TY - JOUR
T1 - A roadmap for Assembly 4.0: self-configuration of fixed-position assembly islands under Graduation Intelligent Manufacturing System
AU - Guo, Daqiang
AU - Zhong, Ray Y.
AU - Ling, Shiquan
AU - Rong, Yiming
AU - Huang, George Q.
N1 - Funding Information:
This work was supported by National Key Research and Development Program of China: [Grant Number 2018YFB1702800]; Shenzhen Science, Technology and Innovation Commission Support Program: [Grant Number KQJSCX20170728162555608]; Hong Kong ITF Innovation and Technology Support Program: [Grant Number ITP/079/16LP].
Publisher Copyright:
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2020/8/2
Y1 - 2020/8/2
N2 - The layout of fixed-position assembly islands (FPAI) is widely used for producing fragile or bulky products. With the increasing customised demand and unique operation patterns, manufacturing practitioners are facing challenges on flexible and efficient production arrangement to meet customer demand, which lead to inappropriate assembly islands configuration, frequent setups and long waiting times in FPAI. Industry 4.0 comes with the promise of improved flexibility and efficiency in manufacturing. In the context of Industry 4.0, this paper proposes a 5-layer APICS (assembly layer, perception layer, interaction layer, cognition layer, and service layer) roadmap for transformation and implementation of Assembly 4.0. Following the 5-layer APICS roadmap, a Graduation Intelligent Manufacturing System (GiMS) is presented as the pioneering implementation in FPAI. A graduation-inspired assembly system is designed for FPAI at assembly layer. Internet of Things (IoT) and industrial wearable technologies are deployed for perception, connection, and collaboration among various manufacturing resources at perception and interaction layer. A self-configuration model is proposed at cognition layer for autonomously configuring optimal assembly islands and corresponding production activities to meet customer demand. Cloud-based services are developed for managers and onsite operators to facilitate their decision-making and daily operations at service layer. Finally, a demonstrative case is conducted to verify the feasibility of the proposed methods.
AB - The layout of fixed-position assembly islands (FPAI) is widely used for producing fragile or bulky products. With the increasing customised demand and unique operation patterns, manufacturing practitioners are facing challenges on flexible and efficient production arrangement to meet customer demand, which lead to inappropriate assembly islands configuration, frequent setups and long waiting times in FPAI. Industry 4.0 comes with the promise of improved flexibility and efficiency in manufacturing. In the context of Industry 4.0, this paper proposes a 5-layer APICS (assembly layer, perception layer, interaction layer, cognition layer, and service layer) roadmap for transformation and implementation of Assembly 4.0. Following the 5-layer APICS roadmap, a Graduation Intelligent Manufacturing System (GiMS) is presented as the pioneering implementation in FPAI. A graduation-inspired assembly system is designed for FPAI at assembly layer. Internet of Things (IoT) and industrial wearable technologies are deployed for perception, connection, and collaboration among various manufacturing resources at perception and interaction layer. A self-configuration model is proposed at cognition layer for autonomously configuring optimal assembly islands and corresponding production activities to meet customer demand. Cloud-based services are developed for managers and onsite operators to facilitate their decision-making and daily operations at service layer. Finally, a demonstrative case is conducted to verify the feasibility of the proposed methods.
KW - Assembly 4.0
KW - cloud-based services
KW - fixed-position assembly
KW - intelligent manufacturing system
KW - self-configuration
UR - https://www.scopus.com/pages/publications/85085482720
U2 - 10.1080/00207543.2020.1762944
DO - 10.1080/00207543.2020.1762944
M3 - Journal article
AN - SCOPUS:85085482720
SN - 0020-7543
VL - 58
SP - 4631
EP - 4646
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 15
ER -