Industrial Ontology for Cloud Manufacturing

Abstract

Cloud manufacturing is a new paradigm designed to address the challenges of the complex manufacturing environment of the 21st century and enable larger-scale collaborative manufacturing. Enterprises involved in a cloud manufacturing network exchange share their heterogeneous business models, manufacturing resources, and knowledge to promote collaboration and provide high-quality configurable solutions. As the foundation of this paradigm, manufacturing resources need to be described in a standardized and meaningful way. In the 1980s, the International Organization for Standardization (ISO) launched the international Standard for the Exchange of Product Model Data (STEP) to represent and facilitate product data exchange. The product information models in STEP are specified in EXPRESS, a modelling language that combines ideas from the entity-attribute-relationship family of modelling languages with object modelling concepts. Although STEP provides the communication mechanism for data exchange between heterogeneous systems, the lack of semantics and the complexity of EXPRESS itself impose challenges on the extensibility and applicability of data exchange. Manufacturing resource modelling has experienced three stages of development: (1) traditional database modelling; (2) object-oriented modelling; and (3) semantics-based modelling. Semantics-based modelling has been developed over recent years to manage knowledge in engineering, assimilate multiple data resources, and facilitate the consideration of complex relations among concepts and properties in decision making. Industrial ontologies for cloud manufacturing resources modelling are the methods of describing physical manufacturing resource structure and composition by defining manufacturing entities, relations, and constraints based on STEP. The right choice, rational use and effective management of machining equipment (both machine tool and cutting tool) have a great impact on production efficiency and quality. When selecting machining equipment, several factors need to be taken into account, i.e. working table size, tool accuracy, tool life, the hand of the tool, and the material, geometry, and number of edges of a tool. In traditional process planning, the selection of machining equipment is mainly based on experience of domain expert, which can be error-prone and time-consuming. The large number of tools in the cloud environment calls for an intelligent cutting tool selection system. Unlike existing models, the prototype described in this thesis automatically generates description logic queries for searching in the ontology according to users’ requirements. Thus, the users can easily use the system even if they do not know description logic or how to program ontologies. This thesis presents an ontology-based approach to enable semantic interoperability related to exchanging manufacturing information and validate the function that matches the requirements of customers with the proper or appropriate resources offered by the ontology. This project which has three main aspects focuses on the physical resources of modelling and other types of manufacturing resources. First, the web ontology language (OWL) and the semantic web rule language (SWRL) are employed to construct the resource description model. A representation or representational methodology for EXPRESS-driven ontology development is then proposed. Second, a web application is provided to deploy the ontology in the cloud to select a machine tool and cutting tools. Finally, to demonstrate its feasibility, a case study using the developed ontology is presented.