• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.5
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• pp.111-116
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• 2018

Thermal fusion bonding is a method to enclose open microchannels fabricated on polymer chips for use in lab-on-a-chip (LOC) devices. Polymethyl methacrylate (PMMA) is utilized in various biomedical-microelectromechanical systems (bio-MEMS) applications, such as medical diagnostic kits, biosensors, and drug delivery systems. These applications utilize PMMAs biochemical compatibility, optical transparency, and mold characteristics. In this paper, we elucidate both the conformational entanglement of PMMA molecules at the contact interfacial regime, and the qualitative nature of the thermal fusion bonding phenomena through systematic molecular dynamics simulations.

• Transactions of Materials Processing
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• v.27 no.6
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• pp.363-369
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• 2018

This study measured the mechanical properties of an ash wood under various temperature and humidity conditions and a finite element model was developed to predict the behavior of the wood. A humidity-controlled chamber was developed and used for measuring the dimensional changes of woods under various humidity conditions. The thermal expansion coefficient and the elastic stiffness constants were measured by using a thermal chamber and the three-point bending test along the three principal axes of the wood. A constitutive model was proposed to describe the moisture content and temperature dependent behavior of wood. The proposed model was validated for the warping test of a wood plate. The warping of the plate was calculated using the finite element method. The calculated amount of warping was in consistence with the measurements.

• 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.

• Transactions of Materials Processing
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• v.30 no.4
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• pp.186-194
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• 2021

Finite element simulation is a widely applied method for practical purpose in various metal forming process. However, in the simulation of elasto-plastic behavior of porous material or in crystal plasticity coupled multi-scale simulation, it requires much calculation time, which is a limitation in its application in practical situations. A machine learning model that directly outputs the constitutive equation without iterative calculations would greatly reduce the calculation time of the simulation. In this study, we examined the possibility of artificial intelligence based constitutive equation with the input of existing state variables and current velocity filed. To introduce the methodology, we described the process of obtaining the training data, machine learning process and the coupling of machine learning model with commercial software DEFROM^TM, as a preliminary study, via rigid plastic finite element simulation.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.11
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.8
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• pp.92-100
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• 1997

An upper-bound elemental technique (UBET) analysis is carried out to improve the material flow and to reduce the load of bearing-race forming process. The UBET analysis, which adapts the advantages of stream function and finite element method, is useful for predicting the profile of complex geometric bound- ary. From the UBET analysis, the forming load, the velocity distribution and the stream line of the deformed billet are determined by minimizing the total power consumption with respect to chosen parameters. The results of present UBET analysis are better than those of previous UBET analysis. Experiments have been carried out with model material plasticine billets at room temperature. The theoretical predictions for forming load and flow pattern(stream line) are in good agreement with the experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.93-101
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• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.

• Journal of Intelligence and Information Systems
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• v.2 no.1
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• pp.11-26
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• 1996

본 논문에서는 화학공정의 이상 진단을 위한 지식 기반 조업 지원 시스템의 개발에 관하여 살펴보고자 한다. 조업지원 시스템에서 가장 핵심적인 부분은 공정에 비정상 상황이 발생한 경우 이를 감지하고, 공정에 발생한 증상들을 분석하여 이상의 근본 원인을 찾아내는 작업-이상 진단이다. 이상 진단을 효과적으로 수행하기 위해서는 적절한 데이터의 해석이 매우 중요한데, 기존의 데이터 해석법들은 정상상태에 기반한 방법들을 동적거동을 효과적으로 표현하기에는 어려움이 많다. 본 연구에서는 RBF에 기반한 신경망을 사용하여 동적을 효과적으로 표현할 수 있는 정성적인 데이터 해석 모듈을 구축하였으며, 이 모듈에서는 공정에서 측정된 정략적인 센서값들을 정성적인 정보로 가공하여 이상진단 모듈에 제공한다. 본 연구에서는 효과적인 이상진단을 위하여 기존의 인과관계 그래프 모델(Cause Effect DiGraph)에 기반한 두가지 그래프 모델을 개발하였다. RCED(Reduced Caue Effect Digraph)는 공정의 측정 변수만으로 공정의 인과관계를 표현하는 오프라인으로 구축된 지식베이스 모델이며, PGTT(Pattern Graph Through Time)는 공정에서 발생한 증상간의 인과관계를 실시간으로 나타내는 동적인 모델이다. 이상, 신경망에 기반한 정성적인 데이터 해석 모듈과 이상진단 모듈을 전문가 시스템 도구인 G2를 DEC AlphaStation 상에서 폴리프로필렌 공정에 대한 조업지원전문가 시스템을 구축하고 이를 적용하여보았다.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.5
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• pp.1161-1171
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• 1993

Model materials are developed to achieve similarity of flow patterns for mild steels in forming processes at high temperatures. The model materials consist of pure plasticine and one or two additives such as resin and lanolin. To verify the similarity of flow patterns between physical modeling and compression of mild steels at high temperatures, ring and compression tests have been carried out with the developed-model materials at various strain rates, temperatures and lubricants. The test results are in good agreement with the flow patterns obtained from upsetting of a mild steel at high temperatures.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.83.1-83.1
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• 2013

선형비점수차를 완벽하게 제거한 비축반사경 이론을 천체 관측용 분광기의 전단 광학계 등에 응용하면 색수차가 없는 기기 제작이 가능하다. 이러한 비축 반사경은 DTM(Diamond Turning Machine)을 이용하여 알루미늄으로 만들면 제작 시간이 단축된다. 그러나 DTM을 이용해 알루미늄과 같이 무른 금속을 가공할 경우 툴마크가 발생하게 된다. 툴마크는 회절현상을 발생시키며 이러한 회절현상은 알루미늄 반사경을 이용한 광학계 개발에 제약이 된다. 툴마크는 DTM 가공 이후 연마를 통해 제거할 수 있지만 알루미늄의 무른 특성으로 인해 연마 과정에서 반사경의 형상이 변할 가능성이 크다. 이러한 알루미늄 반사경의 형상 변화를 최소화하기 위한 방법으로는 알루미늄 반사경 표면에 무전해니켈도금을 하는 것이다. 하지만 도금 과정에서 반사경의 형상이 변할 가능성이 있기 때문에 두가지 방법을 사용하여 툴마크를 제거할 계획이다. 첫 번째 방법은 DTM 가공된 알루미늄 반사경을 5 um의 무전해니켈도금 이후 연마하여 툴마크를 제거하고 반사율 증가를 위해 그 위에 다시 알루미늄 코팅을 하는 방법니다. 두 번째 방법은 100 um의 무전해니켈도금 이후 DTM 가공을 하고 다시 연마를 통해 툴마크를 제거하는 방법이다. 이번 발표에서는 툴마크를 제거하기 위한 2가지 방법의 장단점을 확인하고 툴마크를 제거한 알루미늄 반사경을 제작하기 위한 과정을 설명하였다. 본 연구에서 개발한 비축 반사경은 서울대학교 창의연구단의 광학/적외선 카메라 CQUEAN의 차세대 모델에 적용할 계획이다.