• 한국전기전자재료학회논문지
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• 제31권7호
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• pp.450-454
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• 2018

Because of the rapidly changing environment and high uncertainties, the semiconductor industry is in need of appropriate forecasting technology. In particular, both the cost and time in the test process are increasing because the process becomes complicated and there are more factors to consider. In this paper, we propose a prediction model that predicts a final "good" or "bad" on the basis of preconditioning test data generated in the semiconductor test process. The proposed prediction model solves the classification and regression problems that are often dealt with in the semiconductor process and constructs a reliable prediction model. We also implemented a prediction model through various machine learning algorithms. We compared the performance of the prediction models constructed through each algorithm. Actual data of the semiconductor test process was used for accurate prediction model construction and effective test verification.

• 소성∙가공
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• 제25권6호
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• pp.396-401
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• 2016

Recently, Multi-strength hot stamping process has been widely used to achieve lightweight and crashworthiness in automotive industry. In concept of multi-strength hot stamping process, process design of tailor rolled blank(TRB) in partial heating is difficult because of thickness and temperature variation of blank. In this study, springback prediction of TRB in partial heating process was performed considering its thickness and temperature variation. In partial heating process, TRB was heated up to $900^{\circ}C$ for thicker side and below $Ac_3$ transformation temperature for thinner side, respectively. Johnson-Mehl-Avrami-Kolmogorov(JMAK) equation was applied to calculate austenite fraction according to heating temperature. Calculated austenite fraction was applied to FE-simulation for the prediction of springback. Experiment for partial heating process of TRB was also performed to verify prediction accuracy of FE-simulation coupled with JMAK equation.

• 한국정밀공학회지
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• 제34권1호
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• pp.23-27
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• 2017

This paper aims to develop a prediction model for the product quality of a casting process. Prediction of the product quality utilizes an artificial neural network (ANN) in order to renovate the manufacturing technology of the root industry. Various aspects of the research on the prediction algorithm for the casting process using an ANN have been investigated. First, the key process parameters have been selected by means of a statistics analysis of the process data. Then, the optimal number of the layers and neurons in the ANN structure is established. Next, feed-forward back propagation and the Levenberg-Marquardt algorithm are selected to be used for training. Simulation of the predicted product quality shows that the prediction is accurate. Finally, the proposed method shows that use of the ANN can be an effective tool for predicting the results of the casting process.

• Geomechanics and Engineering
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• 제30권5호
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• pp.413-422
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• 2022

TBM is widely used in the construction of various underground projects in the current world, and has the unique advantages that cannot be compared with traditional excavation methods. However, due to the high cost of TBM, the damage is even greater when geological disasters such as collapse occur during excavation. At present, there is still a shortage of research on various types of risk prediction of TBM tunnel, and accurate and reliable risk prediction model is an important theoretical basis for timely risk avoidance during construction. In this paper, a prediction model is proposed to evaluate the risk level of tunnel collapse by establishing a reasonable risk index system, using analytic hierarchy process to determine the index weight, and using the normal cloud model theory. At the same time, the traditional analytic hierarchy process is improved and optimized to ensure the objectivity of the weight values of the indicators in the prediction process, and the qualitative indicators are quantified so that they can directly participate in the process of risk prediction calculation. Through the practical engineering application, the feasibility and accuracy of the method are verified, and further optimization can be analyzed and discussed.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 제5회 압연심포지엄 신 시장 개척을 위한 압연기술
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• pp.175-183
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• 2004

In hot strip rolling, a capability for precisely predicting roll force is crucial for sound process control. In the past, on-line prediction models have been developed mostly on the basis of Orowan's theory and its variation. However, the range of process conditions in which desired prediction accuracy could be achieved was rather limited, mainly due to many simplifying assumptions inherent to Orowan's theory. As far as the prediction accuracy is concerned, a rigorously formulated finite element(FE) process model is perhaps the best choice. However, a FE process model in general requires a large CPU time, rendering itself inadequate for on-line purpose. In this report, we present a FE-based on-line prediction model applicable to precision process control in a finishing mill(FM). Described was an integrated FE process model capable of revealing the detailed aspects of the thermo-mechanical behavior of the roll-strip system. Using the FE process model, a series of process simulation was conducted to investigate the effect of diverse process variables on some selected non-dimensional parameters characterizing the thermo-mechanical behavior of the strip. Then, it was shown that an on-line model for the prediction of roll force could be derived on the basis of these parameters. The prediction accuracy of the proposed model was examined through comparison with measurements from the hot strip mill.

• 한국초등과학교육학회지:초등과학교육
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• 제26권5호
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• pp.499-508
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• 2007

본 연구의 목적은 외국 교육과정에 나타난 분류, 예상 및 추리 탐구 요소를 살펴보고, 우리나라 3학년부터 10학년까지의 과학교과서에 나타난 기초 탐구 과정 중에서 분류, 예상 및 추리가 어떻게 제시되어 있는지 알아보는 것이다. 분석 결과는 다음과 같다. 분류에서는 전체 빈도수가 관찰이나 측정에 비해서 적었으며, 초등학교에서 많이 발견되었다. 분류의 단계는 '한 가지 특성에 의한 분류'가 대부분이고 분류의 상위 단계인 '복합 특성 특성에 따른 분류' 활동은 많이 발견되지 않았다. 예상에서는 대부분이 실험 결과를 이용한 예상 활동이었고, 예상의 단계에서 초보적 예상이 대부분으로 예상의 상위 단계인 조작적 예상은 많이 발견하지 못했다. 추리는 분류와 예상에 비해서 많은 빈도수를 나타냈으며, 학년급간 분포에서도 상위 학년에서 많은 빈도수를 나타내었다. 또한, 추리의 단계에서도 초등학교에서 중고등학교로 넘어가면서 추리의 상위단계인 이차적 추리의 빈도가 증가하였다.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2001년도 추계학술대회 논문집
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• pp.121-124
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• 2001

On-line prediction model which calculate roll force, roll power and forward slip of continuous hot strip rolling was built based on the results of plane strait rigid-viscoplastic finite element process model. Using the integrated FE process model, a series of finite element simulation was conducted over the process variables, and the influence of various process conditions on non-dimensional parameters was inspected. The prediction accuracy of the proposed on-line model under front and back tension is examined through comparison with predictions from a finite element process model over the various process conditions. In addition, we examined the validity of the on-line prediction model through comparison with roll force of experiment in hot rolling.

• 산업경영시스템학회지
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• 제27권2호
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• pp.10-16
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• 2004

Data mining technique is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. This paper uses a data mining technique for the prediction of defect types in manufacturing Process. The Purpose of this Paper is to model the recognition of defect type Patterns and Prediction of each defect type before it occurs in manufacturing process. The proposed model consists of data handling, defect type analysis, and defect type prediction stages. The performance measurement shows that it is higher in prediction accuracy than logistic regression model.

• 상하수도학회지
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• 제38권1호
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• pp.1-15
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• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• 한국정밀공학회지
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• 제16권11호
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• pp.98-105
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• 1999

As the IC(Integrated Circuit) has been densified and complicated, it is required to thorough process control to improve yield. Experts, for this purpose, focused on the process analysis automation, which is came from the strict data management in semiconductor manufacturing. In this paper, we presents the process analysis system that can analyze causes, for a output after processes. Also, the plasma etching process that highly affects yield among semiconductor process is modeled to predict a output before the process. To approach this problem, we use adaptively trained neural networks that exhibit superior accuracy over statistical techniques. And in comparison with methods in other paper, a method that history of trend for input data is considered is shown to offer advantage in both learning and prediction capability. This research regards CD(Critical Dimension) that is considerable in high integrated circuit as output variable of the prediction model.