Data-based assembly failure state estimation of mobile IT parts using a 6 DOF manipulator

Min Woo Na; Tae Jung Kim; Jae Bok Song

Data-based assembly failure state estimation of mobile IT parts using a 6 DOF manipulator

Min Woo Na, Tae Jung Kim, Jae Bok Song

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This study discusses data-based failure state estimation of the mobile IT parts assembly using a 6 DOF manipulator. A position control-based robotic assembly is fast and simple for automation of production lines. However, when the assembly fails, it is very difficult to find the error that causes the assembly to fail. And the worker should stop and intervene in the assembly process to compensate the error. This is time-consuming and inefficient for the productivity of factory automation. To compensate the error without the aid of worker, this study presents a method for assembly failure state estimation. First, the failure state modeling of the mobile IT parts assembly is proposed. And the supervised learning was used for training whose input is the F/T sensor data and whose output is the failure state of the assembly. Furthermore, it is shown that artificial neural network (ANN) can lead to a higher classification accuracy for estimating the failure state and faster prediction.

Original language	English
Title of host publication	International Conference on Control, Automation and Systems
Publisher	IEEE Computer Society
Pages	1703-1707
Number of pages	5
ISBN (Electronic)	9788993215151
Publication status	Published - 2018 Dec 10
Event	18th International Conference on Control, Automation and Systems, ICCAS 2018 - PyeongChang, Korea, Republic of Duration: 2018 Oct 17 → 2018 Oct 20

Publication series

Name	International Conference on Control, Automation and Systems
Volume	2018-October
ISSN (Print)	1598-7833

Other

Other	18th International Conference on Control, Automation and Systems, ICCAS 2018
Country/Territory	Korea, Republic of
City	PyeongChang
Period	18/10/17 → 18/10/20

Bibliographical note

Funding Information:
This research was supported by the MOTIE under the Industrial Foundation Technology Development Program supervised by the KEIT (No. 10060110)

Publisher Copyright:
© ICROS.

Keywords

Assembly failure state
Fault detection
Robotic assembly
State estimation
Supervised learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering

Cite this

Data-based assembly failure state estimation of mobile IT parts using a 6 DOF manipulator. / Na, Min Woo; Kim, Tae Jung; Song, Jae Bok.
International Conference on Control, Automation and Systems. IEEE Computer Society, 2018. p. 1703-1707 8571933 (International Conference on Control, Automation and Systems; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Na, MW, Kim, TJ & Song, JB 2018, Data-based assembly failure state estimation of mobile IT parts using a 6 DOF manipulator. in International Conference on Control, Automation and Systems., 8571933, International Conference on Control, Automation and Systems, vol. 2018-October, IEEE Computer Society, pp. 1703-1707, 18th International Conference on Control, Automation and Systems, ICCAS 2018, PyeongChang, Korea, Republic of, 18/10/17.

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abstract = "This study discusses data-based failure state estimation of the mobile IT parts assembly using a 6 DOF manipulator. A position control-based robotic assembly is fast and simple for automation of production lines. However, when the assembly fails, it is very difficult to find the error that causes the assembly to fail. And the worker should stop and intervene in the assembly process to compensate the error. This is time-consuming and inefficient for the productivity of factory automation. To compensate the error without the aid of worker, this study presents a method for assembly failure state estimation. First, the failure state modeling of the mobile IT parts assembly is proposed. And the supervised learning was used for training whose input is the F/T sensor data and whose output is the failure state of the assembly. Furthermore, it is shown that artificial neural network (ANN) can lead to a higher classification accuracy for estimating the failure state and faster prediction.",

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AB - This study discusses data-based failure state estimation of the mobile IT parts assembly using a 6 DOF manipulator. A position control-based robotic assembly is fast and simple for automation of production lines. However, when the assembly fails, it is very difficult to find the error that causes the assembly to fail. And the worker should stop and intervene in the assembly process to compensate the error. This is time-consuming and inefficient for the productivity of factory automation. To compensate the error without the aid of worker, this study presents a method for assembly failure state estimation. First, the failure state modeling of the mobile IT parts assembly is proposed. And the supervised learning was used for training whose input is the F/T sensor data and whose output is the failure state of the assembly. Furthermore, it is shown that artificial neural network (ANN) can lead to a higher classification accuracy for estimating the failure state and faster prediction.

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Data-based assembly failure state estimation of mobile IT parts using a 6 DOF manipulator

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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