Construction of Virtual Metrology Cloud Platform with Machine Learning Tools for Providing Factory-Wide Manufacturing Service

Tang Hsuan O, Min Hsiung Hung, Yu Chuan Lin, Chao Chun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, more and more high-tech manufacturing plants have used virtual metrology technology to monitor the production quality of machines and processes. The principle of virtual metrology operation consists of two phases: the off-line modeling stage and the on-line conjecture stage. In the off-line modeling stage, various calculation methods are used (such as neural networks, regression techniques etc.) to build a virtual metrology model. In the on-line conjecture stage, the established virtual metrology model can be used to instantly estimate the manufacturing quality of the workpiece or the health of the machine. Therefore, the virtual metrology can solve the measurement delay problem without increasing the measurement cost, and achieve the full inspection realm that the quality of each production workpiece can be monitored online and immediately. Microsoft Azure Machine Learning Studio (AMLS) is a cloud machine learning service developed by Microsoft. It integrates the tools needed for machine learning on a cloud platform and uses drag and drop to analyze machine learning related data, model building, performance testing and service building which greatly reduce the threshold for learning. For providing factory-wide manufacturing service, this research used AMLS machine learning services to construct a virtual metrology cloud platform, so that all production machines have the virtual metrology capability with a highly integration solution. Finally, the actual production data of the factory was used to conduct the integration test and performance evaluation of the system to verify the availability and industrial utilization of the research.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 12th Asian Conference, ACIIDS 2020, Proceedings
EditorsPawel Sitek, Marcin Pietranik, Marek Krótkiewicz, Chutimet Srinilta
PublisherSpringer
Pages47-59
Number of pages13
ISBN (Print)9789811533792
DOIs
Publication statusPublished - 2020
Event12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020 - Phuket, Thailand
Duration: 2020 Mar 232020 Mar 26

Publication series

NameCommunications in Computer and Information Science
Volume1178 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020
CountryThailand
CityPhuket
Period20-03-2320-03-26

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    O, T. H., Hung, M. H., Lin, Y. C., & Chen, C. C. (2020). Construction of Virtual Metrology Cloud Platform with Machine Learning Tools for Providing Factory-Wide Manufacturing Service. In P. Sitek, M. Pietranik, M. Krótkiewicz, & C. Srinilta (Eds.), Intelligent Information and Database Systems - 12th Asian Conference, ACIIDS 2020, Proceedings (pp. 47-59). (Communications in Computer and Information Science; Vol. 1178 CCIS). Springer. https://doi.org/10.1007/978-981-15-3380-8_5