@inproceedings{Perzylo2020a, author = {Perzylo, Alexander and Kessler, Ingmar and Profanter, Stefan and Rickert, Markus}, title = {Toward a Knowledge-Based Data Backbone for Seamless Digital Engineering in Smart Factories}, booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA)}, pages = {164--171}, year = {2020}, month = sep, address = {Vienna, Austria}, abstract = {Digital transformation efforts in manufacturing companies bear the potential to reduce product costs and increase the flexibility of production systems. The semantic integration of data and information along the value chain enables the automated interpretation of interrelations between its different aspects such as product design, production process and manufacturing resources. These interrelations can be used to automatically generate semantic process descriptions and execute corresponding robot motions. An initial one-time effort to model the required knowledge of a particular application domain can make the manufacturing of high-variant products in small batches or even lot size one production more efficient. This paper introduces a knowledge-based digital engineering concept to automate engineering and production activities without human involvement. The concept was integrated and evaluated in a physical robot workcell where automotive fuse boxes are autonomously fitted with different fuse configurations.}, doi = {10.1109/ETFA46521.2020.9211943}, keywords = {robotics, data backbone}, url = {https://youtu.be/PtPd3YvTTzw}, } @inproceedings{Perzylo2019c, author = {Perzylo, Alexander and Profanter, Stefan and Rickert, Markus and Knoll, Alois}, title = {{OPC} {UA} NodeSet Ontologies as a Pillar of Representing Semantic Digital Twins of Manufacturing Resources}, booktitle = {Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation ({ETFA})}, pages = {1085--1092}, year = {2019}, month = sep, address = {Zaragoza, Spain}, abstract = {The effectiveness of cognitive manufacturing systems in agile production environments heavily depend on the automatic assessment of various levels of interoperability between manufacturing resources. For taking informed decisions, a semantically rich representation of all resources in a workcell or production line is required. OPC UA provides means for communication and information exchange in such distributed settings. This paper proposes a semantic representation of a resource's properties, in which we use OWL ontologies to encode the information models that can be found in OPC UA NodeSet specifications. We further combine these models with an OWL-based description of the resource's geometry and -- if applicable -- its kinematic model. This leads to a comprehensive semantic representation of hardware and software features of a manufacturing resource, which we call semantic digital twin. Among other things, it reduces costs through virtual prototyping and enables the automatic deployment of manufacturing tasks in production lines. As a result, small-batch assemblies become financially viable. In order to minimize the effort of creating OWL-based UA NodeSet descriptions, we provide a software tool for the automatic transformation of XML-based NodeSet specifications that adhere to the OPC Foundation's NodeSet2 XML schema.}, doi = {10.1109/ETFA.2019.8868954}, keywords = {robotics, data backbone, basys 4.0}, } @inproceedings{Profanter2019b, author = {Profanter, Stefan and Breitkreuz, Ari and Rickert, Markus and Knoll, Alois}, title = {A Hardware-Agnostic OPC UA Skill Model for Robot Manipulators and Tools}, booktitle = {Proceedings of the {IEEE} International Conference on Emerging Technologies And Factory Automation ({ETFA})}, year = {2019}, month = sep, address = {Zaragoza, Spain}, abstract = {The current trend to lot-size-one production requires reduced integration effort and easy reuse of available devices inside the production line. These devices have to offer a uniform interface to fulfill these requirements. This paper presents a hardware-agnostic skill model using the semantic modeling capabilities of OPC~UA. The model provides a standardized interface to hardware or software functionality while offering an intuitive way of grouping multiple skills to a higher hierarchical abstraction. Our skill model is based on OPC~UA Programs and modeled as an open source NodeSet. We hereby focus on the reusability of the skills for many different domains. The model is evaluated by controlling three different industrial robots and their tools through the same skill interface. The evaluation shows that our generic OPC~UA skill model can be used as a standardized control interface for device and software components in industrial manufacturing. With our solution new components can easily be exchanged without changing the interface. This is not only true for industrial robots, but for any device which provides a controllable functionality.}, keywords = {robotics, data backbone}, }