The Prediction of Positioning shift for a Robot Arm Using Machine Learning Techniques

Ping Wun Huang, Kuan Jung Chung

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

Abstract

This study presents an Artificial Intelligence (AI) approach for estimating the Cartesian positioning shift of a wafer handling robot arm to prevent the occurrence of unexpected event, drop of wafers. First, a Charge-coupled Device (CCD) based robot arm fault diagnostic system was built to measure the target positions of the robot arm when handling wafers. An ensemble-based machine learning model with time series cross validation technique from a commercial software called Decanter AI (Mobagel Inc.) was applied to predict the quantity of the maximum position shift with respect to X and Y axis for next one minute. The prediction results by the test datasets through 38,417 minutes show that the Root Mean Square Error (RMSE) is 4.351 μm to validate the trained model is appropriate for predicting the positioning shift of the handling robot arm.

Original languageEnglish
Title of host publicationIMPACT 2019 - 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceeding
PublisherIEEE Computer Society
Pages58-61
Number of pages4
ISBN (Electronic)9781728160702
DOIs
Publication statusPublished - 2019 Oct
Event14th International Microsystems, Packaging, Assembly and Circuits Technology Conference, IMPACT 2019 - Taipei, Taiwan
Duration: 2019 Oct 232019 Oct 25

Publication series

NameProceedings of Technical Papers - International Microsystems, Packaging, Assembly, and Circuits Technology Conference, IMPACT
Volume2019-October
ISSN (Print)2150-5934
ISSN (Electronic)2150-5942

Conference

Conference14th International Microsystems, Packaging, Assembly and Circuits Technology Conference, IMPACT 2019
CountryTaiwan
CityTaipei
Period19-10-2319-10-25

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All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Huang, P. W., & Chung, K. J. (2019). The Prediction of Positioning shift for a Robot Arm Using Machine Learning Techniques. In IMPACT 2019 - 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceeding (pp. 58-61). [9024996] (Proceedings of Technical Papers - International Microsystems, Packaging, Assembly, and Circuits Technology Conference, IMPACT; Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/IMPACT47228.2019.9024996