• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.10a
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• pp.121-126
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• 2003

This paper is concerns with the development of the formulae to predict deformation of curved plate due to line heating. For this purpose thermal elasto-plastic analysis has been carried out for both flat and curved plate models with varying parameters which affect the result of line heating. based on the results of numerical analysis, the formulae for predicting angular deformation has been derived through the regression analysis, which. It has been seen that the present model well agrees with the numerical analysis results and can reflect the curvature effect of plate to be heated. This paper ends with some comments on this formulae.

• Special Issue of the Society of Naval Architects of Korea
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• 2005.06a
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• pp.177-184
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• 2005

This paper is concerns with the development of the formulae to predict deformation of curved plate due to line heating. For this purpose thermal elasto-plastic analysis has been carried out for both flat and curved plate models with varying parameters which affect the result of line heating. based on the results of numerical analysis, the formulae for predicting angular deformation has been derived through the regression analysis, which. It has been seen that the present model well agrees with the numerical analysis results and can reflect the curvature effect of plate to be heated. This paper ends with some comments on this formulae.

• Transactions of Materials Processing
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• v.28 no.4
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• pp.190-196
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• 2019

The capability of accurately predicting the roll force profile across a strip in the bite zone in cold rolling process is vital for the calculation of strip profile. This paper presents a derivation of a precision mathematical model for predicting variations in the roll force across a strip in cold rolling. While the derivation is based on an approximate 3-D theory of rolling, this mathematical model also considers plastic deformation in the pre-deformation region which is located close to the roll entrance before the strip enters the bite zone. Finally, the mathematical model is expressed as a boundary value problem, and it predicts the roll force profile and tension profile in addition to lateral plastic strain profile.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.7
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• pp.971-980
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• 2010

In this paper, an artificial neural network (ANN) was employed to build a predicting model for refrigerator structure. The predicting model includes three input variables of the plaque depth (D), width (W) and interval distance(S) on the inner wall. Finite element method was utilized to obtain the data, which would be necessary for the ANN training process. Finally, a genetic algorithm (GA) was applied to find the optimal parameters that leaded to the minimum inner case deformation under operating condition. The optimal combination found is the depth(D) of 2.63mm, the width(W) of 19.24mm and the interval distance(S) of 49.38mm which leaded to the smallest deformation of 1.88mm for the given refrigerator model.

• Steel and Composite Structures
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• v.47 no.4
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• pp.539-550
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• 2023

Owing to the obstruction of section steel on the moisture diffusion in concrete, the existing shrinkage prediction models overestimate the time-dependent deformation of steel reinforced concrete (SRC) columns, particularly for the SRC columns with enclosed section steels. To solve this issue, this study deals with analytical and experimental studies on the drying shrinkage for this type of column. First, an effective method for predicting the drying shrinkage of concrete based on finite element model is introduced and two crucial parameters for simulation of humidity field are determined. Then, the drying shrinkage of SRC columns with enclosed section steels is investigated and two modified parameters, which depend on the ambient relative humidity and the ratio of section steel size to column size, are introduced to the B3 model. Finally, an experiment on the shrinkage deformation of SRC columns with enclosed section steels is conducted. Comparing the predicted results with the experimental ones, it demonstrates that the modified B3 model is quite reasonable.

• Design & Manufacturing
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• v.18 no.2
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• Transactions of Materials Processing
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• v.13 no.2
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• pp.168-173
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• 2004

Microforming can be a good application for bulk metallic glasses. It is important to simulate the deformation behaviour of the bulk metallic glasses in a supercooled liquid region for manufacturing micromachine parts. For these purposes, a correct constitutive model which can reproduce viscosity results is essential for good predicting capability. In this paper, we studied deformation behaviour of the bulk metallic glasses using the finite element method in conjunction with the fictive stress constitutive model which can describe non-Newtonian as well as Newtonian behaviour. A combination of kinetic equation which describes the mechanical response of the bulk metallic glasses at a given temperature and evolution equations fur internal variables provide the constitutive equation of the fictive stress model. The internal variables are associated with fictive stress and relation time. The model has a modular structure and can be adjusted to describe a particular type of microforming process. Implementation of the model into the MARC software has shown its versatility and good predictive capability.

• Korean Journal of Computational Design and Engineering
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• v.20 no.3
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• pp.230-237
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• 2015

Soil deformation on the off-load ground is significantly affected by soil conditions, such as soil type, water content, and etc. Thus, the soil characteristics should be estimated for predicting vehicle movements on the off-load conditions. The plate-sinkage test, a widely-used experimental test for predicting the wheel-soil interaction, provides the soil characteristic parameters from the relationship between soil stress and plate sinkage. In this study, soil stress under the plate-sinkage situation is calculated by the DEM (Discrete Element Method) model. We developed a virtual soil bin with DEM to obtain the vertical reaction forces under the plate pressing the soil surface. Also parametric studies to investigate effects of DEM model parameters, such as, particle density, Young's modulus, dynamic friction, rolling friction, and adhesion, on the characteristic soil parameters were performed.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.5-17
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• 2023

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

• Transactions of Materials Processing
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• v.22 no.8
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• pp.450-455
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• 2013

In the current study, a new dynamic recrystallization model for predicting high temperature flow stress is developed based on a physical model and the mean field theory. In the model, the grain aggregate is assumed as a representative volume element to describe dynamic recrystallization. The flow stress and microstructure during dynamic recrystallization were calculated using three sub-models for work hardening, for nucleation and for growth. In the case of work hardening, a single parameter dislocation density model was used to calculate change of dislocation density and stress in the grains. For modeling nucleation, the nucleation criterion developed was based on the grain boundary bulge mechanism and a constant nucleation rate was assumed. Conventional rate theory was used for describing growth. The flow stress behavior of pure copper was investigated using the model and compared with experimental findings. Simulated results by cellular automata were used for validating the model.