• Title/Summary/Keyword: process data

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Data Management and Analysis in Foundry Industry (1) (주조공정 데이터 처리 및 분석 (1))

  • Cho, In-Sung
    • Journal of Korea Foundry Society
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    • v.42 no.1
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    • pp.35-41
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    • 2022
  • In the present paper, the data management of casting processes has been discussed. In order to construct a smart factory in the foundry industry, understanding of the whole casting processes has to be in the first place. Casting process data can be obtained at the kiosk operated by casting engineers and data acquired by sensors in the foundry facility. However, preprocessing of the casting process data must be carried out in order to analyze the casting process by the data. Techniques and some examples for data preprocessing in the foundry was introduced.

Information Visualization for the Manufacturing Process Optimization Based on Design of Experiment and Data Analysis (실험계획법과 데이터 분석 기반의 제조공정 최적화를 위한 정보 시각화)

  • Kim, Jae Chun;Jin, Seon A;Park, Young Hee;Noh, Seong Yeo;Lee, Hyun Dong
    • KIPS Transactions on Software and Data Engineering
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    • v.4 no.9
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    • pp.393-402
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    • 2015
  • Data visualization technology helps people easily understand various data and its analysis result, so usefulness of it is expected in the real industrial manufacturing sites. The large amount of data which is occurred at the manufacturing sites is able to fulfill very important roll to improve the manufacturing process. In this paper, we propose an information visualization for the manufacturing process optimization based on design of experimental and data analysis. The manufacturing process may be improved and be reduced cause of faulty by providing the easy-process analysis to understand the operation site through the information visualization of data analysis result.

Data Segmentation for a Better Prediction of Quality in a Multi-stage Process

  • Kim, Eung-Gu;Lee, Hye-Seon;Jun, Chi-Hyuek
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.2
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    • pp.609-620
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    • 2008
  • There may be several parallel equipments having the same function in a multi-stage manufacturing process, which affect the product quality differently and have significant differences in defect rate. The product quality may depend on what equipments it has been processed as well as what process variable values it has. Applying one model ignoring the presence of different equipments may distort the prediction of defect rate and the identification of important quality variables affecting the defect rate. We propose a procedure for data segmentation when constructing models for predicting the defect rate or for identifying major process variables influencing product quality. The proposed procedure is based on the principal component analysis and the analysis of variance, which demonstrates a better performance in predicting defect rate through a case study with a PDP manufacturing process.

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Two­Dimensional Warranty Data Modelling (2차원 품질보증데이터 모델링)

  • Jai Wook Baik;Jin Nam Jo
    • Journal of Korean Society for Quality Management
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    • v.31 no.4
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    • pp.219-225
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    • 2003
  • Two­dimensional warranty data can be modelled using two different approaches: two­dimensional point process and one­dimensional point process with usage as a function of age. The first approach has three different models. First of all, bivariate model is appealing but is not appropriate for explaining warranty claims. Next, the rest of the two models (marked point process, and counting and matching on both directions independently) are more appropriate for explaining warranty claims. However, the second one (counting and matching on both directions independently) assumes that the two variables (variables representing the two­dimensions) are independent. Last of all, one­dimensional point process with usage as a function of age is also promising to explain the two­dimensional warranty claims. But the models or variations of them need more investigation to be applicable to real warranty claim data.

Workflow Process-Aware Data Cubes and Analysis (워크플로우 프로세스 기반 데이터 큐브 및 분석)

  • Jin, Min-hyuck;Kim, Kwang-hoon Pio
    • Journal of Internet Computing and Services
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    • v.19 no.6
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    • pp.83-89
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    • 2018
  • In workflow process intelligence and systems, workflow process mining and analysis issues are becoming increasingly important. In order to improve the quality of workflow process intelligence, it is essential for an efficient and effective data center storing workflow enactment event logs to be provisioned in carrying out the workflow process mining and analytics. In this paper, we propose a three-dimensional process-aware datacube for organizing workflow enterprise data centers to efficiently as well as effectively store the workflow process enactment event logs in the XES format. As a validation step, we carry out an experimental process mining to show how much perfectly the process-aware datacubes are suitable for discovering workflow process patterns and its analytical knowledge, like enacted proportions and enacted work transferences, from the workflow process enactment event histories. Finally, we confirmed that it is feasible to discover the fundamental control-flow patterns of workflow processes through the implemented workflow process mining system based on the process-aware data cube.

Detection of API(Anomaly Process Instance) Based on Distance for Process Mining (프로세스 마이닝을 위한 거리 기반의 API(Anomaly Process Instance) 탐지법)

  • Jeon, Daeuk;Bae, Hyerim
    • Journal of Korean Institute of Industrial Engineers
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    • v.41 no.6
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    • pp.540-550
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    • 2015
  • There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

A Study on a Control Model for the Diagnostic and Nonconformity Rate in an Instrumental Process Involving Autocorrelation (자기상관이 있는 장치산업에서 공정 진단 및 부적합품률 제어모형에 관한 연구)

  • Koo, Ja-Hwal;Cho, Jin-Hyung
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.33 no.1
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    • pp.33-40
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    • 2010
  • Because sampling interval for data collection tends to be short compared with the overall processing time, in chemical process, instrumental process related tanks or furnace collected data have a significant autocorrelation. Insufficient control technique and frequent control actions cause unstable condition of the process. Traditional control charts which were developed based on iid (independently and identically distributed) among data cannot be applied on the existence of autocorrelation. Also unstable process is difficult to identity or diagnose. Because large-scale process has a lot of measurable variables and multi-step-structures among data, it is difficult to find relation between measurable variables and nonconformity. In this paper, we suggested an appicable model to diagnose the process and to find relation between measurable variables (CTQ) and nonconformity in the process having autocorrelation, unstable condition frequently, a lot of measurable variables, and multi-step-structure. And we applied this model to real process, to verify that the process engineers could easily and effectively diagnose the process and control the nonconformity.

Scenarios for Manufacturing Process Data Analysis using Data Mining (데이터 마이닝을 이용한 생산공정 데이터 분석 시나리오)

  • Lee, Hyoung-wook;Bae, Sung-min
    • Journal of Institute of Convergence Technology
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    • v.3 no.1
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    • pp.41-44
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    • 2013
  • Process and manufacturing data are numerously accumulated to the enterprise database in industries but little of those data are utilized. Data mining can support a decision to manager in process from the data. However, it is not easy to field managers because a proper adoption of various schemes is very difficult. In this paper, six scenarios are conducted using data mining schemes for the various situations of field claims such as yield problem, trend analysis and prediction of yield according to changes of operating conditions, etc. Scenarios, like templates, of various analysis situations are helpful to users.

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A Study on the Autocorrelation function for Markov Modulated Gaussian Process (마코프 조정 가우시안과정의 자기상관함수에 관한 연구)

  • 이혜연;장중순;신용백
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.25 no.6
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    • pp.1-6
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    • 2002
  • Most of process data control have been designed under the assumption that there are independence between observed data. However, it has been difficult to apply the traditional method to realtime data because they are autocorrelated, and they are not normally distributed. And the more, they have fluctuating means. Already the control method for these data was proposed by Markov Modulated Gaussian Process. Therefore, this study take into account MMGP's traits especially for the MMGP's autocorrelation.

Process Evaluation for Reliability Insurance: An Industrial Case Study

  • Hong, Yeon-Woong
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.2
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    • pp.401-410
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    • 2005
  • In this paper, we calculate the premium rate of reliability insurance policy for brake pads for automobiles using real failure data obtained from use-condition. We try process capability analysis for the manufacturing process of brake-system. We describe the performance factors which have an effect on failure characteristics of brake pads. We also obtain the maximum likelihood estimates of shape and scale parameters of the fitted Weibull distribution for brake pads.

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