• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2004년도 학술대회지
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• pp.306-311
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• 2004

The sliding wear characteristics of the crane sheave were investigated using a pin-on-disk rig tester The experiment was conducted using a high carbon steel wire that was upper material, also carbon steel castings and carbon steel for machine structural use that was disk material. There are various operating conditions in this work. At the room temperature, we carried out the wear test under a grease lubrication. The results of wear test showed that carbon steel for machine structural use have lower wear volume, also the wear curves are linearly increased with increasing of sliding velocity The wear mechanism of a disk is the abrasive, adhesion, and fatigue wear under lubrication.

• Structural Engineering and Mechanics
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• 제66권3호
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• pp.399-409
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• 2018

This paper demonstrates the potential application of machine learning algorithms for approximate prediction of the load and deflection capacities of the novel type of Laced Steel Concrete-Composite (LSCC) beams proposed by Anandavalli et al. (Engineering Structures 2012). Initially, global and local responses measured on LSCC beam specimen in an experiment are used to validate nonlinear FE model of the LSCC beams. The data for the machine learning algorithms is then generated using validated FE model for a range of values of the identified sensitive parameters. The performance of four well-known machine learning algorithms, viz., Support Vector Regression (SVR), Minimax Probability Machine Regression (MPMR), Relevance Vector Machine (RVM) and Multigene Genetic Programing (MGGP) for the approximate estimation of the load and deflection capacities are compared in terms of well-defined error indices. Through relative comparison of the estimated values, it is demonstrated that the algorithms explored in the present study provide a good alternative to expensive experimental testing and sophisticated numerical simulation of the response of LSCC beams. The load carrying and displacement capacity of the LSCC was predicted well by MGGP and MPMR, respectively.

• Tribology and Lubricants
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• 제22권2호
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• pp.73-78
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• 2006

The surface modification of automobile parts is of great technological importance for the improvement of corrosion resistance, wear resistance, fatigue strength and so on. Recently, research on the development of the technology of surface modification substituting 6-balance chrome process has progressively been achieved in automobile parts. Although the innovation technology for the improvement of the corrosion-resisting and wear resistant properties through post oxidation after nitrocarburising process had attracted a great attention. For this, anodically potentiodynamic polarisation testing was carried out to corrosion resistance and friction and wear experiment according to applied load and sliding distance was carried out to evaluate the wear resistance of machine structural steel with nitrocarburising and non-nitrocarburising SM45C. The presumed wear volume was calculated with the image processing far evaluation of wear resistance of two materials. The results show that the nitrocarburising had a distinguished corrosion resistance and wear resistance than non-nitrocarburising.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2012년도 춘계 학술논문 발표대회
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• pp.325-326
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• 2012

Because steel frame has own characteristics as easy to work and structural safety, it is being increased application by the trend of construction industry has been more higher and larger in today. However, steel frame works have potential problem, so fundamental solution is needed for preventing serious accidents. Recently, self-supported steel joint for enhancing safety is developed in Korea, but this system has some limitations as convenience of work, retainment of consistent productivity. For complementing this limitations, we developed the new system named Automated wire control machine. This study is performed productivity of steel frame work by new system. The basis data for analysing productivity is collected from field test.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2001년도 추계학술발표대회 개요집
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• pp.134-139
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• 2001

This paper describes the weldability of JIS S45C medium carbon steel (same material with KS SM45C and SAE 1045) for machine structural use by Nd:YAG laser. This material have a limitation to the industrial application in spite of good mechanical characteristics. This is due to its difficult welding work from high carbon contents. We therefore have investigated laser weldability of this material to extend the application of medium carbon steel. The results of this study provide application possibility of Nd:YAG laser welding for medium carbon steel.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.91-99
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• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

• Computers and Concrete
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• 제33권6호
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• 한국정밀공학회지
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• 제22권2호
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• pp.172-179
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• 2005

A high-precision centerless grinding machine has been recognized as a core equipment performing the finish outer-diameter grinding process of ferrules which are widely used as fiber optic connectors. In this study, in order to realize the high-precision centerless grinding machine, the structural characteristic analysis and evaluation are carried out on the virtual prototype consisted of the steel bed, hydrostatic GW and RW spindle systems, hydrostatic RW feed mechanism, RW swivel mechanism, and on-machine GW and RW dressers. The loop stiffnesses of centerless grinding machine are estimated based on the relative deformations between GW and RW caused by the grinding forces. And the simulated results illustrate that the concrete-filled bed has the considerable effect on the improvement of the structural stiffness of centerless grinding machine.

• 한국전산구조공학회논문집
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• 제37권1호
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• pp.57-65
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• 2024

내화 구조물에서는 환기 계수, 재료 탄성 계수, 항복 강도, 열팽창 계수, 외력 및 화재 위치에서 불확실성이 관찰된다. 환기 불확실성은 화재 온도에 영향을 미치고, 이는 다시 구조물 온도에 영향을 미친다. 이러한 온도는 재료 특성과 함께 불확실한 구조적 응답으로 이어지고 있다. 화재 시 구조적 비선형 거동으로 인해 몬테카를로 시뮬레이션을 사용하여 화재 취약성을 계산하는데, 이는 시간이 많이 소요된다. 따라서 머신러닝 알고리즘을 활용해 화재 취약성 분석을 예측함으로써 효율성을 높이고 정확성을 확보하려는 연구가 진행되고 있다. 이 연구에서는 화재 크기, 위치, 구조 재료 특성의 불확실성을 고려하여 철골 모멘트 골조 건물의 화재 취약성을 예측했다. 화재 시 비선형 구조 거동 결과를 기반으로 한 취약성 곡선은 로그 정규 분포를 따른다. 마지막으로 제안한 방법이 화재 취약성을 정확하고 효율적으로 예측할 수 있음을 보여주었다.

• Steel and Composite Structures
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• 제49권4호
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• pp.419-430
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• 2023

This paper is aimed at investigating the effect of multiple longitudinal stiffeners on the patch loading resistance of slender steel plate girders. Firstly, a numerical study is conducted through geometrically and materially nonlinear analysis with imperfections included (GMNIA), the model is validated with experimental results taken from the literature. The structural responses of girders with multiple longitudinal stiffeners are compared to the one of girders with a single longitudinal stiffener. Thereafter, a patch loading resistance model is developed through machine learning (ML) using symbolic regression (SR). An extensive numerical dataset covering a wide range of bridge girder geometries is employed to fit the resistance model using SR. Finally, the performance of the SR prediction model is evaluated by comparison of the resistances predicted using available formulae from the literature.