科研之友
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摘要
To avoid the waste of remanufactured blank value and economic loss caused by process defects, it is necessary to predict its process reliability and identify the process defect elements after the remanufacturing process of a used product is determined. However, remanufactured blanks are highly variable and have high residual value, which is difficult, poorly generalized and costly to establish a mechanism model affecting process reliability elements. To this end, a data-driven process reliability prediction method for processing elements was proposed. A Bayesian neural network prediction model reflecting the mapping relationship between each processing element and reliability was constructed by using the deviation value between the quality index obtained by processing and the quality requirement as the quantitative index for process reliability evaluation. The proposed prediction method was validated using a remanufactured CNC machine tool as an example. The results showed that the proposed method could identify defective parts and their machining elements and guide the improvement of process quality of remanufactured products. ? 2023 CIMS.
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