A multimedia case-based reasoning framework for assembly sequence planning

Abstract

Purpose Assembly sequence planning (ASP) is a crucial job during assembly process design. However, it is still difficult to reuse the existing solution to solve a new ASP problem. In particular, with the rapid development of digital technologies, the reusable assembly information of an existing solution is not concentrated in one multimedia but dispersed in multiple heterogeneous multimedia, e.g. text, three-dimensional graphics, even images and videos. This paper aims to propose a multimedia case (MC)-based reasoning framework to solve ASP by reusing the existing solution whose assembly information is dispersed in multimedia. Design/methodology/approach The proposed framework is designed with the introduction of the MC. An MC seamlessly integrates the dispersed assembly information of an existing solution. Under the proposed concept and architecture, the assembly information of an existing solution is extracted to build assembly descriptors of multimedia. Therefore, the MC is captured by organizing the assembly descriptors of corresponding multimedia. Findings By means of the framework proposed, it is possible to reuse the existing solution whose assembly information is dispersed in multimedia to solve ASP. Moreover, the extraction method of assembly information can flexibly parse most of the multimedia. Finally, the MC has the capability to represent the existing solution by collecting dispersed assembly information. Originality/value The proposed framework can discover the similar existing solution and avoid the potential failures confronted in the past so that the feasibility of ASP result can be improved as much as possible.

A review of the development and future challenges of case-based reasoning

“…For example, Abdelwahed et al (2020) proposed using learning experience to solve motion planning problems and using CBR technology to provide state samples associated with the problem to achieve path building and find the shortest path. Chen et al (2019) proposed a CBR framework for assembly sequence planning (ASP). By establishing the overall architecture of CBR, the extraction, aggregation and reuse of assembly information of existing solutions are achieved, which can analyze the collection information in heterogeneous multimedia and reuse the existing assembly information scattered in multimedia solutions to solve assembly sequence planning.…”

“…The core idea is to use the solutions of similar problems from past cases to reason and solve new problems based on a cognitive hypothesis: similar problems have similar solutions (Aleven 2003). Since CBR was proposed, it has gradually formed a developed reasoning model framework and has become an effective and practical AI technology that is widely used in industrial control (Ni et al 2021), emergency decisions (Chen et al 2020), planning and design (Chen and Jia 2019), medical diagnosis (Duan et al 2022) and other fields. Case-based reasoning (or case based reasoning) is input into the search bar of the EI database and limited to the title, abstract, and topic.…”

Knowledge-enabled digital twin for smart designing of aircraft assembly line

“…That is, the lack of knowledge support among these approaches prevents applying DT in assembly line design (Boje et al , 2020). With these observations, based on previous research about knowledge capture (Chen and Jia, 2019), knowledge representation (Chen and Jia, 2020) and knowledge-based intelligent skills (Hu et al , 2020), this paper introduces combining knowledge and DT toward AAL smart design, while analyzing its key enabling technologies including dynamic design knowledge library (DDK-Lib), knowledge-driven digital AAL rapid modeling and knowledge-based smart evaluation.…”

“…As shown in Figure 6, retrieve design cases in the DDK-Lib according to the established requirement model (including product topology and assembly relation semantics). The detailed retrieval method was completed in the previous work (Chen and Jia, 2019). Having defined the theoretical design scheme, it will be used to build digital AAL using the pre-defined reference model in the DDK-Lib.…”

Research on assembly sequence planning method based on artificial neural networks

“…Current research on assembly sequence planning can be categorized into two primary approaches: the first revolves around generating feasible sequences through constraint-based reasoning and then optimizing them (methods include constraint reasoning [12] , decomposition [13] , knowledge [14] and case-based approaches [15] ); the second involves modern optimization techniques that evolve feasible sequences and filter them during the process (methods like genetic algorithms [16] , simulated annealing [17] , etc.). The first approach generally manages products with fewer than 20 parts due to computational limitations, which directs the research towards modern optimization algorithms.…”