A study on Machine Learning Approaches for Predicting and Analyzing the Drying Process in the Textile Industry

Ke Haur Taur, Xiang Yun Deng, Mi Huo Chou, Jing Wei Chen, Yi Hsiu Lee, Wen June Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The main objective of this paper is to establish an output/input relationship model based on machine learning for the fabric drying process of a general textile factory. The scenario of the fabric drying process involves a conveyor belt that drives the fabric through eight drying boxes, and the targeted metric of the post-drying fabric is the moisture content rate. This paper is composed of two main parts. The first part is to explain that how to select a predictive model measuring the output performance of the setting machine. The second part discusses the optimization of energy-saving parameters of multiple models chosen in the first part. This paper will introduce some techniques such as neural networks and machine learning algorithms to find the most suitable output/input relationship model, or so called 'drying process quality prediction model', for future development of energy saving.

Original languageEnglish
Title of host publication2019 International Automatic Control Conference, CACS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728138466
DOIs
StatePublished - Nov 2019
Event2019 International Automatic Control Conference, CACS 2019 - Keelung, Taiwan
Duration: 13 Nov 201916 Nov 2019

Publication series

Name2019 International Automatic Control Conference, CACS 2019

Conference

Conference2019 International Automatic Control Conference, CACS 2019
Country/TerritoryTaiwan
CityKeelung
Period13/11/1916/11/19

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