Automated discovery of typical networked process routes with multi-dimensional process information fusion

WANG Meng-jiao; XU Bin-zi; HUANG Deng-chao; WANG Chun; WANG Yan

doi:10.7641/CTA.2024.40392

Abstract：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.