• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1417-1426
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• 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
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• v.30 no.3
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• pp.259-272
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• 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
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• v.62 no.1
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• pp.1-9
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• 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
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• v.19 no.1
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• pp.223-236
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• 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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.3 no.4
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• pp.133-142
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• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.5
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• pp.48-55
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• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.2
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• pp.158-165
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• 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
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• v.8 no.1
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• pp.111-117
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• 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
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• v.3 no.1
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• pp.1-9
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• 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).

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.