引用本文:王梦娇,徐彬梓,黄登朝,王春,王艳.多维工艺信息融合的典型网状工艺路线自动化发现[J].控制理论与应用,2025,42(12):2577~2586.[点击复制]
WANG Meng-jiao,XU Bin-zi,HUANG Deng-chao,WANG Chun,WANG Yan.Automated discovery of typical networked process routes with multi-dimensional process information fusion[J].Control Theory & Applications,2025,42(12):2577~2586.[点击复制]
多维工艺信息融合的典型网状工艺路线自动化发现
Automated discovery of typical networked process routes with multi-dimensional process information fusion
摘要点击 132  全文点击 17  投稿时间：2024-07-24  修订日期：2025-10-17
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DOI编号  10.7641/CTA.2024.40392
  2025,42(12):2577-2586
中文关键词  典型工艺路线  相似度度量  计算机辅助工艺规划  近邻传播算法  火鹰优化算法
英文关键词  typical process route  similarity measurement  CAPP  AP algorithm  FHO
基金项目  国家自然科学基金项目(61973138),安徽省高校自然科学研究项目(2023AH030081),安徽省中青年教师培养行动项目(JNFX2023017),芜湖市科 技计划项目(2023jc05), 安徽工程大学国家自然科学基金预研项目(Xjky2022042),检测技术与节能装置安徽省重点实验室开放基金项目(JCK20 22A09), 安徽省车载显示集成系统工程研究中心开放基金项目(VDIS2023B02)资助.
作者单位E-mail
王梦娇 安徽工程大学电气工程学院 wmj20000525@163.com 
徐彬梓* 安徽工程大学电气工程学院 xubinzi@ahpu.edu.cn 
黄登朝 安徽工程大学电气工程学院  
王春 淮北师范大学计算机科学与技术学院  
王艳 江南大学物联网工程学院  
中文摘要
      大数据背景下,典型工艺路线的有效发现可以为计算机辅助工艺规划(CAPP)的检索过程提供更加精准且 充足的工艺信息,从而提高后续工艺规划的质量.但是现有与线性工艺路线相关的方法由于没有考虑网状工艺路 线的结构复杂性,既无法直接应用,也难以合理量化其中的各类工艺信息.此外,大部分现有研究也忽略了典型工艺 路线发现中的聚类有效性问题,对这一问题缺乏合理的算法设计.鉴于此,本文提出了一种基于多维工艺信息融合 的典型网状工艺路线自动化发现方法.该方法针对网状工艺路线的相似度度量问题,在信息需求分析的基础上,为 网状工艺路线中蕴含的4种工艺信息设计了不同的量化方法,并通过主成分分析(PCA)将其合成综合相似度.此外, 基于上述相似度度量的构建,并考虑聚类有效性,在传统近邻传播(OAP)算法中引入了火鹰优化算法(FHO),以优化 AP算法的参考度与阻尼系数,从而在聚类结果与软约束之间取得平衡,以发现更符合实际需求的典型网状工艺路 线. 仿真实验表明,本文所提网状工艺路线的相似度度量能有效区分各种相似度情况,具有更高的灵敏性.同时,引 入的FHO能提高AP算法的聚类效果,其中FHO-IAP相较于其他算法表现出更好的性能.
英文摘要
      Under the big data era, the effective discovery of typical process routes can provide more accurate and sufficient process information for the retrieval process of computer aided process planning (CAPP), thus improving the quality of process planning. However, the existing approaches cannot be applied directly and are difficult to quantify the process information because they ignore the structural complexity of the networked process routes. In addition, most of the existing researches have overlooked the clustering effectiveness in the discovery of typical process routes, revealing a gap in effective algorithm design for this challenge. To address these shortcomings, this paper proposes an automated discovery method for typical networked process routes based on the multi-dimensional process information fusion. For the similarity measure, different quantification methods for the four types of process information are designed based on the information requirement analysis. Then, the proposed method integrates these findings into a comprehensive similarity using principal component analysis (PCA). Besides, considering the clustering effectiveness, the fire hawk optimizer (FHO) is introduced into the original affinity propagation (OAP) clustering algorithm to optimize its reference degree and damping coefficient, so that a balance between the clustering results and the soft constraints can be achieved. This way, typical networked process routes that are more in line with the practical requirements can be found. Simulation experiments validate that the proposed similarity measure for networked process routes can effectively distinguish various similarity cases with higher sensitivity. Meanwhile, the introduced FHO can enhance the clustering performance of AP, in which FHO-IAP shows the best clustering effect.