A novel cutting tool selection approach based on a metal cutting process knowledge graph

With the wide and deep application of computer technologies in the field of metal cutting, many manufacturing enterprises have accumulated a large volume of data including workpieces, cutting tools, machine tools, processes, and materials. These data are very valuable resources for enterprises. In this paper, a novel tool selection approach is proposed to deeply mine the relationships hidden in these data. First, an ontology model of the metal cutting process is established with OWL (Web Ontology Language), and a metal cutting process knowledge graph (MCPKG) is constructed based on the ontology model. Then, a data model describing the relationship structural feature-material-cutting tool is designed to build a subgraph based on the MCPKG. Moreover, the personalized PageRank (PPR) algorithm is employed with the data model to recommend cutting tools for process planning, and the result of an illustrative example is discussed in detail to verify the algorithm. Finally, a cutting tool selection system with B/S (browser/server) structure based on .NET MVC (model view control) is developed. To recommend cutting tools, the presented approach utilizes the connectivity of the MCPKG to score and rank the usage effect of each tool, which is universally applicable to manufacturing enterprises and provides valuable insights into intelligent cutting tool selection.

Automated summary

This paper presents a novel tool selection approach that deeply mines the relationships in data of metal cutting processes, using a personalized PageRank algorithm to recommend cutting tools. A cutting tool selection system based on .NET MVC is developed as a result.