基于知识图谱的精密传动部件工艺路线推荐模型  

A knowledge graph-based model for process routes recommending of precision transmission components

在线阅读下载全文

作  者:刘利军[1] 高亚楼 曹永鹏 刘凯星 LIU Lijun;GAO Yalou;CAO Yongpeng;LIU Kaixing(College of Mechanical and Electrical Engineering,Shaanxi University of Science&Technology,Xi’an 710016,China)

机构地区:[1]陕西科技大学机电工程学院,西安710016

出  处:《现代制造工程》2025年第2期26-36,共11页Modern Manufacturing Engineering

基  金:陕西省重点研发计划项目(2020GY-219)。

摘  要:针对精密传动部件加工过程中普遍存在的工艺路线设计低效、知识重用困难的问题,提出了一种基于知识图谱的精密传动部件工艺路线推荐模型。首先,利用本体方法构建精密传动部件工艺知识模式层;其次,利用Electra+BiLSTM+CRF模型和BiLSTM+Self-Attention模型分别实现实体识别和关系抽取,并基于达梅劳编辑距离进行知识融合,完成数据层构建;然后,基于构建的工艺知识图谱,结合部件工艺单元以及工艺路线结构的相似度实现工艺路线推荐;最后,开发工艺路线推荐系统,并以某型号滚珠丝杠为例展示工艺路线推荐功能。经实验验证表明:推荐准确率达到89.5%,证明了该模型的可行性,能够提高知识重用性和工艺路线设计效率,为决策提供更加科学合理的参考。To address the inefficiencies in process route design and the difficulty in reusing knowledge in the machining of precision transmission components,a knowledge graph-based process route recommendation model was proposed for precision transmission components.Firstly,an ontology-based method was used to construct the process knowledge schema layer for precision transmission components.Secondly,entity recognition and relation extraction were achieved using the Electra+BiLSTM+CRF model and the BiLSTM+Self-Attention model,respectively,and Damerau-Levenshtein distance was used for knowledge fusion,completing the data layer construction.Then,based on the constructed process knowledge graph,process route recommendations were achieved by combining the similarity of component process units and process route structures.Finally,a process route recommendation system was developed and demonstrated with an example of a specific type of ball screw.Experimental results show that the recommendation accuracy reaches 89.5%,proving the feasibility of the model,which can enhance knowledge reuse and improve process route design efficiency,providing a more scientific and reasonable reference for decision-making.

关 键 词:精密传动部件 知识图谱 工艺设计 工艺路线推荐 

分 类 号:TP391.1[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象