Identifying risky components is crucial to improving product reliability in the final redesign of products. Design failure mode and effects analysis has become a prevalent application in product redesign as a useful risk assessment method. However, critical data, which contain failure causality relationships (FCRs) between failure modes, correlations among risk factors, and user attention index of the product component, are not considered. This study develops an improved approach for identifying the target risky components considering importance index, user attention, and FCRs based on the design risky component (DRC) and nonlinear optimization model. The DRC, which integrates the customer requirement level, quality test level, and failure risk information of product components, is proposed to represent the risk degree of product components. The nonlinear optimization models are constructed to derive the weights of risk factors and final redesign of product components. Firstly, a two-stage fuzzy quality function deployment is established to map the importance index of customer requirements under a trapezoidal fuzzy number. A local–global normalization measure is implemented to calculate the index of user attention based on quality test data. Secondly, the FCRs of failure modes between or within product components are characterized by a directed network model. In this network, the failure modes are modeled as vertices, and the causality relationships among failure modes are modeled as directed edges. The values of the directed edges are characterized by weighted risk priority numbers, and the weight of risk factors is optimized by a nonlinear optimization model. Then, the FCRs incorporate the internal failure effect and the external failure effect, which are characterized by PROMETHEE II with the net flow. A 0–1 optimization model with the maximum redesign value and resource constraints of product components is constructed to decide on the final redesign of target risky components. Finally, a real-world case of display product is conducted to demonstrate the validity and feasibility of the proposed approach. The results demonstrate that the proposed method is more effective in identifying risk components.
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This research is supported by the National Natural Science Foundation of China (No. 51975495) and the Ministry of Science and Technology of China (No. 2020IM010100).
This work was supported by the National Natural Science Foundation of China [grant number 51975495] and the Ministry of Science and Technology of China [grant number 2020IM010100].
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Lian, X., Hou, L., Zhang, W. et al. Identifying risky components of display products for redesign considering user attention and failure causality. Soft Comput 27, 2921–2942 (2023). https://doi.org/10.1007/s00500-022-07660-1
- Design failure mode and effects analysis
- Fuzzy quality function deployment
- Failure causality relationships
- Design risky component
- Optimization model
- Display products