Abstract
The uncertainty recognition, quantification, and optimization of reassembly (remanufacturing assembly) are crucial to improve the precision, quality, and stability of the assembly of remanufactured products. In this paper, we summarize the uncertainties based on the analysis of reassembly features. An uncertainty measure model using information-entropy theory is then established. This model characterizes the uncertainty of remanufactured parts, reused parts, and reassembly quantitatively. An online quality prediction method for reassembly process is then proposed. The method can effectively estimate the quality of remanufactured products using recursive least squares algorithm, which combines real-time data with tremendous amount of history data from the reassembly process. This method can also maximize the use of tremendous amount of history data and improve the accuracy of reassembly by mathematical statistics and examples. Moreover, it can reduce the cost of rework and improve the quality of remanufactured products. Results show that the proposed method achieved good application results with high accuracy and computation efficiency.
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