Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
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.
Sensors and Materials
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.
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


Creative Commons License
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.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Micro-nano Biomedical Sensors, Devices, and Materials
Guest editor, Tetsuji Dohi (Chuo University) and Seiichi Takamatsu (The University of Tokyo)


Special Issue on Artificial Intelligence in Sensing Technologies and Systems
Guest editor, Prof. Lin Lin (Dalian University of Technology)


Special issue on Novel Materials and Sensing Technologies on Electronic and Mechanical Devices Part 3
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Hsien-Wei Tseng (Yango University)


7th Special Issue on the Workshop of Next-generation Front-edge Optical Science Research
Guest editor, Takayuki Yanagida (Nara Institute of Science and Technology)


Special Issue on Sensing and Data Analysis Technologies for Living Environment, Health Care, Production Management and Engineering/Science Education Applications (Selected Papers from ICSEVEN 2020)
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Rey-Chue Hwang (I-Shou University), Ja-Hao Chen (Feng Chia University), Ba-Son Nguyen (Research Center for Applied Sciences)
Call for paper


Special Issue on Materials, Devices, Circuits, and Analytical Methods for Various Sensors (Selected Papers from ICSEVEN 2020)
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Ja-Hao Chen (Feng Chia University), and Yu-Ju Lin (Tunghai University)
Conference website
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.