Combining Instance Segmentation and Ontology for Assembly Sequence Planning Towards Complex Products

Xiaolin Shi (), Xu Wu, Han Zhang and Xiaolong Xu
Additional contact information
Xiaolin Shi: College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China
Xu Wu: College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China
Han Zhang: College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China
Xiaolong Xu: College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China

Sustainability, 2025, vol. 17, issue 9, 1-23

Abstract: Aiming at the efficiency bottleneck and error risk caused by the over-reliance on manual experience in traditional assembly sequence planning, the urgent demand for deep reuse of multi-source knowledge in complex products, and the growing demand for resource saving and sustainable development, this study focuses on the core problem of the lack of empirical knowledge modeling and reasoning mechanism in the assembly process of complex products, and proposes a three-phase assembly sequence intelligent planning method that integrates deep learning and ontology theory. Method: First, we propose an instance segmentation model based on the improved Mask R-CNN architecture, incorporate the ResNet50 pre-training strategy to enhance the generalization ability of the model, reconstruct the Mask branch, and add the attention mechanism to achieve high-precision recognition and extraction of geometric features of the assembly parts. Secondly, a multi-level assembly ontology semantic model is constructed based on the ontology theory, which realizes the structured expression of knowledge from three dimensions: product structure level (product–assembly–part), physical attributes (weight/precision/dimension), and assembly process (number of fits/direction of assembly), and builds a reasoning system with six assembly rules in combination with the SWRL language, which covers the core elements of geometric constraints, process priority, and so on. Finally, experiments are carried out with the example gearbox as the validation object, and the results show that the assembly sequence generated by the method meets the requirements of the process specification, which verifies the validity of the technology path. By constructing a closed-loop technology path of “visual perception–knowledge reasoning–sequence generation”, this study effectively overcomes the subjective bias of manual planning, integrates multi-source knowledge to improve the reuse rate of knowledge, and provides a solution of both theoretical value and engineering feasibility for the intelligent assembly of complex electromechanical products, which reduces the R&D cost and contributes to the sustainable development.

Keywords: mask R-CNN; UNet3+; instance segmentation; ontology; assembly sequence; manufacturing; circular design (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/9/3958/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/9/3958/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:9:p:3958-:d:1644452

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().