Determining optimal granularity level of modular product with hierarchical clustering and modularity assessment

Abstract

Modular product architecture is beneficial for the product maintenance, upgrade, components' concurrent design through team work, and achieving the mass customization eventually. Recent researches tend to group elements into modules as a flat map, but this is inconsistent with the nested composition in product final assembly. Finding the hierarchical modular partition for elements of a product and obtaining its optimal granularity level are still necessary to ease the partition and combination for sub-design tasks. We use the design structure matrix (DSM) to represent the relationships among elements of a product. The hierarchical clustering functions such as pdist and linkage within MATLAB software are utilized to form the hierarchical dendrogram for DSM elements, and the modularity index Q is applied to assess the modular clustering results. The partitions are acquired by various distance threshold values in the vertical axis of the hierarchical dendrogram. The 'cityblock' and 'average' are found as preferred parameters for pdist and linkage functions, respectively, from loop test experiments. Combining the modularity formula in a research literature, a hierarchical modular architecture design methodology is proposed in this work to determine the optimal granularity level of a modular product and to provide solutions for designer selection. Case studies and comparisons in a benchmark problem and a concrete spraying machine illustrate the effectiveness and efficiency of the proposed method. It is characterized by being able to find stable modular partition results for both float and binary DSM elements and satisfying the recommended modular number rule simultaneously.

Dendrogram-directed design space exploration for DSM-based product modularization

“…AlGeddawy and ElMaraghy (2017) proposed a methodology that uses the hierarchical classification technique "Cladistics" to organize components hierarchically and select module configuration in an intuitive manner of system decomposition. Li et al(2019) proposed a modularization method to determine the optimal granularity levels with hierarchical clustering. Their methods perform modularization by maintaining a branching form.…”

An Integrated Method for Modular Design Based on Auto-Generated Multi-Attribute DSM and Improved Genetic Algorithm

“…Therefore, the criterion for selecting the modular object adaptive function should be to analyze whether it can effectively evaluate the pros and cons of the module division scheme. In related research work, scholars have proposed many indexes to evaluate product module division schemes, such as the partition coefficient (PC(c)) [36], the modularity index (MI) [37], the minimum description length [38], the integrative complexity (IC) [39], and the modularity assessment index (Q) [15]. Among them, the modularity Q, as an index introduced from the complex network theory, has been popular in the field of modular design in recent years.…”

“…Modular design concepts and methods have been used in many electromechanical product design processes, such as coffee makers [10], industrial steam turbines [11], wind turbines [12], and large tonnage crawler cranes [13,14]. The modular design methods, as the crucial supporting technique for the design of mechanical and electronic products, are still regarded as a research hot spot in product design for life cycle and sustainability [15].…”

Modular design for optimum granularity with auto-generated DSM and improved elbow assessment method

“…However, the specialized Biology classification tool may be hard for the engineer to operate on. Li et al 14 developed a method to cluster component into multiple dendrogram level. To evaluate the granularity of modules, the distance in the vertical axis is divided into several sections, and each corresponding granularity level is measured by the modularity index Q .…”

“…Aiming to facilitate module combination and replacement, the ideal module partition is often with the maximum internal relationship and the minimum external relationship. 14 To achieve such structure, some assessment indexes are adopted to select optimal module partition spectrum. These indexes, take a specific objective function for clustering evaluation, can be broken down into two categories: data-based index and connection-based index.…”