Product Quality Prediction with Deep Transfer Learning for Smart Factories

Jehn Ruey Jiang, Zi Kuan Cheng

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

Abstract

This paper proposes to use deep transfer learning (DTL) 'layer freezing' method to build deep neural network (DNN) models for a target domain with few data on the basis of well-trained DNN models for a source domain with abundant data. Experiments using the DTL method are conducted for building DNN models to predict product surface roughness of wire electrical discharge machining (WEDM). The experimental results show that DTL indeed can help fast build models with high prediction accuracy for the target domain having few data.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173993
DOIs
StatePublished - 28 Sep 2020
Event7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
Duration: 28 Sep 202030 Sep 2020

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Country/TerritoryTaiwan
CityTaoyuan
Period28/09/2030/09/20

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