Module Partition of Mechatronic Products Based on Core Part Hierarchical Clustering and Non-Core Part Association Analysis
Year: 2025
Type: article
Abstract: Production using modular architecture can not only shorten the product development cycle and improve the efficiency of product development, but also facilitate the upgrading of a product’s main functions and the recycling of materials. However, mechatronic products are plagued by various problems, such as greater difficulty in development and longer product development cycles due to their large numbers of parts with intricate internal relationships. However, the existing modular design method still faces problems when dealing with the modular design of mechatronic products. The structure of mechanical and electrical products is very complex, which is not conducive to the establishment of a model, and complex structural models lead to low efficiency and poor accuracy of module identification. Therefore, we propose an integrated module division method for mechatronic products based on core part hierarchical clustering and non-core part association analysis. Firstly, the core part screening method is used to simplify the structural model of mechatronic products and reduce the difficulty of modeling. Then, based on the core parts, the corresponding product design structural matrix (DSM) model is established. Secondly, the hierarchical clustering algorithm is used to obtain the module division scheme of different levels of mechatronic products, and the optimal modular scheme is obtained through an evaluation of modularity and a rationality analysis of module structure. Finally, based on the analysis of the association strength between the non-core parts and the existing modules, the non-core parts are classified into the corresponding product modules, and the final modularization scheme is obtained. A case study demonstrates the feasibility of the proposed method through the modular design of an electric bicycle.
Source: Applied Sciences
DOI: 10.3390/app15052322
Authors: Shuai Wang, Yi-Fei Song, Guang-Yu Zou, Jia-Xiang Man
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G4800768479, G7337801290
OpenAlex ID: W4407842328
