AI 中文总结
该研究针对工业知识整合难题，提出PCA - GAT方法，将加工工艺计划推荐转化为知识图谱增强的协同过滤问题，引入领域约束和自适应门，在航空航天数据集上效果良好，建立标准化协议并发现知识表示是瓶颈。
AI 中文摘要
整合包括事实关系和决策约束在内的异构工业知识，仍是工业信息系统中的核心挑战。加工工艺规划就是例证，工程师须结合材料属性、特征特性和质量要求来选择操作。现有方法主要依赖相似性检索或分类，缺乏统一排名目标和标准化评估。我们提出PCA - GAT，将加工工艺计划推荐表述为知识图谱增强的协同过滤问题。贝叶斯个性化排序提供学习目标，Recall@K和NDCG@K定义评估。知识图谱在协同信号稀疏时提供语义结构。引入四个领域约束作为图传播中的注意力偏差，特定类型权重学习其重要性，自适应门根据局部上下文调整其影响。在具有115个零件和507个计划的真实航空航天数据集上，PCA - GAT实现Recall@1 = 0.9087且具有强大的冷启动鲁棒性。消融研究表明知识图谱丰富化至关重要，约束增加价值，无门控约束注入会损害性能。研究结果支持在制造业之外的泛化。本研究为工程工艺规划建立了标准化推荐协议，并对七类方法进行基准测试，表明知识表示是主要瓶颈。
英文摘要
Integrating heterogeneous industrial knowledge, including factual relations and decision constraints, remains a core challenge in industrial information systems. Machining process planning exemplifies this problem because engineers must select operations by combining material properties, feature characteristics, and quality requirements. Existing methods rely mainly on similarity retrieval or classification, without a unified ranking objective or standardized evaluation. We propose PCA-GAT, which formulates machining process plan recommendation as a knowledge graph enhanced collaborative filtering problem. Bayesian Personalized Ranking provides the learning objective, while Recall@K and NDCG@K define evaluation. The knowledge graph supplies semantic structure when collaborative signals are sparse. Four domain constraints, material compatibility, precision requirements, feature applicability, and operation sequencing, are introduced as attention biases during graph propagation. Type-specific weights learn their importance, and an adaptive gate adjusts their influence using local context. On a real aerospace dataset with 115 parts and 507 plans, PCA-GAT achieves Recall@1 = 0.9087 and strong cold-start robustness, with about half the degradation of the strongest baseline under severe sparsity. Ablation studies show that knowledge graph enrichment is essential, constraints add value, and ungated constraint injection can hurt performance. The learned weights identify material-operation compatibility as the dominant factor, consistent with domain expertise. Results on three public benchmarks show no degradation when constraints are absent, supporting generalization beyond manufacturing. This study establishes a standardized recommendation protocol for engineering process planning and benchmarks seven methods across three categories, showing that knowledge representation is the main bottleneck.
