数据与知识驱动的零件特征工艺决策方法

在零件的工艺设计阶段, 加工工艺方案的生成强依赖于设计人员选择和应用的工艺知识. 而由于实际的生产环境与设计人员选择工艺知识存在着诸多偏差, 加工方案与实际的工艺过程不匹配成为当前零件制造领域关注的难题. 为解决上述问题, 本文提出了一种数据与知识双驱动的零件特征工艺决策方法. 本方法使用基于注意力机制的MLP深度学习算法, 从结构化工艺数据中挖掘工艺知识, 关联零件特征与特征工艺标签. 将其经过数据加工后, 用于训练神经网络模型. 经过验证, 该方法能够以零件特征的工艺数据为输入, 输出其对应的特征工艺标签的概率分布, 为零件工艺方案的选择提供决策支持.

Decision Method for Processes of Parts Machining Features Driven by Data and Knowledge

In the process planning stage of parts, the generated process schemes strongly depend on the process knowledge selected and applied by designers. However, due to the many deviations between the actual manufacturing logics and the process knowledge selected by designers, the mismatch between the generated process scheme and the actual process has become a problem of concern in the current parts manufacturing field. This study proposes a decision method for processes of machining features driven by data and knowledge to solve the above problems. In this method, an MLP deep learning algorithm based on an attention mechanism is utilized to mine process knowledge from structured process data and correlate machining features with feature process labels. After data processing, the method is applied to train a neural network model. After verification, the method can take the feature process data of parts as input and output the distributions of corresponding feature process labels, providing decision support for the generation of the process scheme of parts.