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RISS 인기검색어
- 검색키워드
- 저자 : D. Schramm (검색결과 4 건)
- 무료
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Y. S. Kong,S. ABDULLAH,D. Schramm,M. Z. Omar,S. M. Haris 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.11
This paper presents the optimisation of spring fatigue life based on an artificial neural network (ANN) architecture and particle swarm optimisation algorithm (PSO) using ISO 2631 vertical vibration as input. The road-induced vibration of a ground vehicle caused the spring to fail due to fatigue and human discomfort. Hence, there is a need to model the relationship between these two parameters for spring design assistance. Vibration and force signals were extracted from a quarter car model simulation for fatigue life and ISO 2631 vertical vibration estimations. PSO was applied to the datasets for ANN weights and biases adjustments while the mean squared error (MSE) was set as the objective function. For validation purposes, a set of independent datasets was applied to the ANN. The residuals were analysed using Lilliefors normality and error histogram. For prediction accuracy, the predicted fatigue lives were analysed using scatter band approach and compared with traditional trained ANN. The results have shown that most of the PSO-based ANN predicted fatigue lives were in the acceptable region and the root mean square error (RMSE) value of 0.6391 life cycles in natural logarithm was obtained. The PSO-based ANN has shown improved performance compared to the conventional ANN approach in predicting fatigue life.
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Carl L. Keen,John A. Polagruto,Derek D. Schramm,Janice F. Wang-Polagruto,Luke Lee 한국식품영양과학회 2003 Journal of medicinal food Vol.6 No.4
Diets rich in flavonoids have been associated with reduced risk for cardiovascular disease. This may be due,in part, to flavonoid-induced alterations in eicosanoid synthesis. Our objective was to identify plant-derived beverages that al-ter synthesis of prostacyclin in cultured human aortic endothelial cells (HAEC), and to determine if these beverages could al-ter in vivo6-keto-prostaglandin F 1a (a stable metabolite of prostacyclin) synthesis and platelet function. HAEC were treatedwith nine commonly consumed beverages to determine their effects on prostacyclin synthesis under acute and chronic treat-ment regimens. Orange, purple grape, and pomegranate juices and coffee (6 9 mL/kg) were then provided to 28 fasted, healthyadult subjects (eight men and 20 women) on five separate days. Plasma samples were collected immediately following juiceconsumption (baseline), and at 2 and 6 hours post-consumption. On an acute basis, administration of HAEC with pomegran-ate juice increased media prostacyclin. Chronic exposure to purple grape and pomegranate juice increased aortic endothelialcell prostacyclin synthesis (38% and 61%, respectively; P, .05). The consumption of purple grape, pomegranate, and orangejuice prolonged epinephrine/collagen-induced clotting time (P, .05). Purple grape juice increased plasma 6-keto-prostaglandinF1a (20%; P, .05) at 2 hours; pomegranate and orange juice did not significantly influence plasma prostacyclin concentra-tions. Consistent with the in vitrodata, coffee consumption did not influence clotting time or plasma prostacyclin concentra-tions. These results indicate that the HAEC model system can provide a qualitative means to screen food and food-derivedproducts for biologic activity related to cardiovascular health.
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Neuro-fuzzy fatigue life assessment using the wavelet-based multifractality parameters
C. H. Chin,S. ABDULLAH,S.S.K. Singh,A. K. ARIFFIN,D. Schramm 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.2
This study aims to establish a fatigue life predictive model based on multifractality of road excitations using neuro-fuzzy method to assess the durability of suspension spring. Traditional durability analysis in time domain is complicated and time-consuming due to the needs of large data amount. Thus, it is an idea to adopt an adaptive neuro-fuzzy inference system (ANFIS) for relating the performance of coil spring to the multifractal properties of road excitations, giving a meaningful fatigue life prediction. Different membership function numbers were tested to obtain the optimum membership function number. During the data training process, the checking data was used to test the trained model each Epoch of training for overfitting detection. As a result, the Morrow-based fatigue life prediction model was found to give the most suitable result with three membership functions. The SWT-based model needed five membership functions due to nonlinear properties in the SWT-based fatigue life data. Training process of Morrow-based-ANFIS was stopped at Epoch 8 given its lowest checking root-meansquare-error of 0.6953. SWT-based model recorded a higher error of 0.7940. The neuro-fuzzy models gave accurate fatigue life predictions with 96 % of the data distributed within the acceptance boundary, hence, contributing to an acceptable assessment of coil spring fatigue life based on load multifractality. This study had shown a nonlinear relationship between road multifractality and durability performance of coil spring. Multifractality had been proven an important feature to characterise various road excitations for durability prediction.
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C. H. Chin,S. ABDULLAH,S.S.K. Singh,A. K. ARIFFIN,D. Schramm 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.1
This paper aims to assess the fatigue reliability of random loading signals of a suspension coil spring using probabilistic approaches. Strain time histories were acquired while the car was travelling on different road conditions (i.e., in a rural area, in an industrial area, on a university campus, on a highway and on a newly constructed road). Fatigue lives were predicted from the strain histories and fitted into probability density functions. Lognormal distribution was found to be an appropriate way to represent fatigue data. Next, the reliability function and mean-cycles-to-failure (MCTF) were determined. The results indicated that fatigue reliability rapidly deteriorated under rural road conditions, which resulted in a short MCTF of 10 4 cycles. Meanwhile, the new road signals had the longest MCTF of about 10 8 cycles. Accordingly, this is due to the rural road having the most surface irregularities, which caused more severe fatigue damage to the coil spring. This study contributed to a greater in-depth understanding of the effect of loading signals on fatigue reliability. This is essential in determining the appropriate service life of the coil spring during its production to ensure vehicle safety and reduce maintenance costs.
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