Multi-sensor data fusion for improved measurement accuracy in injection molding

Zhaoyan Fan; Robert X. Gao; Peng Wang; David O. Kazmer

doi:10.1109/I2MTC.2016.7520465

Multi-sensor data fusion for improved measurement accuracy in injection molding

Zhaoyan Fan
, Robert X. Gao
, Peng Wang
, David O. Kazmer

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

8 Scopus citations

Abstract

Data fusion enables the integration of multiple sensing data associated with the same physical process for more comprehensive process representation, thereby improving quality control in manufacturing. Based on a correlation analysis of measurement data, the effectiveness of data fusion has been investigated, using precision injection moulding as an application context and the predicted weight of moulded parts as a criterion. The study systematically explains why a multivariate sensor that quantifies four parameters at the same sensor location has consistently outperformed multiple single-parameter commercial sensors under various operation conditions, thereby contributing to the theory of data fusion for measurement enhancement.

Original language	English
Title of host publication	I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference
Subtitle of host publication	Measuring the Pulse of Industries, Nature and Humans, Proceedings
ISBN (Electronic)	9781467392204
DOIs	https://doi.org/10.1109/I2MTC.2016.7520465
State	Published - Jul 22 2016
Event	2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016 - Taipei, Taiwan, Province of China Duration: May 23 2016 → May 26 2016

Publication series

Name	Conference Record - IEEE Instrumentation and Measurement Technology Conference
Volume	2016-July
ISSN (Print)	1091-5281

Conference

Conference	2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016
Country/Territory	Taiwan, Province of China
City	Taipei
Period	5/23/16 → 5/26/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Funding

This work was sponsored by the National Science Foundation under CMMI-1000816/1000551

Funders	Funder number
National Science Foundation (NSF)	CMMI-1000816/1000551

Keywords

Accuracy
Data Fusion
Measurement

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/I2MTC.2016.7520465

Cite this

Fan, Z., Gao, R. X., Wang, P., & Kazmer, D. O. (2016). Multi-sensor data fusion for improved measurement accuracy in injection molding. In I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings Article 7520465 (Conference Record - IEEE Instrumentation and Measurement Technology Conference; Vol. 2016-July). https://doi.org/10.1109/I2MTC.2016.7520465

Fan, Zhaoyan ; Gao, Robert X. ; Wang, Peng et al. / Multi-sensor data fusion for improved measurement accuracy in injection molding. I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings. 2016. (Conference Record - IEEE Instrumentation and Measurement Technology Conference).

@inproceedings{9c34c050537f40049d56178d8aa46198,

title = "Multi-sensor data fusion for improved measurement accuracy in injection molding",

abstract = "Data fusion enables the integration of multiple sensing data associated with the same physical process for more comprehensive process representation, thereby improving quality control in manufacturing. Based on a correlation analysis of measurement data, the effectiveness of data fusion has been investigated, using precision injection moulding as an application context and the predicted weight of moulded parts as a criterion. The study systematically explains why a multivariate sensor that quantifies four parameters at the same sensor location has consistently outperformed multiple single-parameter commercial sensors under various operation conditions, thereby contributing to the theory of data fusion for measurement enhancement.",

keywords = "Accuracy, Data Fusion, Measurement",

author = "Zhaoyan Fan and Gao, \{Robert X.\} and Peng Wang and Kazmer, \{David O.\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016 ; Conference date: 23-05-2016 Through 26-05-2016",

year = "2016",

month = jul,

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doi = "10.1109/I2MTC.2016.7520465",

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Fan, Z, Gao, RX, Wang, P & Kazmer, DO 2016, Multi-sensor data fusion for improved measurement accuracy in injection molding. in I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings., 7520465, Conference Record - IEEE Instrumentation and Measurement Technology Conference, vol. 2016-July, 2016 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2016, Taipei, Taiwan, Province of China, 5/23/16. https://doi.org/10.1109/I2MTC.2016.7520465

Multi-sensor data fusion for improved measurement accuracy in injection molding. / Fan, Zhaoyan; Gao, Robert X.; Wang, Peng et al.
I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings. 2016. 7520465 (Conference Record - IEEE Instrumentation and Measurement Technology Conference; Vol. 2016-July).

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

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AB - Data fusion enables the integration of multiple sensing data associated with the same physical process for more comprehensive process representation, thereby improving quality control in manufacturing. Based on a correlation analysis of measurement data, the effectiveness of data fusion has been investigated, using precision injection moulding as an application context and the predicted weight of moulded parts as a criterion. The study systematically explains why a multivariate sensor that quantifies four parameters at the same sensor location has consistently outperformed multiple single-parameter commercial sensors under various operation conditions, thereby contributing to the theory of data fusion for measurement enhancement.

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M3 - Conference contribution

AN - SCOPUS:84980383787

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Fan Z, Gao RX, Wang P, Kazmer DO. Multi-sensor data fusion for improved measurement accuracy in injection molding. In I2MTC 2016 - 2016 IEEE International Instrumentation and Measurement Technology Conference: Measuring the Pulse of Industries, Nature and Humans, Proceedings. 2016. 7520465. (Conference Record - IEEE Instrumentation and Measurement Technology Conference). doi: 10.1109/I2MTC.2016.7520465

Multi-sensor data fusion for improved measurement accuracy in injection molding

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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