Framework of knowledge management for human–robot collaborative mold assembly using heterogeneous cobots

Yee Yeng Liau () and Kwangyeol Ryu ()
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Yee Yeng Liau: Pusan National University
Kwangyeol Ryu: Pusan National University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 7, 3713-3729

Abstract: Abstract Molds are assembled manually due to a shortage of skilled workers and challenges associated with automating operations, which arise from the low-volume, high-variety characteristics of mold production. This study proposed a human–robot collaborative mold assembly using two heterogeneous collaborative robots to address the ergonomic concerns. The use of two heterogeneous cobots enables the handling of different assembly requirements. The diversity of mold structure and different specifications of resources require comprehensive knowledge management to enable interaction and collaboration among resources. However, knowledge management in the domain of mold assembly is yet to be developed in a format understandable by both human and robots. Therefore, a framework of knowledge management is proposed to manage the knowledge within the human–robot collaboration (HRC) in a mold assembly domain. This framework includes an ontology-based decision making that utilizes outcomes from task assignment to decide the mold parts arrangement within the HRC workspace. A set of rules are modeled in the developed ontology for knowledge reasoning according to the use case of collaborative assembly of two-plate injection mold. In addition to part arrangement, the developed HRC ontology can be used to extract data and information based on user’s request and decisions, such as tool selection for subtask execution. The HRC mold assembly ontology serves as a stepping stone towards developing a context-based decision making for multi-resources HRC in future implementation.

Keywords: Human–robot collaboration; Knowledge management; Ontology; Mold assembly; Collaborative robot (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-024-02439-7

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