In the design of complex products, change propagation often leads to overlapping impact paths, structural conflicts, and decreased coordination efficiency, especially in multi-source change scenarios. These issues limit the effectiveness of existing path extraction and management methods. This study addresses the challenge of managing multi-source change propagation by proposing a novel optimization method. The method involves constructing a multi-layer propagation network model that integrates structural, functional, and form-level information and combines propagation risk assessment with frequency feedback mechanisms. By employing a path cost function based on node risks and propagation probabilities, the model accurately evaluates the costs of change propagation and identifies high-risk paths while prioritizing those with lower costs. Additionally, a multi-source path combination optimization approach is developed, and it dynamically adjusts path costs to reduce overlap and conflicts, improving coordination between paths in multi-source change scenarios. The proposed approach was validated through a case study based on the design data of an intelligent cabin system, where the constructed propagation network reflects real-world structural, functional, and form-level dependencies. The experimental results show that the proposed method reduces total propagation cost by an average of 27.3% and lowers node-level conflict rate by 41.6% compared with baseline methods. Tests on different complex products confirmed consistent advantages over baselines with $p<{10}^{-5}$, while also reducing redundancy and conflicts. The method maintained these advantages across networks of different sizes with linear scalability. Sensitivity analysis confirms that frequency-feedback weighting $\alpha$ is the main driver for cost reduction, and the overlap penalty $\lambda$ effectively suppresses node congestion. The method provides a robust solution for managing change propagation in complex product design and enhances overall propagation efficiency and system adaptability.

In the process of complex product design, rapid design changes bring product development risk. The study of complex product change propagation is an important means to reduce change risks. Traditional methods model the product structure into a network and predict the impact of change propagation through network attributes. However, these methods ignore the form elements of industrial design based on product structure, and the influence mechanism of the designer in the process of change propagation. To explore the influence of industrial design in change propagation, this study proposes a risk prediction method for industrial design change propagation based on complex network evolution. A comprehensive evaluation method based on network and design scheme correlation characteristics is developed to map the structure layer and the form layer, resulting in a multilayer industrial design network model. By extracting influencing factors from designers, a designer-constrained and driven industrial design change propagation and evolution method is established to simulate the real-world influence of design team behavior and support dynamic risk prediction. Finally, the effectiveness and reliability of the method are verified through an engineering case involving the industrial design change of an intelligent cabin. For industrial design change, it performed with better accuracy and robustness.

In the field of psychology and cognition within closed cabins, noncontact vital sign detection holds significant potential as it can enhance the user’s experience by utilizing objective measurements to assess emotions, making the process more sustainable and easier to deploy. To evaluate the capability of noncontact methods for emotion recognition in closed spaces, such as submarines, this study proposes an emotion recognition method that employs a millimeter-wave radar to capture respiration signals and uses a machine-learning framework for emotion classification. Respiration signals were collected while the participants watched videos designed to elicit different emotions. An automatic sparse encoder was used to extract features from respiration signals, and two support vector machines were employed for emotion classification. The proposed method was experimentally validated using the FaceReader software, which is based on audiovisual signals, and achieved an emotion classification accuracy of 68.21%, indicating the feasibility and effectiveness of using respiration signals to recognize and assess the emotional states of individuals in closed cabins.

Function-behavior-structure (FBS) method models the functional structure and principle scheme in conceptual design by connecting three variables: product function, behavior, and structure. The traditional FBS method cannot deal effectively with the cooperation problem between man and machine in the function achievement process. This paper proposes an FBS-based interactive mechanical product design method by extending the behavior layer. First, define and transform operational behavior from the function to the behavior layer. Second, the genetic engineering method is used in expected operational behavior design. In this part, the operational behavior as genetic information is constructed to operational behavior genes model by Extenics theory. Based on a homology product sample library, screen the target gene through the GloVe model and similarity algorithm. The original operational behavior is then modified to an expected one by Extenics transformation. Finally, the product structure is obtained through an analogy transformation process. This way, the operational behavior problem in the interactive mechanical product can be solved by this FBS design process. The feasibility and effectiveness of the proposed process are verified in the development of an apple picking device.