• 한국철도학회:학술대회논문집
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• 한국철도학회 2005년도 추계학술대회 논문집
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• pp.258-263
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• 2005

The purpose of this research is to present a practical method for efficiently monitoring a reliability growth process using AMSAA(Army Materiel Systems Analysis Activity) reliability growth model. The presented method is viable for identifying failure modes, incorporating design changes and monitoring reliability progress on an on-going basis during the early stages of a product development program. According to the Application Guide for EN 50126(RAM part for Rolling Stock), reliability growth monitoring is essential part of the main tasks of design phase in RAM growth monitoring. Implementation of reliability growth management program will provide very useful information on concept selection, product/process reliability, and cost effectiveness without too much time, money and engineering effort being spent on the development of failure suspect parts.

• 품질경영학회지
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• 제26권4호
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• pp.143-150
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• 1998

We propose an adaptive monitoring a, pp.oach for serially correlated data. This algorithm uses the adaptive linear prediction lattice filter (ALPLF) which makes it compute process parameters in real time and recursively update their estimates. It involves computation of the forward and backward prediction errors. CUSUM control charts are a, pp.ied to prediction errors simulaneously in both directions as an omnibus method for detecting changes in process parameters. Results of computer simulations demonstrate that the proposed adaptive monitoring a, pp.oach has great potentials for real-time industrial a, pp.ications, which vary frequently in their control environment.

• 한국기계가공학회지
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• 제11권4호
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• Journal of Welding and Joining
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• 제1권2호
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• pp.69-75
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• 1983

Both in-process quality control and reliability of the weld is one of the major concerns is applying friction welding. No reliable nondestructive monitoring method is available at present to determine the weld quality particularly in process of production. So that, this paper presents an experimental examination and quantitative analysis for the effects of initial acoustic emission(AE) counts on the weld strength relating to the rotating speed as a new approach which attempts finally to develop an on-line quality monitoring system design for friction welds using AE techniques. As one of the important results, it was well confirmed that the initial AE counts occurring during plastic deformation period of welding were quantitatively correlated with reliability at 95% confidence level to the joint strength of welds, tube-to-tube (SM 20 C to STS 304) and then an AE technique using the initial AE counts can be reliably applied to in-process strength monitoring of the weld.

• Smart Structures and Systems
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• 제29권6호
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• pp.767-775
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• 2022

Metal additive manufacturing (AM), also known as metal three-dimensional (3D) printing, produces 3D metal products by repeatedly adding and solidifying metal materials layer by layer. During the metal AM process, products experience repeated local melting and cooling using a laser or electron beam, resulting in product defects, such as warpage, cracks, and internal pores. Such defects adversely affect the final product. This paper proposes the in situ monitoring-based warpage prediction of metal AM products with experimental feature extraction. The temperature profile of the metal AM substrate during the process was experimentally collected. Time-domain features were extracted from the temperature profile, and their relationships to the warpage mechanism were investigated. The standard deviation showed a significant linear correlation with warpage. The findings from this study are expected to contribute to optimizing process parameters for metal AM warpage reduction.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.89-92
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• 2000

Laser claddmg 1s thc deposition of material on the surface of a part or workpiece. Cladding of metals produces a 100% dense metallurgically-bonded coating with minimal dilution for enhanced corrosion, abrasion and wear resistance. Despite of minimal heat Input and reduced processing time, cladding quality 1s affected by various process condition such as laser power and feed rate. Therefore, it is necessary to develop the momtoring and control methods of laser cladding process for the best cladding quality. In this paper, laser cladding monitoring system using CCD camera for measuring cladding pool shape, and photo-diode sensor for detecting optical signal emitted from the cladding front is introduced The variables extracted using this system can be apphed to control the laser cladding system to achieve the best claddmg results..

• Journal of Welding and Joining
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• 제32권6호
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• pp.64-69
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• 2014

The applications by using fiber laser have increased recently. However, due to high beam quality of fiber laser, it is inappropriate to apply the existing laser welding monitoring technology to the fiber laser welding as it is. On this study, thus, we analyzed emission signal with RMS and FFT for the in-process monitoring during fiber laser welding. 12mm-thick 304L stainless steel sheet was used in fiber laser welding and the result showed as follows: The intensity changes in RMS did not clarify the distinction between full penetration and partial penetration. However, as welding speed increases, specific frequency also increases in regards of frequency analysis by using FFT.

• 제어로봇시스템학회논문지
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• 제8권10호
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.122-122
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• 2000

Polishing work of a free-curved surface die demands simple and repetitive operations but requires a considerable amount of time for high precision. In out previous study, to reduce the polishing time and solve the problem of the shortage of skilled workers, the automatic polishing system was developed. However, in the polishing process of die, workers have to stay still in factory to monitor the polishing process for a long time in the poor environment. Therefore, this study proposes the remote operation and monitoring system of the automatic polishing robot. The developing system offer worker monitoring functions and teleoperating functions, as following: system state check, manual manipulation mode, automatic mode, manual teaching mode, automatic teaching mode, simulation by virtual manufacturing device. And automatic teaching system is developed to easily obtain a teaching data.

• Journal of Biosystems Engineering
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• 제33권4호
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• pp.276-281
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• 2008

A flow injection analysis method (FIA), which analyzes sample conditions after injecting a sample and reagents into a continuous stream, are recognized as the most adequate analyzing method according to the increase of sampling frequency, the decrease of measuring time and the diversity of measuring targets. Specially, the FIA is considered to be used effectively for the control of a fermentation process to produce fermentation food and useful microbial production by activation of a fermentation industry for development of biological materials. In this study, a flow injection analysis sensor unit was developed for on-line monitoring of the fermentation process. The performance was verified by on-line measuring the concentration of glucose of the fermentation process of wine. The glucose concentrations of the samples were measured every 12 hours during the whole fermentation process and compared with those by a HPLC. The concentration relative errors of glucose on the fermentation process of wine showed below 30% within 72 hours and over 50% after the 72 hours. The sensor unit had potential to on-line monitoring of the fermentation process but some problems to overcome for an commercial application.