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A hybrid approach of rough set and case-based reasoning to remanufacturing process planning

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Abstract

Remanufacturing, a process returning used products to at least as good as new condition, is increasingly recognized as an important part of the circular economy. Since returned used components for remanufacturing have varying conditions and different defects, remanufacturing is very time-consuming and labor-intensive. There is an urgent need to reuse knowledge generated from existing parts remanufacturing to rapidly create sound process planning for the new arrival of used parts. A hybrid method combing rough set (RS) and cased-based reasoning (CBR) for remanufacturing process planning is presented in this paper. RS is employed for features reduction and rapid determination of features’ weights automatically, and CBR is utilized to calculate the similarity of process cases to identify the most suitable solution effectively from case database. The application of the methodology is demonstrated in an example of remanufacturing process for a saddle guide. The results indicated that the quality of remanufactured products has been improved significantly. The method has been implemented in a prototype system using Visual Studio 2010 and Microsoft SQL Server2008. The results suggested that the hybrid RS–CBR system is feasible and effective for the rapid generation of sound process planning for remanufacturing.

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Abbreviations

B :

An equivalence relation on \(\Omega \)

C :

A set of condition features

\(C_{k}\) :

The \(k{\mathrm{th}}\) condition feature

\(C_i^X \) :

The \(i{\mathrm{th}}\) features of case X

\(C_i^Y \) :

The \(i{\mathrm{th}}\) features of case Y

D :

A decision features set

f :

Information function

H :

Case number

\(L_{j}\) :

Value of \(j{\mathrm{th}}\) feature of reconditioning process

m :

The number of features

M:

Maximum assignment value of the feature

\(M_{j}\) :

Name of \(j{\mathrm{th}}\) feature of reconditioning process

\(N_{i}\) :

Name of \(i{\mathrm{th}}\) essential feature

\(P_{j}\) :

The\( j{\mathrm{th}}\) process case

q :

Each feature

Q :

Vector set of features of reconditioning process

\(Q_{j}\) :

The \(j{\mathrm{th}}\) feature of reconditioning process

r :

An arbitrary feature

R :

Vector set of essential features of used cores

\(R_{i}\) :

The \(i{\mathrm{th}}\) essential feature

S :

Corresponding solution

U :

A nonempty and finite set of objects

\(V_{i}\) :

Value of \(i{\mathrm{th}}\) essential feature

\(V_{q}\) :

Value set of q

\(w(C_k )\) :

The feature weights of condition feature \(C_k \)

\(w_{R_i}\) :

Weight of \(i{\mathrm{th}}\) essential feature

\(w_{Q_j}\) :

Weight of \(j{\mathrm{th}}\) feature of reconditioning process

\(W_D (C_k)\) :

The significance of condition feature \(C_k \)

X :

New case

Y :

Source case

\(\psi \) :

Proportional factor

\(\Omega \) :

A finite set of features

\(\varepsilon \left( {P_j } \right) \) :

The confidence of the \(j{\mathrm{th}}\) process case

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Acknowledgments

The work described in this paper was supported by the National Natural Science Foundation of China (51205295, 51405075), Wuhan Youth Chenguang Program of Science and Technology (2014070404010214). These financial contributions are gratefully acknowledged.

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Authors and Affiliations

  1. School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, 430081, China

    Zhigang Jiang, Ya Jiang & Hua Zhang

  2. Department of Computing, Engineering and Mathematics, University of Brighton, Brighton, BN2 4GJ, UK

    Yan Wang

  3. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Huajun Cao

  4. Transportation College, Jilin University, Changchun, 130022, China

    Guangdong Tian

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  1. Zhigang Jiang
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  2. Ya Jiang
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  3. Yan Wang
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  4. Hua Zhang
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  6. Guangdong Tian
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Correspondence to Zhigang Jiang.

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Jiang, Z., Jiang, Y., Wang, Y. et al. A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. J Intell Manuf 30, 19–32 (2019). https://doi.org/10.1007/s10845-016-1231-0

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  • DOI: https://doi.org/10.1007/s10845-016-1231-0

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