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Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 33, Number 2(3) (2021)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 755-761
S&M2492 Research Paper of Special Issue https://doi.org/10.18494/SAM.2021.3042 Published: February 26, 2021 Processing Cycle Prediction Using Support Vector Regression in Intelligent Manufacturing [PDF] Wencan Tong, Hsien-Wei Tseng, and Zhiqiang Huang (Received July 20, 2020; Accepted January 6, 2021) Keywords: big data technology, intelligent manufacturing equipment, processing cycle, cycle prediction
The processing cycle in an intelligent manufacturing machine (IMM) is difficult to predict accurately owing to uncertainties caused by unexpected maintenance errors and damage. Thus, a new method for accurate prediction is required. We propose a new prediction method using an algorithm based on support vector regression (SVR) in this study. The new method uses big data and determines its logical relationship with a processing cycle to obtain an accurate prediction of the cycle. The accuracy of the SVR method (>95%) is better than that of the traditional method (79.3‒89.6%). The result proves that the method predicts the processing cycle accurately and provides essential information for developing algorithms for designing processing cycles in an IMM.
Corresponding author: Wencan Tong  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Wencan Tong, Hsien-Wei Tseng, and Zhiqiang Huang, Processing Cycle Prediction Using Support Vector Regression in Intelligent Manufacturing, Sens. Mater., Vol. 33, No. 2, 2021, p. 755-761. |
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