An ontology self-learning approach for CNC machine capability information integration and representation in cloud manufacturing

Abstract

Manufacturing activities in cloud-based environments strongly rely on the online integration and description of the capability of machining resources. In the authors’ previous work, STEP-NC schema was applied to construct an ontology model to support the integration and reasoning of machine tool information and capability. For the maintenance and update of the model, in this paper, a self-learning method is proposed to explore correlations from STEP-NC process planning documents to obtain machining knowledge to improve the comprehensiveness of the model. In this method, a Map/Reduce-based Apriori algorithm is developed incombination with the built ontological model. First, a dataset is extracted from the document according to the importance analysis results of the model. Then, a mining procedure that combinesApriori algorithm and Map/Reduce framework is developed. Finally, two representation modes are adopted to embed the mined results into the model. According to the outcomes of a preliminary experiment with standard STEP-NC documents, this method effectively enables the ontology learning mechanism from the aspects of time consumption and mined associations, which improves the suitability of the enriched ontology model to handle information integration and industrial applications.