• 대한기계학회논문집A
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• 제36권8호
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• pp.945-951
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• 2012

본 논문에서는 하부 프레임 구조가 보다 단순해진 분말류 운송차량인 벌크시멘트 트레일러의 정동적 특성을 유한요소해석을 통하여 분석하였다. 이를 위하여 벌크시멘트 트레일러가 받는 하중의 대부분을 지지하는 섀시 프레임과 탱크부분을 상용 유한요소해석 소프트웨어인 ANSYS를 이용하여 삼차원 상세 유한요소모델링을 수행하였다. 자유진동해석을 통하여 차체의 동적특성을 이해하는데 필수적인 벌크시멘트 트레일러 몸통의 고유진동수와 진동모드를 분석하였다. 또한, 정적인 응력해석을 통하여 트레일러의 취약부위를 찾은 후 다구찌 실험계획법을 적용하여 경량화를 시키면서도 취약부위의 강도를 높일 수 있는 방안을 제시하였다.

• 센서학회지
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• 제32권3호
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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%).