• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.47-51
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• 1997

The high speed machining which can improve the production and quality in machining has been adopted remarkably in dietmold industry. As the speed of machine tool spindle increases, the machinability evaluation and monitoring of high speed machining is necessary. In this study, the machinability of 30, 000rpm class spindle was evaluated by using the developed tool dynamometer and the machining properties of high hardened and toughness materials in high speed were examined. Finally, the in-process monitoring technologies of tool wear were presented through the prediction by the experimental formula and pattern recognition by the neural network.

• 한국소음진동공학회논문집
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• 제13권1호
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• pp.48-55
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• 2003

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

• Geomechanics and Engineering
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• 제29권3호
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• pp.219-228
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• 2022

The most widely used parameter to represent rock abrasiveness is the Cerchar abrasivity index (CAI). The CAI value can be applied to predict wear in TBM cutters. It has been extensively demonstrated that the CAI is affected significantly by cementation degree, strength, and amount of abrasive minerals, i.e., the quartz content or equivalent quartz content in rocks. The relationship between the properties of rocks and the CAI is investigated in this study. A database comprising 223 observations that includes rock types, uniaxial compressive strengths, Brazilian tensile strengths, equivalent quartz contents, quartz contents, brittleness indices, and CAIs is constructed. A linear model is developed by selecting independent variables while considering multicollinearity after performing multiple regression analyses. Machine learning-based regression methods including support vector regression, regression tree regression, k-nearest neighbors regression, random forest regression, and artificial neural network regression are used in addition to multiple linear regression. The results of the random forest regression model show that it yields the best prediction performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• 한국터널지하공간학회 논문집
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• 제26권1호
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• pp.39-58
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• 2024

TBM (Tunnel boring machine)은 터널 굴착 과정에서 여러 디스크 커터를 이용하여 암석을 절삭한다. 디스크 커터는 암석과의 지속적인 접촉과 마찰로 인해 마모된다. 디스크 커터의 표면이 마모되면 절삭 능력이 감소하고 굴착 효율이 떨어진다. 암석의 마모성은 디스크 커터 마모에 큰 영향을 미친다. 높은 마모도를 가진 암석은 커터에 더 큰 마모를 일으키며, 이는 디스크 커터의 수명을 단축시킨다. 세르샤 마모지수(Cerchar abrasivity index, CAI)는 암석의 마모성을 평가하는데 널리 사용되는 지표로 CAI는 암석의 마모특성을 나타내며, 디스크 커터의 수명과 성능 예측에 필수적인 요소로 인식되고 있다. 본 연구의 목적은 암석의 강도, 암석학적 특성과 선형회귀, 머신러닝 기법을 이용하여 CAI를 효과적으로 추정하는 새로운 방법을 개발하는 것이다. 문헌 조사를 통해 CAI, 일축압축강도, 압열인장강도, 등가석영함량이 포함된 데이터베이스를 구축하고 파생변수를 추가하였다. 통계적 유의성과 다중공선성을 고려하여 다중선형회귀분석을 위한 입력변수를 선정하였고, 머신러닝 모델의 입력변수는 변수중요도 분석을 통해 선정하였다. 머신러닝 예측모델 중 Gradient Boosting 모델의 예측 성능이 가장 높게 나타나 최적의 CAI 예측 모델로 선정되었다. 마지막으로 본 연구에서 도출한 다중선형회귀분석과 Gradient Boosting 모델의 예측 성능을 선행연구들의 CAI 예측모델과 비교하여 연구 결과의 타당성을 확인하였다.

• Nuclear Engineering and Technology
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• 제28권1호
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• pp.17-26
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• 1996

프레팅마모 기인 연료봉 손상을 방지할 수 있는 노내 연료봉 지지조건은 잔여 지지격자스프링 변위량 또는 연료봉 /지지격자 갭에 의해 평가될 수 있다. 핵연료 설계 인자들이 프레팅마모 손상에 미치는 영향을 평가하기 위해 연소도의 함수로서 노내 연료봉 지지조건을 모사할 수 있는 방법론을 사용하여 GRID-FORCE프로그램을 개발하였다. 이 프로그램에서는 노내 연료봉 지지조건에 영향을 주는 주요 인자로서 피복관 크립, 초기 스프링 변위, 초기 스프링힘 그리고 스프링힘 조사이완이 고려된다. 이 주요 인자들에 대한 민감도 분석 결과, 초기 스프링 변위, 스프링힘 조사이완, 피복관 크립 순으로 노내 연료봉 지지조건에 영향을 주는 것으로 나타났다. 이 프로그램을 실제 노내에서 발생한 프레팅마모 기인 연료봉 손상에 적용한 결과를 토대로 판단해 볼 때 이 프로그램을 새로 개발된 피복관 재질 및 /또는 새로 개발된 지지격자 설계가 프레팅마모 기인 연료봉 손상을 방지할 수 있는 설계여유도를 효과적으로 평가할 수 있음을 알 수 있다.

• Design & Manufacturing
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• 제16권4호
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• pp.1-6
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• 2022

Fine blanking is a press processing technology that can process most of the product thickness into a smooth surface with a single stroke. In this fine blanking process, shear is an essential step. The punches and dies used in the shear are subjected to impacts of tens to hundreds of gravitational accelerations, depending on the type and thickness of the material. Therefore, among the components of the fine blanking mold (dies), punches and dies are the parts with the shortest lifespan. In the actual production site, various types of tool damage occur such as wear of the tool as well as sudden punch breakage. In this study, machine learning algorithms were used to predict these problems in advance. The dataset used in this paper consisted of the signal of the vibration sensor installed in the tool and the measured burr size (tool wear). Various features were extracted so that artificial intelligence can learn effectively from signals. It was trained with 5 features with excellent distinguishing performance, and the SVM algorithm performance was the best among 33 learning models. As a result of the research, the vibration signal at the time of imminent tool replacement was matched with an accuracy of more than 85%. It is expected that the results of this research will solve problems such as tool damage due to accidental punch breakage at the production site, and increase in maintenance costs due to prediction errors in punch exchange cycles due to wear.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2005년도 창립60주년 기념 추계 학술대회
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• pp.113.2-113.2
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• 2005

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1996년도 제24회 추계학술대회
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• pp.176-182
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• 1996

This paper describes an empherical equation which is to predict the engine condition of the supersonic aircraft. The equation, which is a function of running time of engine and engine oil, is derived from the trend analysis of JOAP data. Qualitative analysis is carried out to make up for the weak points in the current JOAP system. Also wear debris collected from the abnormal engine is analyzed by EDS to detect the damaged parts of engine.

• Tribology and Lubricants
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• 제18권2호
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• pp.160-166
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• 2002

In case of rough contact fatigue, the accurate calculation of surface tractions is essential to the prediction of crack initiation life. Accurate Surface tractions influencing shear stress amplitude can be obtained by contact analysis based on the morphology of contact surfaces. In this study, to simulate rough contact under sliding condition, gaussian rough surface generated numerically in the previous study was used and to calculate clack initiation life in the substrate, dislocation pileup theory was used.