A Prediction Method for Design Change Propagation in Complex Mechanical Products Based on Immune Entities

Abstract To enhance competitiveness by reducing design change costs, enterprises must limit the propagation of changes from key components in complex mechanical products, particularly when such changes trigger ripple effects. This paper proposes a temporal design change propagation model in which immune entities are defined as key components whose received change impacts are required to be constrained within a specified propagation threshold to prevent large-scale propagation, along with an immune-entity-based propagation likelihood backtracking optimization algorithm to achieve this objective. First, an inter-component relationship matrix is constructed by integrating functional and structural matrices. Module analysis and component-importance analysis are then used to identify key components within each module, which are designated as candidate immune entities. Second, an improved breadth-first search, guided by the priority of component completion times, is applied to determine the propagation sequence. This process accounts for change duration, comprehensive interrelationships and change impacts to quantify dynamic change propagation likelihood. Third, branch-node backtracking and immune-branch propagation likelihood optimization, based on propagation history and immune entity change thresholds, are employed to prevent further propagation through immune entities. The identified candidate immune entity combinations sets are then integrated into the propagation process to determine the immune entity corresponding to minimal change cost. Finally, a case study on an air conditioner product demonstrates that the proposed method can dynamically identify immune entities and reduce workload by 3.1%-17.4%.