Abstract

Manufacturing industry is facing new challenges in that fast-changing demands for products and services from customers push manufacturers to be more flexible and adaptive. The concept of batch-size-of-one production is presented in this paper, which defines a fully automated, highly customised, and short lead time production model. The desired batch-size-of-one production model is a promising solution for the above challenges in manufacturing industry, especially for highly customised or families of similar products like in the mobile phone industry. Along with the concept, we introduce a novel control method that enables the desired batch-size-of-one production model in operation of robots in manufacturing and assembly systems. The strategy was developed for robot control based on a distributed system to enable industrial robots to receive job commands on the fly and to conduct different jobs without the need for reconfiguration and reprogramming and without overheads. The aim of the research is to create the basis for a fully automated robot flexible assembly cell to perform batch-size-of-one assembly tasks with minimal human involvement by eliminating interruptions from the reconfiguration and reprogramming processes. The proposed strategy has been validated in practice in a multi-robot, multi-product flexible assembly cell.

Details

Title
Achieving batch-size-of-one production model in robot flexible assembly cells
Author
Jin, Ziyue 1   VIAFID ORCID Logo  ; Marian, Romeo M. 1 ; Chahl, Javaan S. 2 

 University of South Australia, UNISA STEM, Australian Research Centre for Interactive and Virtual Environments, Mawson Lakes, Australia (GRID:grid.1026.5) (ISNI:0000 0000 8994 5086) 
 University of South Australia, UNISA STEM, Australian Research Centre for Interactive and Virtual Environments, Mawson Lakes, Australia (GRID:grid.1026.5) (ISNI:0000 0000 8994 5086); Defence Science and Technology Organisation, Joint and Operations Analysis Division, Melbourne, Australia (GRID:grid.431245.5) (ISNI:0000 0004 0385 5290) 
Pages
2097-2116
Publication year
2023
Publication date
May 2023
Publisher
Springer Nature B.V.
ISSN
02683768
e-ISSN
14333015
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2806271182
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.