Digital thread enabled manufacturing automation towards mass personalization

Abstract

Manufacturing industries are moving towards mass personalization, which refers to the rapid production of individualized products, with large scale efficiencies. This shift from push-type mass customization to pull-type mass personalization will pose critical operational challenges to manufacturing businesses, with complexities ranging from effective requirements elicitation to design, manufacturing, commissioning and after-sales support. Aiming at addressing these challenges, a feasible operational framework for enabling efficient manufacturing automation for mass personalization is proposed in this paper. A key element of this operational framework is the Digital Thread, which streamlines information flow associated with design, manufacturing, maintenance and servicing of a personalized product, each of which are represented as Digital Twins. An As-Designed Digital Twin is created from the beginning of the product co-design process, which then evolves into the subsequent design and manufacturing process and systems resulting in As-Designed Digital Twin evolving to As-Planned Digital Twin and then to As-Built Digital Twin. The personalized product, after it’s commissioning and installation constitutes the As-Maintained Digital Twin of the product, which stores product data related to field performance. The data exchange and communications between these Digital Twins that reside in the various departments of the organization and the management systems create a seamless Digital Thread, capturing the lifecycle information of each personalized product. Personalized product is proposed to be developed through a self-organizing shopfloor, working on a multi-agent mechanism and controlled by a central agent control algorithm, which can coordinate and provide individualized process plans. The Digital Twins, interlinked by a Digital Thread and realized by a self-organizing shopfloor, thus result in increased level of automated control in engineering and manufacturing. To validate the feasibility of this proposed framework, we tested the information flow in the Digital Thread with a case study in the construction industry. Finally the challenges faced by such an automation framework and the area of future work are also discussed. Copyright © 2020 ASME

Publication
ASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020
Akhilnandh Ramesh
Akhilnandh Ramesh
PhD Student
Zhaojun Qin
PhD Student

My research interests include smart manufacturing, self-organizing manufacturing system and reinforcement learning.

Yuqian Lu
Yuqian Lu
Principle Investigator / Senior Lecturer

My research interests include smart manufacturing systems, industrial AI and robotics.