• Smart Structures and Systems
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• v.17 no.1
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• pp.135-147
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• 2016

This paper describes a smart structural system, which uses smart materials for real-time monitoring and active control of bolted-joints in steel structures. The goal of this research is to reduce the possibility of failure and the cost of maintenance of steel structures such as bridges, electricity pylons, steel lattice towers and so on. The concept of the smart structural system combines impedance based health monitoring techniques with a shape memory alloy (SMA) washer to restore the tension of the loosened bolt. The impedance-based structural health monitoring (SHM) techniques were used to detect loosened bolts in bolted-joints. By comparing electrical impedance signatures measured from a potentially damage structure with baseline data obtained from the pristine structure, the bolt loosening damage could be detected. An outlier analysis, using generalized extreme value (GEV) distribution, providing optimal decision boundaries, has been carried out for more systematic damage detection. Once the loosening damage was detected in the bolted joint, the external heater, which was bonded to the SMA washer, actuated the washer. Then, the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. Additionally, temperature variation due to the heater was compensated by applying the effective frequency shift (EFS) algorithm to improve the performance of the diagnostic results. An experimental study was conducted by integrating the piezoelectric material based structural health monitoring and the SMA-based active control function on a bolted joint, after which the performance of the smart 'self-monitoring and self-healing bolted joint system' was demonstrated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.833-839
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• 2013

In this study, an experiment was performed to monitor the two-dimensional shape of a cantilever composite structure using fiber Bragg grating (FBG) sensors. To monitor the shape of a composite structure, a deflection equation developed by NASA was applied and a composite beam attached to three FBG sensors was used. In the experiment, the shape of the composite beam was successfully estimated and an error was evaluated by comparing a real deflection. The error increased with real deflection; therefore, it was compensated by using the linear relationship between the error and the real deflection. After compensating the error, the measured deflection shows good agreement with the real deflection. Finally, the experiment shows that the FBG sensor and the deflection equation are suitable for monitoring the deflection curve of the beam structure with compensation of the error.

• The KIPS Transactions:PartA
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• v.17A no.1
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• pp.33-44
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• 2010

We present a pipeline monitoring system based on bio-memetic robot in this paper. A bio-memetic robot exploring pipelines measures temperature, humidity, and vibration. The principal function of pipeline monitoring robot for the exploring pipelines is to recognize the shape of pipelines. We use infrared distance sensor to recognize the shape of pipelines and potentiometer to measure the angle of motor mounting infrared distance sensor. For the shape recognition of pipelines, the number of detected pipelines is used during only one scanning of distance. Three fuzzy classifiers are used for the number of detected pipelines, and the classifying results are presented in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.539-544
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• 1998

Generally, welding gap is a serious factor of a falling-off in weld quality among various kind of weld defect. Welding gap is created between two work piece in GMAW(Gas Metal Arc Welding) of horizontal fillet weld because surface of workpiece is not flat by cutting process. In these days, there were many attempts to detect welding gap. though we prevalently use the vision sensor or arc sensor in welding process, it is difficult to detect welding gap for improvement of welding quality. But we have a trouble to find relationship between welding gap and many welding parameters due to non-linearity of welding process. As mentioned about the various difficult problem, we can detect welding gap precisely using neural networks which are able to model non-linear function. Also, this paper was proposed real-time monitoring of weld bead shape to find effect of welding gap and to estimate weld quality. Monitoring of weld bead shape examined the correlation of welding parameters with bead eometry using learning ability of neural networks. Finally, the developed system, welding gap detecting system and bead shape monitoring system, is expected to the successful capability of automation of welding process by result of simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.613-616
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• 2000

This paper deals with monitoring of grinding wheel wear in surface grinding process. A laser scanning micrometer is used to measure the circumferential shape as well as the axial shape of grinding wheel. The monitoring system is applied to two kinds of grinding methods: plunge and traverse grinding. Through experiments, it is found that measurement of grinding wheel wear reveals information of roughness of ground surface and the adequate dressing time. In addition, monitoring of grinding wheel wear makes it possible to identify abnormal grinding conditions.

• Journal of Ship and Ocean Technology
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• v.8 no.4
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• pp.17-25
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• 2004

Hull girder stress monitoring system for container ship uses four long-base-strain-gages at mid-ship to monitor the resultant stresses and the applied moment components of horizontal, vertical and torsional moments. The bending moments are estimated by using the conventional strain-moment relations, however, the torsional moment related to the warping strain requires the assumption of the shape of torsional moments over the hull girder. Though this shape could be a sine function with an adequate period, it largely depends upon certain empirical formulas. This paper introduces additional four long-base-strain-gages at mid-ship to derive the longitudinal slope of the warping strain because this slope is directly related to the torsional moment by Bi-moment concept. An open-channel-type cantilever beam has been selected as a simplified model for container ship and the result has proved that the suggested concepts can estimate the torsional component accurately. Finally this method can become reliable technique to derive all external moments in hull girder stress monitoring system for container ships.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.595-599
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• 2002

Edge welding of thin sheets is very difficult because of the fit-up problem and small weld area In laser welding, joint fit-up and penetration are critical for sound weld quality, which can be monitored by appropriate methods. Among the various monitoring systems, visual monitoring method is attractive because various kinds of weld pool information can be extracted directly. In this study, a vision sensor was adopted for the weld pool monitoring in pulsed Nd:YAG laser edge welding to monitor whether the penetration is enough and the joint fit-up is within the requirement. Pulsed Nd:YAG laser provides a series of periodic laser pulses, while the shape and brightness of the weld pool change temporally even in one pulse duration. The shutter-triggered and non-interlaced CCD camera was used to acquire a temporally changed weld pool image at the moment representing the weld status well. The information for quality monitoring can be extracted from the monitored weld pool image by an image processing algorithm. Weld pool image contains not only the information about the joint fit-up, but the penetration. The information about the joint fit-up can be extracted from the weld pool shape, and that about a penetration from the brightness. Weld pool parameters that represent the characteristics of the weld pool were selected based on the geometrical appearance and brightness profile. In order to achieve accurate prediction of the weld penetration, which is nonlinear model, neural network with the selected weld pool parameters was applied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1738-1756
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• 2020

To improve the accuracy and realism of the virtual surgical simulation system, this paper proposes an optimized mass-spring model with shape restoration ability based on volume conservation to simulate soft tissue deformation. The proposed method constructs a soft tissue surface model that adopts a new flexion spring for resisting bending and incorporates it into the mass-spring model (MSM) to restore the original shape. Then, we employ the particle swarm optimization algorithm to achieve the optimal solution of the model parameters. Besides, the volume conservation constraint is applied to the position-based dynamics (PBD) approach to maintain the volume of the deformable object for constructing the soft tissue volumetric model base on tetrahedrons. Finally, we built a simulation system on the PHANTOM OMNI force tactile interaction device to realize the deformation simulation of the virtual liver. Experimental results show that the proposed model has a good shape restoration ability and incompressibility, which can enhance the deformation accuracy and interactive realism.

• Structural Engineering and Mechanics
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• v.72 no.2
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• pp.217-227
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• 2019

The methodology known as "shape sensing" allows the reconstruction of the displacement field of a structure starting from strain measurements, with considerable implications for structural monitoring, as well as for the control and implementation of smart structures. An approach to shape sensing is based on the inverse Finite Element Method (iFEM) that uses a variational principle enforcing a least-squares compatibility between measured and analytical strain measures. The structural response is reconstructed without the knowledge of the mechanical properties and load conditions but based only on the relationship between displacements and strains. In order to efficiently apply iFEM to the most common structural typologies of civil engineering, its formulation according to the kinematical assumptions of the Bernoulli-Euler theory is presented. Two beam inverse finite elements are formulated for different loading conditions. Depending on the type of element, the relationship between the minimum number of required measurement stations and the interpolation order is defined. Several examples representing common applications of civil engineering and involving beams and frames are presented. To simulate the experimental strain data at the station points and to verify the accuracy of the displacements obtained with the iFEM shape sensing procedure, a direct FEM analysis of the considered structures is performed using the LUSAS software.

• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.