S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
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Vol. 32, No. 8(2), S&M2292

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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


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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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