A Resilient Product Design Model Based on Functional Backtrace and Its Evaluation Method

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ID: 319198
2026
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Abstract
Abstract Products are facing an increasing number of high-impact disruptions that can cause catastrophic failures. Although periodic maintenance, structural reinforcement, and optimization improve reliability, products suffer failure due to inadequate preparedness under high-impact disruptions. Resilient products absorb disruptions, adapt to dynamic environments, and recover from failure, which extends product life and effectively responds to high-impact disruptions. However, endowing products with resilience during the design phase poses two challenges to the existing design models: (a) These models require clearly defined design objectives in the early stage, but the resilient design needs to consider potential structural failures caused by disruptions, which are difficult to specify early on. (b) How to minimize its side effects while enhancing product resilience, such as cost fluctuations and reductions in user satisfaction. To address these issues, a resilient product design model based on functional backtrace and its evaluation method is proposed. High-impact disruptions evolve resilient design principles into requirements to guide functional-structural mapping. Risky functions and structures are identified along the disruption-structural failure-functional failure network (DSFN). Resilient design factors, including absorption, adaptation, recovery, learning, and service, are incorporated to enable risky functions and structures to respond to disruptions, thereby enhancing product resilience. Candidate design schemes are configured among these factors based on NSGA-Ⅱ under constraints of cost, weight, and carbon emissions, and ranked via rough VIKOR to meet user requirements. A grab drilling machine example illustrates the effectiveness of the proposed methodology.
Reference Key
openalex_W7166888898 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yang M, Xin Guo, Chong Jiang, Linhong Jiang, Hailong Liao, Wu Zhao, Kai Zhang
Journal journal of computational design and engineering
Year 2026
DOI
10.1093/jcde/qwag063
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