Design change propagation routing in the modular product. (October 2022)

• Record Type: Journal Article
• Authors: Li, Ting; Zhang, Na; Ren, Haibing; Liu, Qinming; Li, Yupeng
• Is Part Of: Advanced engineering informatics. Volume 54(2022)
• Publication Date: 2022-10
• Subjects: Design change routing -- Modular product -- The correlation between modules -- Penalty mechanism -- Multiple-population genetic algorithm; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101784
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: In product engineering, design changes are unavoidable because of the evolution of customer requirements and key technologies. Modularization has been extensively applied as an effective strategy for the development of complex products. In a modular product, although modules are supposed to be functionally independent, the design change in a module can still propagate to other modules because of the interfaces between modules. This propagation leads to numerous potential propagation paths and uncontrollable change in costs. Thus, it is valuable to consider the impact of the modular structure during the optimization of design change propagation. This research offers a glimpse into design change propagation routing in modular products to accelerate design change propagation within the modules. First, the relationships between components were determined to construct the product network model. Subsequently, an optimization model of the design change propagation path was established to minimize the change propagation intensity, which includes the importance of the components, change in cost, and path length. To decrease the number of modules that the design change propagates into, the correlation between the modules was explored as penalties, considering the dependencies of a module on the interface(s) that are defined from aspects of the topological characteristics, change in the propagation characteristics, and cost of design changes. A multiple-population genetic algorithm was developed to solve this model. Finally, a case study of the design change propagation routing of a potato lifter was implemented to expound on the utility and effectiveness of the proposed approach.