• 대한기계학회논문집A
• /
• 제33권12호
• /
• pp.1417-1426
• /
• 2009

Various types of damper are usually applied to reduce noise and vibration for mechanical systems. Especially, for washing machines, the free-friction stroke damper is installed. The behavior of the free-friction stroke damper has nonlinear characteristics such as hysteresis and viscoelastic properties because of its foam material. First of all, the dynamic experiments were carried out by using a MTS machine to find characteristics of the free-friction stroke damper. And the simulation model of the free-friction stroke damper and characteristics of a foam material were evaluated by using optimization technique. To make a good simulation model which can show the dynamic characteristics, it is important to understand the working mechanism of the damper. The Finite Element Method (FEM) technique can help us instinctively understand the damping phenomenon under operating conditions, because we can observe the condition of damper at every step in the simulation by using it. Also, by changing factors, we can comprehend the variation of characteristics of damper. So, in this paper, a study on the dynamic characteristics of free-friction stroke damper by FEM is focused on. Finally, the possibility which physical experiments can be replaced into simulations is shown.

• Geomechanics and Engineering
• /
• 제30권3호
• /
• pp.259-272
• /
• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

• Structural Engineering and Mechanics
• /
• 제62권1호
• /
• pp.1-9
• /
• 2017

This paper describes laboratory tests carried out to evaluate the influence of class F fly ash (FA) on fracture toughness of plain concretes, specified at the third model fracture. Composites with the additives of: 0%, 20% and 30% siliceous FA were analysed. Fracture toughness tests were performed on axial torsional machine MTS 809 Axial/Torsional Test System, using the cylindrical specimens with dimensions of 150/300 mm, having an initial circumferential notch made in the half-height of cylinders. The studies examined effect of FA additive on the critical stress intensity factor $K_{IIIc}$. In order to determine the fracture toughness $K_{IIIc}$ a special device was manufactured.The analysis of the results revealed that a 20% FA additive causes increase in $K_{IIIc}$, while a 30% FA additive causes decrease in fracture toughness. Furthermore, it was observed that the results obtained during fracture toughness tests are convergent with the values of the compression strength tests.

• Steel and Composite Structures
• /
• 제19권1호
• /
• pp.223-236
• /
• 2015

Despite the proliferation of the industrial application of steel tubes, the effect of collision on the surface of steel tubes subject to cyclic loading has largely remained untouched. This paper studies the fatigue behavior of steel tubes which are impacted by an external object. A dent imperfection caused by a collision was modeled and fatigue tests were conducted using a MTS machine. Fatigue life as well as the failure modes were thoroughly discussed in a way that the fatigue life of the dented tubes with similar geometrical specifications at full-scale can be generalized.

• 대한기계학회논문집
• /
• 제3권4호
• /
• pp.133-142
• /
• 1979

The fracture toughness of a hot rolled 100 mm thick SS41 steel plate was investigated for various crack ratios and thichnesses using the method of J-integral. The experiments were performed on an MTS machine and the crack initiation point was detected by using an electricl impedance method. The J-integral computed at the initiation point of the slow stable crack growth was almost constant within the range of crack ratios tested. The fracture toughness thus obtained was $J_{1c}/=27.0kgf/mm$ for specimens having fracture plane parallel to the rolling direction and 35.5kgf/mm for those perpendicular to the rolling direction. The J- integral computed at maximum load point was found to be unsuitable for fracture toughness determination, becaese of large variation depending on the crack ratio and thickness. It was also found that the slow stable crack growth increases as the thickness and/or crack ration of the specimen decrease.

• 한국자동차공학회논문집
• /
• 제15권5호
• /
• pp.48-55
• /
• 2007

A bushing component of a vehicle suspension system is tested to capture the nonlinear behavior of rubber bushing element using the MTS 3-axes rubber test machine. The results of the tests are used to model the artificial neural network bushing model. The performances from the neural network model usually are dependent on the structure of the neural network. In this paper, maximum error, peak error, root mean square error, and error-to-signal ratio are employed to evaluate the performances of the neural network bushing model. A simple simulation is carried out to show the usefulness of the developed procedure.

• 한국자동차공학회논문집
• /
• 제14권2호
• /
• pp.158-165
• /
• 2006

In this paper, a new uni-axial bushing model for vehicle dynamics analysis is proposed. Bushing components of a vehicle suspension system are tested to capture the nonlinear and hysteric behavior of the typical rubber bushing elements using the MTS machine. The results of the tests are used to develop the Bouc-Wen bushing model. The Bouc-Wen model is employed to represent the hysteretic characteristics of the bushing. ADAMS program is used for the identification process and VisualDOC program is also used to find the optimal coefficients of the model. Genetic algorithm is employed to carry out the optimal design. A numerical example is suggested to verify the performance of the proposed model.

• International Journal of Automotive Technology
• /
• 제8권1호
• /
• pp.111-117
• /
• 2007

The nonlinear characteristics of a suspension is directly related to the ride quality of a passenger car. In this study, the nonlinear characteristics of a spring and a damper of a passenger car is analyzed by dynamic experiments using the MTS single-axial testing machine. Also, a mathematical nonlinear dynamic model for the suspension is devised to estimate the ride quality using the K factor. And the effect on the variation of the parameters of the suspension is examined. The results showed that the dynamic viscosity of the oil in a damper was the parameter that most influeced the ride quality of a passenger car for the ride quality of a passenger car.

• The Journal of Advanced Prosthodontics
• /
• 제3권1호
• /
• pp.1-9
• /
• 2011

PURPOSE. This study investigated the strain of implants using a chewing simulator with strain gauges in mandibular implant-supported fixed prostheses under various dynamic loads. MATERIALS AND METHODS. Three implant-supported 5-unit fixed prostheses were fabricated with three different occlusion types (Group I: Canine protected occlusion, Group II: Unilaterally balanced occlusion, Group III: Bilaterally balanced occlusion). Two strain gauges were attached to each implant abutment. The programmed dynamic loads (0 - 300 N) were applied using a chewing simulator (MTS 858 Mini Bionix II systems, MTS systems corp., Minn, USA) and the strains were monitored. The statistical analyses were performed using the paired t-test and the ANOVA. RESULTS. The mean strain values (MSV) for the working sides were 151.83 ${\mu}{\varepsilon}$, 176.23 ${\mu}{\varepsilon}$, and 131.07 ${\mu}{\varepsilon}$ for Group I, Group II, and Group III, respectively. There was a significant difference between Group II and Group III (P < .05). Also, the MSV for non-working side were 58.29 ${\mu}{\varepsilon}$, 72.64 ${\mu}{\varepsilon}$, and 98.93 ${\mu}{\varepsilon}$ for Group I, Group II, and Group III, respectively. One was significantly different from the others with a 95% confidence interval (P < .05). CONCLUSION. The MSV for the working side of Groups I and II were significantly different from that for the non-working side (Group I: t = 7.58, Group II: t = 6.25). The MSV for the working side of Group II showed significantly larger than that of Group III (P < .01). Lastly, the MSV for the non-working side of Group III showed significantly larger than those of Group I or Group II (P < .01).

• 한국융합학회논문지
• /
• 제12권6호
• /
• pp.9-16
• /
• 2021

과거 주가 데이터와 금융 관련 빅 데이터를 사용해 머신러닝 기법으로 주식시장을 예측하는 연구는 다양하게 있어 왔지만, HTS와 MTS를 통해 거래가 가능한 주가지수 연동 ETF가 생기면서 주가지수를 예측하는 연구가 최근 주목받고 있다. 본 논문에서는 KOSPI 연동 ETF를 거래할 목적으로 KOSPI의 상승 예측을 위한 머신러닝 모델과 하락예측을 위한 모델을 각각 구현한다. 이들 모델은 매개변수의 그리드 탐색을 통해 최적화 된다. 또한 정밀도를 개선해 ETF 거래 수익률을 높일 수 있도록 개별 모델들을 조합한 하이브리드 머신러닝 모델을 제안한다. 예측 모델의 성능은 정확도와 ETF 거래 수익률에 큰 영향을 미치는 정밀도로 평가된다. 하이브리드 상승 예측 모델의 정확도와 정밀도는 72.1 %와 63.8 %이고 하락 예측 모델은 79.8 %와 64.3 %이다. 하이브리드 하락 예측 모델에서 정밀도는 개별 모델보다 최소 14.3 %, 최대 20.5 % 개선되었다. 테스트 기간에 하이브리드 모델은 하락에서 10.49 %, 상승에서 25.91 %의 ETF 거래 수익률을 보였다. 인버스×2와 레버리지 ETF로 거래하면 수익률을 1.5 ~ 2배로 높일 수 있다. 하락예측 머신러닝 모델에 대한 추가 연구로 수익률을 더 높일 수 있을 것으로 기대한다.