Toward an action-granularity-oriented modularization strategy for complex mechanical products using a hybrid GGA-CGA method

Neural Computing and Applications(2022)

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Modular technology is a mainstream industrial trend, especially in manufacturing transformation and upgrading. Modularization is fundamental in modular technology and plays an important role in modular production processes, such as modular design, manufacturing, and assembly. Previous modularization studies have neglected large-scale and complex mechanical products. Furthermore, the traditional modularization method generates coarse-grained modular results with complex structures and specific functions; these modules are difficult to standardize and apply in modular production processes for cross-family products. Therefore, this study proposes an action-granularity-oriented modularization strategy to obtain finer-granularity modules and emphasizes the simplicity, fundamentality, and typicality of these modules to increase their generality. This strategy clusters components into different modules at the action level by first analyzing the association strengths among the components based on the concept of key action components and a modularity-driven factor system. Then, with the help of the design structure matrix (DSM) theory and the evidence theory, the association information is synthesized to construct a synthetic association DSM. Additionally, a new modularization method combining the grouping genetic algorithm (GGA) and constrained genetic algorithm (CGA), called the hybrid GGA-CGA method, is developed to search for the optimal modular schemes among all possible solutions based on the synthetic association DSM. Finally, a modularization case for a typical complex mechanical product (a winding engine) is used to demonstrate the feasibility and effectiveness of the proposed hybrid method in addressing modularization.
Modularization,Complex mechanical products,Action-granularity module,Hybrid GGA-CGA method
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