General Adaptable Design and Evaluation Using Markov Processes

Facing the challenges posed by increasingly complex, dynamic, and unforeseen requirements, the design process is grappling with the critical issue of ensuring sustained product satisfaction amid changing demands. This paper introduces an approach for evaluating design adaptability, considering potential future requirements. Entropy serves as a crucial indicator to quantify design effort and the Markov process is employed to simulate potential requirement changes. The information contents of design requirements and design solutions are defined based on information entropy theory, and the design adaptability of a design candidate is evaluated by calculating the extra design effort for satisfying the design requirements, which is the difference in information content between the design candidate and design requirements. Moreover, a simulation method for requirement evolution is proposed, which integrates information entropy theory and the Markov process to accommodate potential future requirements. The general design adaptability of design solutions is then calculated based on conditional entropy, taking into account the evolving design requirements. Finally, the effectiveness of the proposed approach is validated through a case study involving the design and evaluation of a hybrid additive manufacturing device.