• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.3
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• pp.81-87
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• 2007

It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. However, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis and artificial neural network were used as the research methods. And, the results of two prediction methods were compared and analyzed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.84-89
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• 2007

This research was done on the basis of assumption that there is a relationship between welding parameters and geometry of the back-bead being a gap in arc welding. Multiple regression analysis was used as method for predicting the geometry of the back-bead. The analysis data and the verification data were used for the formation of multiple regression analysis. The method was used to perform the prediction of the back-bead.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.79-86
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• 2007

The shape of excessive penetration mainly depends on welding conditions(welding current and welding voltage), and welding process(groove gap and welding speed). These conditions are the major affecting factors to width and height of back bead. In this paper, back-bead prediction and weldability estimation using artificial neural network were investigated. Results are as follows. 1) If groove gap, welding current, welding voltage and welding speed will be previously determined as a welding condition, width and height of back bead can be predicted by artificial neural network system without experimental measurement. 2) From the result applied to three weld quality levels(ISO 5817), both experimented measurement using vision sensor and predicted mean values by artificial neural network showed good agreement. 3) The width and height of back bead are proportional to groove gap, welding current and welding voltage, but welding speed. is not.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.201-208
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• 2000

In this study, the behavior of saturated sandy soils under dynamic loads - pore water pressure and effective stress - was investigated using Disturbed State Concept(DSC) model. The model parameters are evaluated from laboratory test data. During the process of loading and reverse loading, DSC model is utilized to trace strain-hardening and cyclic softening behavior. The procedure of back prediction proposed in this study are verified by comparing with laboratory test results. From the back prediction of pore water pressure and effective mean pressure under cyclic loading, excess pore water pressure increases up to initial effective confining pressure and effective mean pressure decrease close to zero in good greement with laboratory test results. Those results represent the liquefaction of saturated sandy soils under dynamic loads. The number of cycles at initial liquefaction using the model prediction is in good agreement with laboratory test results. Therefore, the results of this study state that the liquefaction of saturated sandy soils can be explained by the effective tress analysis.

• Food Engineering Progress
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• v.14 no.1
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• pp.75-79
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• 2010

Partial least square regression (PLSR) was executed on curve data of force-deformation from back extrusion test and sensory data for commercial instant noodles. Sensory attributes considered were hardness (A), springiness (B), roughness (C), adhesiveness to teeth (D), and thickness (E). Eight and two kinds of fried and non-fried instant noodles respectively were used in the tests. Changes in weighted regression coefficients were characterized as three stages: compaction, yielding, and extrusion. Correlation coefficients appeared in the order of E>D>A>B>C, root mean square error of prediction D>C>E>B>A, and relative ability of prediction D>C>E>B>A. Overall, 'D' was the best in the correlation and prediction. 'A' with poor prediction ability but high correlation was considered good when determining the order of magnitude.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.871-877
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• 2008

It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. Howeve, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis for the prediction of process parameters was used as the research method. And, the results of the prediction method were compared and analyzed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.1
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• pp.74-80
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• 2011

In this study, a variety of welding experiments were carried out to optimize root-pass welding process using GMA process. Based on the experimental results, optimal welding conditions were selected after analyzing correlation between welding parameters and back-bead geometry. Then, effectiveness of empirical models developed was compared and analyzed, and optimized empirical models were finally developed for predicting back-bead by analyzing the main effect of each factor which affects back-bead geometry and their influence on interaction. Also, functions proper for expressing the surface of back-bead were selected using diverse quadratic functions, and back-bead geometry was visualized using empirical models developed and quadratic functions.

• Structural Engineering and Mechanics
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• v.65 no.4
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• pp.447-452
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• 2018

The controlling and prediction of spring back is one of the most important factors in sheet metal forming processes which require high dimensional precision. The relationship between effective parameters and spring back phenomenon is highly nonlinear and complicated. Moreover, the objective function is implicit with regard to the design variables. In this paper, first the influence of some effective factors on spring back in U-die bending process was studied through some experiments and then regarding the robustness of artificial neural network (ANN) approach in predicting objectives in mentioned kind of problems, ANN was used to estimate a prediction model of spring back. Eventually, the spring back angle was optimized using the Imperialist Competitive Algorithm (ICA). The results showed that the employment of ANN provides us with less complicated and time-consuming analytical calculations as well as good results with reasonable accuracy.

• Safety and Health at Work
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• v.2 no.3
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• pp.236-242
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• 2011

Objectives: To determine the feasibility of predicting static and dynamic peak back-compressive forces based on (1) static back compressive force values at the lift origin and destination and (2) lifting speed. Methods: Ten male subjects performed symmetric mid-sagittal floor-to-shoulder, floor-to-waist, and waist-to-shoulder lifts at three different speeds (slow, medium, and fast), and with two different loads (light and heavy). Two-dimensional kinematics and kinetics were captured. Linear regression analyses were used to develop prediction equations, the amount of predictability, and significance for static and dynamic peak back-compressive forces based on a static origin and destination average (SODA) backcompressive force. Results: Static and dynamic peak back-compressive forces were highly predicted by the SODA, with R2 values ranging from 0.830 to 0.947. Slopes were significantly different between slow and fast lifting speeds (p < 0.05) for the dynamic peak prediction equations. The slope of the regression line for static prediction was significantly greater than one with a significant positive intercept value. Conclusion: SODA under-predict both static and dynamic peak back-compressive force values. Peak values are highly predictable and could be readily determined using back-compressive force assessments at the origin and destination of a lifting task. This could be valuable for enhancing job design and analysis in the workplace and for large-scale studies where a full analysis of each lifting task is not feasible.

• Geomechanics and Engineering
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• v.36 no.1
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• pp.83-97
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• 2024

It is important to make reasonable prediction about the long-term deformation of high rockfill geostructures. However, the deformation is usually underestimated using the rockfill parameters obtained from laboratory tests due to different size effects, which make it necessary to identify parameters from in-situ monitoring data. This paper proposes a novel hybrid back-analysis method with a modified objective function defined for the time-dependent back-analysis problem. The method consists of two stages. In the first stage, an improved weighted average method is proposed to quickly narrow the search region; while in the second stage, an adaptive response surface method is proposed to iteratively search for the satisfactory solution, with a technique that can adaptively consider the translation, contraction or expansion of the exploration region. The accuracy and computational efficiency of the proposed hybrid back-analysis method is demonstrated by back-analyzing the long-term deformation of two high embankments constructed for airport runways, with the rockfills being modeled by a rheological model considering the influence of stress states on the creep behavior.