A Product Quality Prediction Model Using Real-Time Process Monitoring in Manufacturing Supply Chain

Oh, YeongGwang; Park, Haeseung; Yoo, Arm; Kim, Namhun; Kim, Younghak; Kim, Dongchul; Choi, Jin-Uk; Yoon, Sung Ho; Yang, Hee-Jong

doi:10.7232/jkiie.2013.39.4.271

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…There are various ways to solve problems when they occur, and selfhealing using machine learning is becoming more popular these days [25,26]. However, they are limited to realtime monitoring, as they do not only detect and diagnose machine failures, defective products, or training and test predefined dataset.…”

Section: Machine Learning In Cpsmentioning

confidence: 99%

SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Shin

Cho

2018

Wireless Communications and Mobile Computing

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CPS is potential application in various fields, such as medical, healthcare, energy, transportation, and defense, as well as Industry 4.0 in Germany. Although studies on the equipment aging and prediction of problem have been done by combining CPS with Industry 4.0, such studies were based on small numbers and majority of the papers focused primarily on CPS methodology. Therefore, it is necessary to study active self-protection to enable self-management functions, such as self-healing by applying CPS in shop-floor. In this paper, we have proposed modeling of shop-floor and a dynamic reconfigurable CPS scheme that can predict the occurrence of anomalies and self-protection in the model. For this purpose, SVM was used as a machine learning technology and it was possible to restrain overloading in manufacturing process. In addition, we design CPS framework based on machine learning for Industry 4.0, simulate it, and perform. Simulation results show the simulation model autonomously detects the abnormal situation and it is dynamically reconfigured through self-healing.

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Section: Machine Learning In Cpsmentioning

confidence: 99%

SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Shin

Cho

2018

Wireless Communications and Mobile Computing

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“…다변량 시계열 데이터를 미리 정의된 범주로 분류하는 다변량 시계열 데이터 분류(multivariate time series classification)는 시 계열 데이터 마이닝 분야에서 중요하게 다루어지는 문제 중 하나이다 (He et al, 2015). 다변량 시계열 데이터 분류의 예로, 제조 설비에 부착된 센서 데이터를 바탕으로 제품의 양/불량 을 예측하는 것을 들 수 있다 (Oh et al, 2013). 이렇듯 최근 제 조 설비를 비롯한 다양한 분야에서 사용되는 센서가 엄청난 양의 다변량 시계열 데이터를 생산함에 따라, 다변량 시계열 데이터 분류가 더욱 중요해졌다.…”

Section: 서 론unclassified

Feature Selection Method for Multivariate Time Series Data Classification

안길승¹,

이환철²,

허선³

2017

JKIIE

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Multivariate time series data classification has recently attracted interests from both industry and academia, as sensors used in various industries produce a lot of multivariate time series data. Having a lot of features, feature selection from those time series is essential to efficiently construct a classifier. In this paper, we propose a feature selection method to efficiently select features from the multivariate time series data considering variation. The candidate feature set is too large to efficiently select features and there are some feature redundancies. The proposed method can efficiently resolve these problems, and is validated by real datasets obtained from UCI Machine Learning Repository. Experiments show that the proposed method outperforms the typical feature selection methods in terms of accuracy and precision.

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“…이러한 양상은 작은 크기의 고집적 도 제품과 특수기능 제품의 생산에 따른 검사 비용 상승으로 더욱더 심화 되고 있다 (Bae, 1995). 품질도 수율 향상만큼 중요 한 요소로 제품에 대한 품질을 확보하지 못한 기업은 고객 불 만으로 인한 이미지 하락, 판매 시장 감소, 제품 리콜(Recall)에 따른 처리 비용 증가로 치열한 경쟁에서 도태된다 (Oh et al, 2013). 따라서 품질을 확보한 상태에서의 수율 향상이 반도체 제조 공정에서 풀어야 하는 과제이다.…”

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A Prediction of Chip Quality using OPTICS (Ordering Points to Identify the Clustering Structure)-based Feature Extraction at the Cell Level

Kim¹,

Baek²

2014

Journal of Korean Institute of Industrial Engineers

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The semiconductor manufacturing industry is managed by a number of parameters from the FAB which is the initial step of production to package test which is the final step of production. Various methods for prediction for the quality and yield are required to reduce the production costs caused by a complicated manufacturing process. In order to increase the accuracy of quality prediction, we have to extract the significant features from the large amount of data. In this study, we propose the method for extracting feature from the cell level data of probe test process using OPTICS which is one of the density-based clustering to improve the prediction accuracy of the quality of the assembled chips that will be placed in a package test. Two features extracted by using OPTICS are used as input variables of quality prediction model because of having position information of the cell defect. The package test progress for chips classified to the correct quality grade by performing the improved prediction method is expected to bring the effect of reducing production costs.

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A Product Quality Prediction Model Using Real-Time Process Monitoring in Manufacturing Supply Chain

Cited by 6 publications

References 7 publications

SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

SVM‐Based Dynamic Reconfiguration CPS for Manufacturing System in Industry 4.0

Feature Selection Method for Multivariate Time Series Data Classification

A Prediction of Chip Quality using OPTICS (Ordering Points to Identify the Clustering Structure)-based Feature Extraction at the Cell Level

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