Quality prediction plays a key role in all kinds of manufacturing. To predict quality products or forecasting a product quality, data should contain time series properties. However, these properties often fail to be contained in the data. This usually happens in many industrial manufacturing environments. For example, lot processing often fails because of many reasons: parts having different lot numbers can be mixed up in the following step; parts are missing, broken, etc; in some cases, the long term data are required but short-term data are only gathered. Without these properties, any machine learning technique should fail to predict data quality. In this paper, we have data collected in short-term period although the total samples are 3, 000 and under a condition, we fail to predict a quality of a sample with various machine learning prediction techniques: Gaussian process regression method and auto-regressive integrated moving average. Although hyper-parameters are well-optimized and both predicted results are almost the same, the predicted ones are far from the ground truth. Through these experiments, we concluded that well-designed data collection is the most important in quality prediction.
KSP Keywords
Auto Regressive Integrated Moving Average(ARIMA), Batch Process, Cause analysis, Data Collection, Data Quality, Data collected, Failure cause, Gaussian Process(GP), Gaussian Process Regression(GPR), Ground Truth, Industrial manufacturing
Copyright Policy
ETRI KSP Copyright Policy
The materials provided on this website are subject to copyrights owned by ETRI and protected by the Copyright Act. Any reproduction, modification, or distribution, in whole or in part, requires the prior explicit approval of ETRI. However, under Article 24.2 of the Copyright Act, the materials may be freely used provided the user complies with the following terms:
The materials to be used must have attached a Korea Open Government License (KOGL) Type 4 symbol, which is similar to CC-BY-NC-ND (Creative Commons Attribution Non-Commercial No Derivatives License). Users are free to use the materials only for non-commercial purposes, provided that original works are properly cited and that no alterations, modifications, or changes to such works is made. This website may contain materials for which ETRI does not hold full copyright or for which ETRI shares copyright in conjunction with other third parties. Without explicit permission, any use of such materials without KOGL indication is strictly prohibited and will constitute an infringement of the copyright of ETRI or of the relevant copyright holders.
J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
If you have any questions or concerns about these terms of use, or if you would like to request permission to use any material on this website, please feel free to contact us
KOGL Type 4:(Source Indication + Commercial Use Prohibition+Change Prohibition)
Contact ETRI, Research Information Service Section
Privacy Policy
ETRI KSP Privacy Policy
ETRI does not collect personal information from external users who access our Knowledge Sharing Platform (KSP). Unathorized automated collection of researcher information from our platform without ETRI's consent is strictly prohibited.
[Researcher Information Disclosure] ETRI publicly shares specific researcher information related to research outcomes, including the researcher's name, department, work email, and work phone number.
※ ETRI does not share employee photographs with external users without the explicit consent of the researcher. If a researcher provides consent, their photograph may be displayed on the KSP.