基于Kolmogorov-Arnold网络的容忍度感知可制造性评估：整合面向制造的设计原则
Kolmogorov-Arnold Networks-Based Tolerance-Aware Manufacturability Assessment Integrating Design-for-Manufacturing Principles
AI总结：
本文提出基于Kolmogorov-Arnold网络的可制造性评估方法，直接从参数化设计特征学习功能关系，通过高可解释性可视化识别影响可制造性的关键参数。
摘要：
可制造性评估是连接设计与生产之间持续存在的差距的关键步骤。尽管人工智能（AI）已被广泛应用于此任务，但大多数现有框架依赖于几何驱动的方法，这些方法需要大量的预处理，导致信息丢失，并且可解释性有限。本研究提出了一种方法，该方法直接从参数化设计特征评估可制造性，从而能够显式地纳入尺寸公差，而无需计算机辅助设计（CAD）处理。该方法采用Kolmogorov-Arnold网络（KANs）来学习设计参数、公差和可制造性结果之间的功能关系。生成了一个包含30万条标记设计的合成数据集，以在三个代表性场景中评估性能：钻孔、槽铣削和钻孔-铣削结合情况，同时考虑了加工约束和面向制造（DFM）规则。与十四种机器学习（ML）和深度学习（DL）模型的基准测试显示，KAN在所有场景中均取得了最高性能，钻孔的AUC值为0.9919，铣削为0.9841，结合情况为0.9406。所提出的框架通过基于样条的功能可视化和潜在空间投影提供了高可解释性，能够识别对可制造性影响最大的设计和公差参数。一个工业案例研究进一步展示了该框架如何使非可制造部件通过迭代的参数级设计修改转变为可制造部件。
英文摘要：
Manufacturability assessment is a critical step in bridging the persistent gap between design and production. While artificial intelligence (AI) has been widely applied to this task, most existing frameworks rely on geometry-driven methods that require extensive preprocessing, suffer from information loss, and offer limited interpretability. This study proposes a methodology that evaluates manufacturability directly from parametric design features, enabling explicit incorporation of dimensional tolerances without requiring computer-aided design (CAD) processing. The approach employs Kolmogorov-Arnold Networks (KANs) to learn functional relationships between design parameters, tolerances, and manufacturability outcomes. A synthetic dataset of 300,000 labeled designs is generated to evaluate performance across three representative scenarios: hole drilling, pocket milling, and combined drilling-milling, while accounting for machining constraints and design-for-manufacturing (DFM) rules. Benchmarking against fourteen machine learning (ML) and deep learning (DL) models shows that KAN achieves the highest performance in all scenarios, with AUC values of 0.9919 for drilling, 0.9841 for milling, and 0.9406 for the combined case. The proposed framework provides high interpretability through spline-based functional visualizations and latent-space projections, enabling identification of the design and tolerance parameters that most strongly influence manufacturability. An industrial case study further demonstrates how the framework enables iterative, parameter-level design modifications that transform a non-manufacturable component into a manufacturable one.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
