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A data-driven method of selective disassembly planning at end-of-life under uncertainty

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Abstract

Selective disassembly is a systematic method to remove target components or high-valuable components from an EOL product for reuse, recycling and remanufacturing as quick and feasible as possible, which plays a key role for the effective application of circular economy. However, in practice, the process of selective disassembly is usually characterized by various unpredictable factors of EOL products. It is very difficult to identify a feasible disassembly sequence for getting the target components before taking actions due to the uncertainty. In this paper, a data-driven method of selective disassembly planning for EOL products under uncertainty is proposed, in which disassemblability is regarded as the degree of difficulty in removing components under uncertainty. Taxonomy of uncertainty metrics that represents uncertain characteristics of components and disassembly transitions of selective disassembly is established. Random and fuzzy assessment data of uncertainty is converted into qualitative values and aggregated to fit a prediction model based on the trapezium cloud model. The turning time of disassemblability is predicted for a given set of certainty degree. Further, the disassemblability values are applied to determine the best selective disassembly sequence in order to get target component with tradeoff between minimum number of disassembly operations and maximum feasibility. The effectiveness of the proposed method is illustrated by a numerical example. Moreover, by comparing to selective disassembly planning without considering uncertainty, the proposed method turns selective disassembly of EOL products more realistic than 11% and provide insights on how to design product to facilitate disassembly operations.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 51805472, 51975386), the Natural Science Foundation of Zhejiang Province (No. LZ21E050004) and China Postdoctoral Science Foundation (2021M690312).

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Authors and Affiliations

  1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China

    Yicong Gao, Shanhe Lou & Jianrong Tan

  2. Hangzhou Innovation Institute, Beihang University, Hangzhou, China

    Hao Zheng

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  1. Yicong Gao
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  2. Shanhe Lou
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  3. Hao Zheng
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  4. Jianrong Tan
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Correspondence to Hao Zheng.

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Appendices

Appendix 1

See Tables 1, 2, 3, 4, 5 and 6.

Table 1 Assessment grades of physical condition
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Table 2 Assessment grades of accessibility
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Table 3 Assessment grades of disassembly pattern
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Table 4 Assessment grades of mating face
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Table 5 Assessment grades of connection type
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Table 6 Assessment grades for quantity and variety of connections
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Appendix 2

See Table 7.

Table 7 The details of main components
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Appendix 3

See Tables 8 and 9.

Table 8 Parameters of TCM of each component
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Table 9 The aggregated uncertainty assessments for each component
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Gao, Y., Lou, S., Zheng, H. et al. A data-driven method of selective disassembly planning at end-of-life under uncertainty. J Intell Manuf 34, 565–585 (2023). https://doi.org/10.1007/s10845-021-01812-0

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