SMErobotics: Smart Robots for Flexible Manufacturing

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

Kessler

Profanter

et al. 2020

Self Cite

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.

Section: Semantic Manufacturing Execution Systemmentioning

confidence: 99%

“…However, the manual reconfiguration and operation of production systems are major cost drivers. Automating these activities is therefore essential when competing in a global market [1].…”

Section: Introductionmentioning

confidence: 99%

Toward a Knowledge-Based Data Backbone for Seamless Digital Engineering in Smart Factories

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

Kessler

Profanter

et al. 2020

Self Cite

“…The framework responsible for solving geometric interrelational constraints and real-time control of a robot that performs an associated assembly is described in [5]. 2 https://youtu.be/o5EiAut3N2c 3 https://www.w3.org/TR/sparql11-overview/ 4 https://angular.io/ Fig. 3: Overview of the instruction framework.…”

Section: A Robot Instruction Frameworkmentioning

confidence: 99%

“…Moreover, creating new and adapting existing programs during changeover requires experienced operators that need to be extensively trained. These conditions severely hinder the integration of robots in such manufacturing processes [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Semantic Mates: Intuitive Geometric Constraints for Efficient Assembly Specifications

Wildgrube

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Rickert

et al. 2019

Self Cite

In this paper, we enhance our knowledge-based and constraint-based approach of robot programming with the concept of Semantic Mates. They describe intended mechanical connections between parts of an assembly. This allows deriving appropriate assembly poses from the type of connection and the geometric properties of the involved parts. The paper presents an ontology-based representation of Semantic Mates that is used to augment object models with additional information regarding their potential use in an assembly. Such semantically annotated object models can be used in our instruction framework to program a robot to perform assembly tasks through simple drag-and-drop operations in a graphical user interface. We conducted a user study with 21 participants in order to evaluate the efficiency and usability of the Semantic Mates concept based on a use-case from the domain of mechanical assembly. Across different experience levels in robotics, the participants achieved a significantly faster workflow and improved perceived usability compared to the manual specification of constraint-based assembly operations.

“…This typically leads to a highly challenging situation, in which many different product variants have to be produced in only small batch sizes. In order to still maintain an economically viable production environment, the effort of programming and adjusting production processes needs to be reduced [1].…”

Section: Introductionmentioning

confidence: 99%

OPC UA NodeSet Ontologies as a Pillar of Representing Semantic Digital Twins of Manufacturing Resources

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

Profanter

Rickert

et al. 2019

Self Cite

The effectiveness of cognitive manufacturing systems in agile production environments heavily depends 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 OWLbased 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.