Automatic generation of assembly hierarchies for products with complex liaison relations

ABSTRACT

The assembly hierarchy for a product design determines the subassembly module formation, assembly tasks for the modules and serial-parallel material flow among these tasks. Automatic generation of the candidate assembly hierarchies by computer algorithms is a critical step to exploring potential design space for the assembly system design, and existing research on assembly sequence generation and subassembly identification has limitations in dealing with this challenge. This paper proposes to use assembly hierarchy instead of assembly sequence to generate the design space for assembly system design and optimisation. Based on liaison graphs, this paper first characterises the assembly hierarchy by developing a unique representation model to capture the hierarchical relationship among the assembly operations. A recursive algorithm is then developed to search the candidate design space and facilitate the computer implementation of assembly system configuration design. Two case studies including a real-world laptop assembly demonstrate the effectiveness of the proposed algorithm in the reduction of repetitive exploration of design space and avoidance of missing scenarios for assembly system configuration design by comparing with state-of-the-art assembly sequence generation algorithms. The method can lead to an automated tool to evaluate the manufacturability of product designs and optimise assembly system configuration design.

KEYWORDS:

• Assembly system design
• assembly planning
• assembly hierarchy
• automatic generation algorithm

Acknowledgments

This research is partially supported by an NSF grant HRD-1646897 and has been conducted at the FAMU-FSU College of Engineering. The authors also thank Prof. S. Jack Hu at the University of Michigan and Dr. Yhu-tin Lin from the GM Technical Center for providing industry backgrounds that motivate this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science Foundation Grants CMMI-1901109 and HRD-1646897.