## 2 Related Work
### Module Partition Method  
Module partition involves the identification of the module structure in a product by technical means, based on the product structure model. In the past two decades, module partitions based on function analysis [23], physical architecture [24], manufacturing [25], disassembling [26], and other aspects of the product life cycle have been proposed. Based on the various principles of the module partition algorithm, it can be divided into heuristic algorithm-based methods and cluster algorithm-based methods. Modular methods driven by the heuristic algorithm usually establish the corresponding objective function according to different modular requirements and then use the appropriate heuristic algorithm to obtain the optimal modular solution. [PERSON] and [PERSON] took the number of interfaces between product modules as the objective function of module division and then used the simulated annealing algorithm to obtain the optimal product modularization scheme [27]. To reduce the environmental impact of product recycling, [PERSON] et al. established a comprehensive model based on a material reuse and technology system and used a bi-level optimization model based on a constraint genetic algorithm for the solution [28]. [PERSON] et al. solved a multi-objective optimization model based on minimizing maintenance costs and maximizing product modularity by using an improved-strength Pareto evolutionary algorithm 2 to obtain the optimal Pareto set of product modularity schemes [29]. [PERSON] et al. established a comprehensive DSM structure model and used an improved genetic algorithm to obtain the optimal scheme for the modular design of a bicycle [22]. The modular method based on the clustering algorithm is to use quantitative tools to express the association between parts, and then realize the module division of products according to the intimate relationship between parts. [PERSON] et al. established a DSM model of the shifting system of an equation car based on the function-behavior-state model, and then used the k-means clustering algorithm to obtain its modular scheme [9]. [PERSON] et al. established a DSM model of a gear reducer based on function- and sustainability-related factors, and then determined the optimal module number and the final module division scheme using a genetic algorithm and a kernel-based fuzzy C-means algorithm [30]. On the basis of establishing the function-behavior-structure mapping model of 3D printer, [PERSON] et al. used the K-means clustering algorithm to realize the module division of the printer [31]. [PERSON] and [PERSON] obtained a clustering tree that reflected the internal structure of a product by using the hierarchical clustering algorithm; they then selected the optimal separation granularity to obtain the final modular results [32]. [PERSON] et al. used a modularization index based on product hierarchy clustering to obtain the optimal product modularization scheme and then demonstrated the feasibility of this method for a concrete-spraying machine product [33]. [PERSON] and [PERSON] proposed an improved elbow evaluation method to analyze the results of product hierarchical clustering and applied it to the modular design of jaw crusher products [34]. Researchers have also put forward some other algorithms to realize module division. For example, the community identification algorithm in complex networks was introduced by [PERSON] and [PERSON] into the modular design of a product [8; 35]. [PERSON] et al. proposed a multi-granularity module partition approach for complex mechanical products based on a complex network, and proved the effectiveness and superiority of the method through the module division of elevator products [36]. [PERSON] et al. proposed a module partition method for complex products based on stable overlapping community detection and overlapping component allocation, and used this method to obtain a modularization scheme for CNC grinding machines [37].