Machining Scheme Selection of Features Based on Process Knowledge Graph and Improved Cosine Similarity Matching

The machining scheme selection (MSS) for features is to choose the optimal machining scheme for a feature before machining. To solve the issue of excessive human subjectivity in the traditional MSS, this paper proposes a simple and easy-to-use method based on process knowledge graph retrieval and through machining scheme similarity matching. First, process knowledge is extracted using natural language processing techniques, focusing on forming ternary groups such as part–feature, feature–attribute, and scheme–resource to construct a multi-level process knowledge graph. This graph is used to retrieve the available machining schemes for the features. Based on the part property, the feature basic information and manufacturing information are used to establish a feature information model and information coding dimensionality reduction. Then, considering the influence coefficient of the process parameter and the usage coefficient of the machining scheme, an improved cosine similarity formula is designed for MSS. According to the maximum similarity, the optimal machining scheme is matched to the feature. Finally, the effectiveness of this method is verified by selecting the machining schemes for six types of hole features on a typical shell part. The results demonstrate that the recommended schemes by the proposed method closely align with the existing mature schemes.