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Association Rule Mining-Based Generalized Growth Mode Selection: Maximizing the Value of Retired Mechanical Parts
Due to the inability to restore the original performance, a significant portion of retired mechanical products is often replaced with new ones and discarded or recycled as low-value materials. This practice leads to energy waste and a decline in their residual value. The generalized growth remanufacturing model (GGRM) presents opportunities to enhance the residual value of retired products and parts. It achieves this by incorporating a broader range of growth modes compared to traditional restorative remanufacturing approaches. The selection of the growth mode is a crucial step to achieve GGRM. However, there is a limited number of growth mode selection methods that are specifically suitable for GGRM. The capacity and efficiency of the method are also significant factors to consider. Therefore, we propose a growth mode selection method based on association rule mining. This method consists of three main steps: Firstly, the ReliefF method is used to select the core failure characteristics of retired parts. Secondly, a genetic algorithm (GA) is employed to identify the association between core failure characteristics, repair technology, and maximum recoverability. Finally, based on the maximum recoverability, the appropriate growth mode is selected for each retired part. We conduct a case study on retired automobile universal transmission, and the results demonstrate the feasibility, efficiency, and accuracy of the proposed method.
Association Rule Mining-Based Generalized Growth Mode Selection: Maximizing the Value of Retired Mechanical Parts
Due to the inability to restore the original performance, a significant portion of retired mechanical products is often replaced with new ones and discarded or recycled as low-value materials. This practice leads to energy waste and a decline in their residual value. The generalized growth remanufacturing model (GGRM) presents opportunities to enhance the residual value of retired products and parts. It achieves this by incorporating a broader range of growth modes compared to traditional restorative remanufacturing approaches. The selection of the growth mode is a crucial step to achieve GGRM. However, there is a limited number of growth mode selection methods that are specifically suitable for GGRM. The capacity and efficiency of the method are also significant factors to consider. Therefore, we propose a growth mode selection method based on association rule mining. This method consists of three main steps: Firstly, the ReliefF method is used to select the core failure characteristics of retired parts. Secondly, a genetic algorithm (GA) is employed to identify the association between core failure characteristics, repair technology, and maximum recoverability. Finally, based on the maximum recoverability, the appropriate growth mode is selected for each retired part. We conduct a case study on retired automobile universal transmission, and the results demonstrate the feasibility, efficiency, and accuracy of the proposed method.
Association Rule Mining-Based Generalized Growth Mode Selection: Maximizing the Value of Retired Mechanical Parts
Yuyao Guo (author) / Lei Wang (author) / Zelin Zhang (author) / Jianhua Cao (author) / Xuhui Xia (author)
2023
Article (Journal)
Electronic Resource
Unknown
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