The parameter traceability chain is essential for maintaining the controllability, consistency, and accuracy of performance parameters throughout the lifecycle of aviation products. Aviation enterprises have amassed substantial parameter traceability data, and enhancing the reuse of this data is critical for achieving efficient digital manufacturing. However, the current approach to establishing metrology parameter traceability relationships relies heavily on the experience of metrology personnel and manual processes. This method often results in redundant work when dealing with similar products and parameters, leading to inefficiencies and increased risk of errors. To this end, this paper proposes an entity matching-based method for reusing parameter traceability chains in aviation products. Initially, a novel entity-matching model utilizing a Siamese neural network is designed. This model transforms text entities into semantic vector representations using word2vec and determines if entity pairs match via a multilayer fully connected neural network. Subsequently, based on the matched parameter entities and the corresponding product information, the historical parameter traceability chains are reused for newly developed products. The experimental results demonstrate that the proposed entity matching model outperforms the five baseline models. Moreover, the case study confirms the method’s effectiveness in reusing aviation product parameter traceability, offering engineers a fast and accurate reference for parameter traceability. This approach is crucial for improving aviation products’ manufacturing efficiency and quality.
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