School of mechanical engineering, Baoji University of Arts and Sciences, Baoji Shaanxi, P.R. China.
Shaanxi Key Laboratory of Advanced Manufacturing and Evaluation of Robot Key Components, Baoji Shaanxi, P.R. China.
PLoS One. 2022 Sep 12;17(9):e0274532. doi: 10.1371/journal.pone.0274532. eCollection 2022.
Manufacturing enterprises accumulate numerous manufacturing instances as they run and develop. Being able to excavate and apply the instance resources reasonably is one of the most effective approaches to improve manufacturing and support innovation. A novel framework for the discovery and reuse of typical process routes driven by symbolic entropy and intelligent optimisation algorithm so as to scientifically determine reuse objects and raise the reuse flexibility is proposed in this paper. A similarity measurement method of machining process routes based on symbolic entropy is developed in this framework. Subsequently, a typical process route discovery method based on the ant colony clustering model and similarity measurement is devised, and two reuse approaches based on the typical process route are analysed. Finally, three case studies are rendered. These case studies cover the aspects of similarity analysis, mining, and reuse of manufacturing instances, which systematically explains the whole procedure of discovery and reuse based on typical process route. The case studies show that (i) the similarity measurement method based on symbolic entropy can accurately evaluate the similarity among ten machining process routes, (ii) ant colony clustering model can realize adaptive clustering for these ten process routes, and (iii) indirect reuse approach for the typical process route can support the generation of new machining plan effectively. This reveal that the proposed framework comprehensively considers various aspects of retrieval and reuse of manufacturing instances, which can effectively support process instance reuse. Can better support process instance reuse.
制造企业在运行和发展过程中积累了大量的制造实例。合理挖掘和应用实例资源是提高制造水平和支持创新的最有效方法之一。本文提出了一种新的基于符号熵和智能优化算法驱动的典型工艺路线发现和重用框架，以科学地确定重用对象并提高重用灵活性。在该框架中，开发了一种基于符号熵的加工工艺路线相似性度量方法。随后，设计了一种基于蚁群聚类模型和相似性度量的典型工艺路线发现方法，并分析了基于典型工艺路线的两种重用方法。最后，进行了三个案例研究。这些案例研究涵盖了制造实例的相似性分析、挖掘和重用等方面，系统地解释了基于典型工艺路线的发现和重用的整个过程。案例研究表明：(i)基于符号熵的相似性度量方法可以准确评估十种加工工艺路线之间的相似性；(ii)蚁群聚类模型可以对这十种工艺路线进行自适应聚类；(iii)典型工艺路线的间接重用方法可以有效地支持新加工计划的生成。这表明所提出的框架全面考虑了制造实例检索和重用的各个方面，能够有效地支持工艺实例重用。可以更好地支持工艺实例重用。
