A General Product Identification Method for Mass Customization based on Deep Learning

Chenxiao Lin, Shigeru Fujimura, Wutie Zhou, Haipeng Chen

研究成果: Conference contribution

抄録

Most manufacturing industries are facing changes due to the increasing competition in the global market. Regarding this current situation, manufacturing firms that offer mass customization usually have a competitive edge over counterparts offering generic products. However, the Mass Production-to-Mass Customization (MP-to-MC) transition has brought upon unprecedented challenges to many Small and Medium Enterprises (SMEs). One of the challenges is to identify customized products in harsh production environments. The reason behind this is that identification tags such as barcodes, Quick Response (QR) codes and Radio Frequency Identification (RFID) cannot function in special production processes like heating and dissolution. It is therefore of prime importance to find a solution by devising a product identification method without making use of marks or tags. In the paper, a novel method that using computer vision to identify the customized products in mass customization and a hybrid Convolutional Neural Network (CNN) model are proposed. To illustrate the efficacy of the proposed method, a case study in a shoe-manufacturing company was reported. The results yielded demonstrated that the proposed method is an efficient and economical solution.

本文言語English
ホスト出版物のタイトルProceeding - 2021 China Automation Congress, CAC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ883-890
ページ数8
ISBN(電子版)9781665426473
DOI
出版ステータスPublished - 2021
イベント2021 China Automation Congress, CAC 2021 - Beijing, China
継続期間: 2021 10月 222021 10月 24

出版物シリーズ

名前Proceeding - 2021 China Automation Congress, CAC 2021

Conference

Conference2021 China Automation Congress, CAC 2021
国/地域China
CityBeijing
Period21/10/2221/10/24

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • エネルギー工学および電力技術
  • 制御およびシステム工学
  • 電子工学および電気工学
  • 安全性、リスク、信頼性、品質管理
  • 制御と最適化
  • 人工知能

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