Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development

Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical program- ming model is presented to predict the change propagation impact quantitatively. As the foundation of change propa- gation prediction, a design change analysis model（DCAM） is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity（CPI） is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by tour assessment factors： propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimiza- tion（ACO） algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The pro- posed change propagation prediction method is verified tobe efficient and effective, which could provide different results according to various the initial changes.

A New Approach to Analyzing Behavioral Change Propagation in Redesigning of Drone Camera Stabilizer

Redesigning of complex products is not an easy task. Engineering change requirements can be extracted at any stage of the product redesign process, and it makes the management of engineering change become a challenging mission. The motivation for this study is to find the shortest path of behavioral change propagation (BCP), minimize the BCP, access to the special behavioral elements in order to better managing the BCP and classifying the behavioral attribute of the elements in terms of their relationship to change by betweenness centrality coefficient (BNCC), clustering coefficient (CLC), reachability coefficient (RC) and change propagation index (CPI). In this article, the procedure of managerial decision-making is proposed by combining system restrictions in behavioral clustering design structure matrix (BCDSM) with optimization algorithms. The practicality of suggested method is verified in redesign procedure of a phantom drone camera stabilizer as a case study. The results, indicate that the absorption of change by behavioral elements is dominant in the mechanical (63.9%), electrical (61.1%) and thermal (38.9%) behaviors of the drone camera stabilizer system in redesign process. These elements are best candidates for reducing the cost and time of behavioral changes in the system redesign and are desirable for the designer.