research-article

Applying data mining techniques to address critical process optimization needs in advanced manufacturing

Authors:

Pengnian WangAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1739 - 1748

https://doi.org/10.1145/2623330.2623347

Published: 24 August 2014 Publication History

Abstract

Advanced manufacturing such as aerospace, semi-conductor, and flat display device often involves complex production processes, and generates large volume of production data. In general, the production data comes from products with different levels of quality, assembly line with complex flows and equipments, and processing craft with massive controlling parameters. The scale and complexity of data is beyond the analytic power of traditional IT infrastructures. To achieve better manufacturing performance, it is imperative to explore the underlying dependencies of the production data and exploit analytic insights to improve the production process. However, few research and industrial efforts have been reported on providing manufacturers with integrated data analytical solutions to reveal potentials and optimize the production process from data-driven perspectives.

In this paper, we design, implement and deploy an integrated solution, named PDP-Miner, which is a data analytics platform customized for process optimization in Plasma Display Panel (PDP) manufacturing. The system utilizes the latest advances in data mining technologies and Big Data infrastructures to create a complete analytical solution. Besides, our proposed system is capable of supporting automatically configuring and scheduling analysis tasks, and balancing heterogeneous computing resources. The system and the analytic strategies can be applied to other advanced manufacturing fields to enable complex data analysis tasks. Since 2013, PDP-Miner has been deployed as the data analysis platform of ChangHong COC. By taking the advantages of our system, the overall PDP yield rate has increased from 91% to 94%. The monthly production is boosted by 10,000 panels, which brings more than 117 million RMB of revenue improvement per year.

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Cited By

Wanner JWissuchek CWelsch GJaniesch C(2023)A Taxonomy and Archetypes of Business Analytics in Smart ManufacturingACM SIGMIS Database: the DATABASE for Advances in Information Systems10.1145/3583581.358358454:1(11-45)Online publication date: 7-Feb-2023
https://doi.org/10.1145/3583581.3583584
Sen SKarmakar GPang S(2023)Critical Data Detection for Dynamically Adjustable Product Quality in IIoT-Enabled ManufacturingIEEE Access10.1109/ACCESS.2023.327694211(49464-49480)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3276942
Tasci TLechler ASommer SSelig AGöb TGruber AMelchiors PHempelmann RWutzke RBasner DSchel DGrigorjan ARauhoeft GSchopp MPreuß R(2021)MultiCloud-basierte Dienstleistungen für die ProduktionDienstleistungsinnovationen durch Digitalisierung10.1007/978-3-662-62144-8_10(457-503)Online publication date: 5-Jan-2021
https://doi.org/10.1007/978-3-662-62144-8_10
Show More Cited By

Index Terms

Applying data mining techniques to address critical process optimization needs in advanced manufacturing
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 August 2014

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KDD '14

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Wanner JWissuchek CWelsch GJaniesch C(2023)A Taxonomy and Archetypes of Business Analytics in Smart ManufacturingACM SIGMIS Database: the DATABASE for Advances in Information Systems10.1145/3583581.358358454:1(11-45)Online publication date: 7-Feb-2023
https://doi.org/10.1145/3583581.3583584
Sen SKarmakar GPang S(2023)Critical Data Detection for Dynamically Adjustable Product Quality in IIoT-Enabled ManufacturingIEEE Access10.1109/ACCESS.2023.327694211(49464-49480)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3276942
Tasci TLechler ASommer SSelig AGöb TGruber AMelchiors PHempelmann RWutzke RBasner DSchel DGrigorjan ARauhoeft GSchopp MPreuß R(2021)MultiCloud-basierte Dienstleistungen für die ProduktionDienstleistungsinnovationen durch Digitalisierung10.1007/978-3-662-62144-8_10(457-503)Online publication date: 5-Jan-2021
https://doi.org/10.1007/978-3-662-62144-8_10
Krauß BHöhr SEckhardt AScheib J(2019)Digitale Services in der FabrikZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb10.3139/104.112131114:9(580-583)Online publication date: 27-Sep-2019
https://doi.org/10.3139/104.112131
Hohr STasci TVerl A(2019)Realization of Data Analytics Projects in Manufacturing Using a Microservice-Based Approach2019 IEEE International Conference on Mechatronics (ICM)10.1109/ICMECH.2019.8722926(321-326)Online publication date: Mar-2019
https://doi.org/10.1109/ICMECH.2019.8722926
Tasci THöhr SMagerstedt S(2019)Datenprozessabbildung über multiple Cloud-DienstleisterDigitale Dienstleistungsinnovationen10.1007/978-3-662-59517-6_18(363-391)Online publication date: 28-Aug-2019
https://doi.org/10.1007/978-3-662-59517-6_18
Liu JShen HSarker AChung W(2018)Leveraging Dependency in Scheduling and Preemption for High Throughput in Data-Parallel Clusters2018 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER.2018.00054(359-369)Online publication date: Sep-2018
https://doi.org/10.1109/CLUSTER.2018.00054
Aschenbrenner DFritscher MSittner FKrauss MSchilling K(2017)User Study on the Facility Asynchronous Data Analysis Tool (FADAT) for Teleanalysis and Optimization of an Industrial Robot Plant * *funded by the Bavarian Ministry of Economic Affairs, Infrastructure, Transport and Technology in its R&D programs ‘MainTelRob’ and ‘Bayern digital’.IFAC-PapersOnLine10.1016/j.ifacol.2017.08.160450:1(11239-11244)Online publication date: Jul-2017
https://doi.org/10.1016/j.ifacol.2017.08.1604
Li TZeng CZhou WXue WHuang YLiu ZZhou QXia BWang QWang WZhu X(2017)FIU-Miner (a fast, integrated, and user-friendly system for data mining) and its applicationsKnowledge and Information Systems10.1007/s10115-016-1014-052:2(411-443)Online publication date: 1-Aug-2017
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Mejía JÍñiguez FMuñoz M(2017)Data Analysis for Software Process Improvement: A Systematic Literature ReviewRecent Advances in Information Systems and Technologies10.1007/978-3-319-56535-4_5(48-59)Online publication date: 28-Mar-2017
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