simpler structure that carries powders. The commercial software ANSYS is used to prepare a detailed three-dimensional model of the chassis frame and tank body that bear most of the load of a bulk-cement trailer for the finite element analysis. Modal analysis is conducted to examine the dynamic characteristics of the trailer body, and static analysis shows weak links in the structure. Finally, we propose a method to increase the strength of vulnerable areas and to reduce the weight of the trailer by applying the Taguchi method.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.174-179
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• 2023

Bulk trailers, used for the transportation of powdered materials, such as cement and fly ash, are crucial in the construction industry. The speedy exhaustion of powdered materials stored in the tank of bulk trailers is relevant to improving transportation efficiency and reducing transportation costs. The exhaust time can be reduced by developing an automatic control system to replace the manual exhaust operation. The instantaneous or accumulated exhausts of powdered materials must be measured for automatic control of the bulk trailer exhaust system. Accordingly, we previously proposed a recurrent neural network (RNN) model that estimated the instantaneous exhaust based on low-cost pressure sensor signals without an expensive flowmeter for powders. Although our previous study utilized only an RNN model, models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are also widely utilized for time-series estimation. This study compares the performance of three neural network models (MLP, CNN, and RNN) in estimating instantaneous and accumulated exhausts. In terms of the instantaneous exhaust estimation, the difference in the performance of neural network models was insignificant (that is, 8.64, 8.62, and 8.56% for the MLP, CNN, and RNN, respectively, in terms of the normalized root mean squared error). However, in the case of the accumulated exhaust, the performance was excellent in the order of CNN (1.67%), MLP (2.03%), and RNN (2.20%).