Home // International Journal On Advances in Intelligent Systems, volume 10, numbers 3 and 4, 2017 // View article

Resources and their Description for Additive Manufacturing

Authors:
Felix W. Baumann
Julian Eichhoff
Dieter Roller

Keywords: 3D Printing; Additive Manufacturing; Resource Description; Capability Description; Service Selection; Service Discovery

Abstract:
For an enhanced automated usage of 3D-printers in case of multiple available 3D-printers, such as in Cloud Man- ufacturing or Cloud Printing services, the requirement arises to select and provision suitable resources for user provided model files. As Additive Manufacturing (AM) consists of a number of different technologies, ranging from fabrication using thermoplastic extrusion to electron beam based curing of metal powder, the necessity is evident to enable users to describe limitations, capabilities, interfaces and requirements for a these resources in a machine readable and processable format. This resource description enables the discovery and provisioning of appropriate resources within a service composition, where 3D-printing resources are regarded as manufacturing services themselves. In order to compose a service from these hard- ware resources, the comprehensive description of such resources must be provided. With this work, we provide an abstract and universal capability description framework of such 3D- printing resources. The framework consists of an ontology for the resources of the AM Domain, a flexible Extensible Markup Language (XML) schema and the implementation in a cloud- based 3D-printing system. With this resource description both hard- and software resources are universally defined. Applied to systems with multiple 3D-printers, a scheduling component is capable of resource discovery. This selection is based on the matching of described capabilities, status information and derived requirements from specific 3D-printing job definitions. This work provides a framework for the description of resources in the AM domain with an ontology, based on a collection of identified resource descriptors extracted from literature.

Pages: 238 to 260

Copyright: Copyright (c) to authors, 2017. Used with permission.

Publication date: December 31, 2017

Published in: journal

ISSN: 1942-2679