Selecting cutting tools for milling is a critical and complex task that directly affects product quality, cost, and operational efficiency. The growing diversity of tools and vendor-specific catalogues makes this process especially challenging, particularly for less experienced operators. In this paper, we present KG4CUT, an application ontology aligned with W3C Semantic Web standards and FAIR principles, designed to standardize and integrate cutting tool information across providers. To demonstrate its practical utility, we populated a knowledge graph using an automated pipeline that extracts structured data from real-world PDF catalogues. This graph serves as both a proof of concept and a functional basis for intelligent tool recommendation and cutting parameter retrieval, based on material properties, operation types, and geometric constraints. Evaluation with domain experts showed improved retrieval efficiency and reduced selection errors. KG4CUT thus supports the digitalization of machining knowledge and enables faster, more accurate process planning in industrial settings.
KG4CUT: an ontology to facilitate cutting tool selection and interoperability
Piano L.
;Carta S. M.;Manca M. M.;Pompianu L.;
2026-01-01
Abstract
Selecting cutting tools for milling is a critical and complex task that directly affects product quality, cost, and operational efficiency. The growing diversity of tools and vendor-specific catalogues makes this process especially challenging, particularly for less experienced operators. In this paper, we present KG4CUT, an application ontology aligned with W3C Semantic Web standards and FAIR principles, designed to standardize and integrate cutting tool information across providers. To demonstrate its practical utility, we populated a knowledge graph using an automated pipeline that extracts structured data from real-world PDF catalogues. This graph serves as both a proof of concept and a functional basis for intelligent tool recommendation and cutting parameter retrieval, based on material properties, operation types, and geometric constraints. Evaluation with domain experts showed improved retrieval efficiency and reduced selection errors. KG4CUT thus supports the digitalization of machining knowledge and enables faster, more accurate process planning in industrial settings.
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